Training in progress, epoch 1

README.md

@@ -17,12 +17,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: spans
-      split: test
+      split: train[80%:100%]
       args: spans
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9360779676806765
+      value: 0.9412361055794923
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +32,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1786
-- B: {'precision': 0.8126064735945485, 'recall': 0.9008498583569405, 'f1-score': 0.8544558889386475, 'support': 1059.0}
-- I: {'precision': 0.9468377121729875, 'recall': 0.9617069701280228, 'f1-score': 0.9542144187884605, 'support': 17575.0}
-- O: {'precision': 0.9307744259342638, 'recall': 0.8915363881401617, 'f1-score': 0.9107329698771959, 'support': 9275.0}
-- Accuracy: 0.9361
-- Macro avg: {'precision': 0.8967395372339334, 'recall': 0.918031072208375, 'f1-score': 0.9064677592014346, 'support': 27909.0}
-- Weighted avg: {'precision': 0.9364060284323041, 'recall': 0.9360779676806765, 'f1-score': 0.9359789133327677, 'support': 27909.0}
+- Loss: 0.2837
+- B: {'precision': 0.8616822429906542, 'recall': 0.8839884947267498, 'f1-score': 0.872692853762423, 'support': 1043.0}
+- I: {'precision': 0.9506446299767138, 'recall': 0.9647262247838617, 'f1-score': 0.957633664216037, 'support': 17350.0}
+- O: {'precision': 0.9322299261910088, 'recall': 0.9035334923043572, 'f1-score': 0.9176574196389256, 'support': 9226.0}
+- Accuracy: 0.9412
+- Macro avg: {'precision': 0.9148522663861257, 'recall': 0.9174160706049896, 'f1-score': 0.9159946458724618, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9411337198513156, 'recall': 0.9412361055794923, 'f1-score': 0.9410720907422853, 'support': 27619.0}
 
 ## Model description
 
@@ -63,16 +63,27 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 4
+- num_epochs: 15
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2836          | {'precision': 0.7968253968253968, 'recall': 0.4740321057601511, 'f1-score': 0.5944345766725874, 'support': 1059.0} | {'precision': 0.9161490683229814, 'recall': 0.9399715504978663, 'f1-score': 0.9279074339315303, 'support': 17575.0} | {'precision': 0.8717421866551314, 'recall': 0.8691105121293801, 'f1-score': 0.8704243602202786, 'support': 9275.0} | 0.8987   | {'precision': 0.8615722172678364, 'recall': 0.7610380561291326, 'f1-score': 0.7975887902747987, 'support': 27909.0} | {'precision': 0.8968636193428943, 'recall': 0.8987423411802644, 'f1-score': 0.8961505359950553, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.2022          | {'precision': 0.7857142857142857, 'recall': 0.8621340887629839, 'f1-score': 0.8221521837010356, 'support': 1059.0} | {'precision': 0.9355464420305919, 'recall': 0.9605120910384068, 'f1-score': 0.9478649035627053, 'support': 17575.0} | {'precision': 0.9268068482132598, 'recall': 0.8696495956873316, 'f1-score': 0.8973189453776839, 'support': 9275.0} | 0.9266   | {'precision': 0.8826891919860458, 'recall': 0.8974319251629076, 'f1-score': 0.889112010880475, 'support': 27909.0}  | {'precision': 0.9269566686171867, 'recall': 0.926582822745351, 'f1-score': 0.9262968240005718, 'support': 27909.0}  |
-| No log        | 3.0   | 123  | 0.1852          | {'precision': 0.7871125611745514, 'recall': 0.9112370160528801, 'f1-score': 0.8446389496717724, 'support': 1059.0} | {'precision': 0.9400952275495515, 'recall': 0.966145092460882, 'f1-score': 0.9529421668490613, 'support': 17575.0}  | {'precision': 0.940726133859181, 'recall': 0.8743935309973045, 'f1-score': 0.9063477872150201, 'support': 9275.0}  | 0.9336   | {'precision': 0.8893113075277613, 'recall': 0.9172585465036889, 'f1-score': 0.9013096345786179, 'support': 27909.0} | {'precision': 0.9345000078114988, 'recall': 0.9335698161883264, 'f1-score': 0.9333479148838716, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.1786          | {'precision': 0.8126064735945485, 'recall': 0.9008498583569405, 'f1-score': 0.8544558889386475, 'support': 1059.0} | {'precision': 0.9468377121729875, 'recall': 0.9617069701280228, 'f1-score': 0.9542144187884605, 'support': 17575.0} | {'precision': 0.9307744259342638, 'recall': 0.8915363881401617, 'f1-score': 0.9107329698771959, 'support': 9275.0} | 0.9361   | {'precision': 0.8967395372339334, 'recall': 0.918031072208375, 'f1-score': 0.9064677592014346, 'support': 27909.0}  | {'precision': 0.9364060284323041, 'recall': 0.9360779676806765, 'f1-score': 0.9359789133327677, 'support': 27909.0} |
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2927          | {'precision': 0.8069852941176471, 'recall': 0.42090124640460214, 'f1-score': 0.5532451165721487, 'support': 1043.0} | {'precision': 0.8852390417407678, 'recall': 0.9754466858789625, 'f1-score': 0.9281561917297357, 'support': 17350.0} | {'precision': 0.9349000879728541, 'recall': 0.8063082592672881, 'f1-score': 0.8658557876971424, 'support': 9226.0} | 0.8980   | {'precision': 0.8757081412770896, 'recall': 0.7342187305169509, 'f1-score': 0.7824190319996757, 'support': 27619.0} | {'precision': 0.8988729225389979, 'recall': 0.8980049965603389, 'f1-score': 0.8931869394398603, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.1958          | {'precision': 0.7986171132238548, 'recall': 0.8859060402684564, 'f1-score': 0.84, 'support': 1043.0}                | {'precision': 0.9361619307123394, 'recall': 0.9703170028818444, 'f1-score': 0.9529335182407381, 'support': 17350.0} | {'precision': 0.9455124425050124, 'recall': 0.8689572946022112, 'f1-score': 0.905619881389438, 'support': 9226.0}  | 0.9333   | {'precision': 0.8934304954804023, 'recall': 0.908393445917504, 'f1-score': 0.8995177998767253, 'support': 27619.0}  | {'precision': 0.9340912032116592, 'recall': 0.933270574604439, 'f1-score': 0.932863809956036, 'support': 27619.0}   |
+| No log        | 3.0   | 123  | 0.1754          | {'precision': 0.8552631578947368, 'recall': 0.87248322147651, 'f1-score': 0.8637873754152824, 'support': 1043.0}    | {'precision': 0.966759166322253, 'recall': 0.9437463976945245, 'f1-score': 0.9551141832181294, 'support': 17350.0}  | {'precision': 0.8988355167394468, 'recall': 0.9370257966616085, 'f1-score': 0.9175334323922734, 'support': 9226.0} | 0.9388   | {'precision': 0.9069526136521455, 'recall': 0.9177518052775477, 'f1-score': 0.9121449970085617, 'support': 27619.0} | {'precision': 0.9398590639347346, 'recall': 0.9388102393279989, 'f1-score': 0.9391116535227126, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1844          | {'precision': 0.861003861003861, 'recall': 0.8552253116011506, 'f1-score': 0.8581048581048581, 'support': 1043.0}   | {'precision': 0.9428187016481668, 'recall': 0.9693371757925072, 'f1-score': 0.9558940547914061, 'support': 17350.0} | {'precision': 0.9376786735277302, 'recall': 0.8887925428137872, 'f1-score': 0.912581381114017, 'support': 9226.0}  | 0.9381   | {'precision': 0.9138337453932527, 'recall': 0.904451676735815, 'f1-score': 0.908860098003427, 'support': 27619.0}   | {'precision': 0.9380120548386821, 'recall': 0.9381223071074261, 'f1-score': 0.937732757876541, 'support': 27619.0}  |
+| No log        | 5.0   | 205  | 0.2030          | {'precision': 0.8463611859838275, 'recall': 0.9031639501438159, 'f1-score': 0.8738404452690166, 'support': 1043.0}  | {'precision': 0.9367116741679169, 'recall': 0.9716426512968299, 'f1-score': 0.9538574702237813, 'support': 17350.0} | {'precision': 0.9452344576330943, 'recall': 0.8717754172989378, 'f1-score': 0.9070200169157033, 'support': 9226.0} | 0.9357   | {'precision': 0.9094357725949461, 'recall': 0.9155273395798611, 'f1-score': 0.9115726441361671, 'support': 27619.0} | {'precision': 0.9361466877844027, 'recall': 0.9356964408559325, 'f1-score': 0.9351898826482664, 'support': 27619.0} |
+| No log        | 6.0   | 246  | 0.1880          | {'precision': 0.8593012275731823, 'recall': 0.87248322147651, 'f1-score': 0.8658420551855375, 'support': 1043.0}    | {'precision': 0.9416148372275452, 'recall': 0.9685878962536023, 'f1-score': 0.954910929908799, 'support': 17350.0}  | {'precision': 0.9369907035464249, 'recall': 0.8848905267721656, 'f1-score': 0.9101956630804393, 'support': 9226.0} | 0.9370   | {'precision': 0.9126355894490508, 'recall': 0.9086538815007593, 'f1-score': 0.9103162160582586, 'support': 27619.0} | {'precision': 0.9369616871420418, 'recall': 0.9369998913791231, 'f1-score': 0.9366104162010322, 'support': 27619.0} |
+| No log        | 7.0   | 287  | 0.1950          | {'precision': 0.8525345622119815, 'recall': 0.8868648130393096, 'f1-score': 0.8693609022556391, 'support': 1043.0}  | {'precision': 0.9470030477480528, 'recall': 0.9670893371757925, 'f1-score': 0.9569408007300102, 'support': 17350.0} | {'precision': 0.9362522686025408, 'recall': 0.8946455668762194, 'f1-score': 0.9149761667220929, 'support': 9226.0} | 0.9399   | {'precision': 0.9119299595208584, 'recall': 0.9161999056971072, 'f1-score': 0.9137592899025807, 'support': 27619.0} | {'precision': 0.9398443048967325, 'recall': 0.9398602411383468, 'f1-score': 0.939615352760648, 'support': 27619.0}  |
+| No log        | 8.0   | 328  | 0.2260          | {'precision': 0.8517495395948435, 'recall': 0.8868648130393096, 'f1-score': 0.868952559887271, 'support': 1043.0}   | {'precision': 0.933457985041795, 'recall': 0.978328530259366, 'f1-score': 0.955366691056453, 'support': 17350.0}    | {'precision': 0.9556833153671098, 'recall': 0.8648384998916107, 'f1-score': 0.9079943100995733, 'support': 9226.0} | 0.9370   | {'precision': 0.9136302800012494, 'recall': 0.9100106143967621, 'f1-score': 0.9107711870144325, 'support': 27619.0} | {'precision': 0.9377966283301177, 'recall': 0.9369636844201455, 'f1-score': 0.9362788339465783, 'support': 27619.0} |
+| No log        | 9.0   | 369  | 0.2217          | {'precision': 0.8499079189686924, 'recall': 0.8849472674976031, 'f1-score': 0.8670737435415689, 'support': 1043.0}  | {'precision': 0.9531535648994516, 'recall': 0.9616138328530259, 'f1-score': 0.9573650083204224, 'support': 17350.0} | {'precision': 0.927455975191051, 'recall': 0.9076522870149577, 'f1-score': 0.9174472747192549, 'support': 9226.0}  | 0.9407   | {'precision': 0.910172486353065, 'recall': 0.9180711291218623, 'f1-score': 0.9139620088604153, 'support': 27619.0}  | {'precision': 0.9406704492415535, 'recall': 0.9406930011948297, 'f1-score': 0.9406209263707241, 'support': 27619.0} |
+| No log        | 10.0  | 410  | 0.2663          | {'precision': 0.8574091332712023, 'recall': 0.8820709491850431, 'f1-score': 0.8695652173913044, 'support': 1043.0}  | {'precision': 0.9361054205193511, 'recall': 0.9744668587896254, 'f1-score': 0.9549010194572307, 'support': 17350.0} | {'precision': 0.9483794932233353, 'recall': 0.8722089746368957, 'f1-score': 0.9087008074078257, 'support': 9226.0} | 0.9368   | {'precision': 0.9139646823379629, 'recall': 0.9095822608705214, 'f1-score': 0.9110556814187869, 'support': 27619.0} | {'precision': 0.937233642655096, 'recall': 0.9368188565842355, 'f1-score': 0.9362454418504176, 'support': 27619.0}  |
+| No log        | 11.0  | 451  | 0.2752          | {'precision': 0.8570110701107011, 'recall': 0.8906999041227229, 'f1-score': 0.8735307945463094, 'support': 1043.0}  | {'precision': 0.9348246340789838, 'recall': 0.9755043227665706, 'f1-score': 0.954731349598082, 'support': 17350.0}  | {'precision': 0.9505338078291815, 'recall': 0.8685237372642532, 'f1-score': 0.9076801087449027, 'support': 9226.0} | 0.9366   | {'precision': 0.9141231706729555, 'recall': 0.9115759880511822, 'f1-score': 0.911980750963098, 'support': 27619.0}  | {'precision': 0.9371336709666482, 'recall': 0.9365654078713929, 'f1-score': 0.9359476526130199, 'support': 27619.0} |
+| No log        | 12.0  | 492  | 0.2662          | {'precision': 0.8555657773689053, 'recall': 0.8916586768935763, 'f1-score': 0.8732394366197183, 'support': 1043.0}  | {'precision': 0.9461304151624549, 'recall': 0.9667435158501441, 'f1-score': 0.9563259022749302, 'support': 17350.0} | {'precision': 0.9358246251703771, 'recall': 0.8930197268588771, 'f1-score': 0.9139212423738213, 'support': 9226.0} | 0.9393   | {'precision': 0.9125069392339125, 'recall': 0.9171406398675325, 'f1-score': 0.9144955270894899, 'support': 27619.0} | {'precision': 0.9392677432450942, 'recall': 0.9392809297947066, 'f1-score': 0.9390231550383894, 'support': 27619.0} |
+| 0.1232        | 13.0  | 533  | 0.2681          | {'precision': 0.8646895273401297, 'recall': 0.8945349952061361, 'f1-score': 0.8793590951932139, 'support': 1043.0}  | {'precision': 0.9548364966841985, 'recall': 0.9626512968299712, 'f1-score': 0.9587279719878308, 'support': 17350.0} | {'precision': 0.9293766578249337, 'recall': 0.9114459137220897, 'f1-score': 0.9203239575352961, 'support': 9226.0} | 0.9430   | {'precision': 0.9163008939497539, 'recall': 0.9228774019193989, 'f1-score': 0.9194703415721136, 'support': 27619.0} | {'precision': 0.9429274571700438, 'recall': 0.9429740396104132, 'f1-score': 0.9429020124731535, 'support': 27619.0} |
+| 0.1232        | 14.0  | 574  | 0.2835          | {'precision': 0.8643592142188962, 'recall': 0.8859060402684564, 'f1-score': 0.875, 'support': 1043.0}               | {'precision': 0.9461283248045886, 'recall': 0.9697406340057637, 'f1-score': 0.9577889733299177, 'support': 17350.0} | {'precision': 0.9405726018022128, 'recall': 0.8937784522003035, 'f1-score': 0.9165786694825766, 'support': 9226.0} | 0.9412   | {'precision': 0.9170200469418992, 'recall': 0.9164750421581745, 'f1-score': 0.9164558809374981, 'support': 27619.0} | {'precision': 0.9411845439739722, 'recall': 0.9411998986205149, 'f1-score': 0.9408964297013043, 'support': 27619.0} |
+| 0.1232        | 15.0  | 615  | 0.2837          | {'precision': 0.8616822429906542, 'recall': 0.8839884947267498, 'f1-score': 0.872692853762423, 'support': 1043.0}   | {'precision': 0.9506446299767138, 'recall': 0.9647262247838617, 'f1-score': 0.957633664216037, 'support': 17350.0}  | {'precision': 0.9322299261910088, 'recall': 0.9035334923043572, 'f1-score': 0.9176574196389256, 'support': 9226.0} | 0.9412   | {'precision': 0.9148522663861257, 'recall': 0.9174160706049896, 'f1-score': 0.9159946458724618, 'support': 27619.0} | {'precision': 0.9411337198513156, 'recall': 0.9412361055794923, 'f1-score': 0.9410720907422853, 'support': 27619.0} |
 
 
 ### Framework versions

meta_data/README_s42_e10.md

@@ -0,0 +1,89 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.939172308917774
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2166
+- B: {'precision': 0.8636788048552755, 'recall': 0.8868648130393096, 'f1-score': 0.8751182592242194, 'support': 1043.0}
+- I: {'precision': 0.948943661971831, 'recall': 0.9630547550432277, 'f1-score': 0.9559471365638768, 'support': 17350.0}
+- O: {'precision': 0.9289709172259508, 'recall': 0.9001734229351832, 'f1-score': 0.9143454805680943, 'support': 9226.0}
+- Accuracy: 0.9392
+- Macro avg: {'precision': 0.9138644613510191, 'recall': 0.9166976636725735, 'f1-score': 0.9151369587853968, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9390519284189125, 'recall': 0.939172308917774, 'f1-score': 0.9389978843359775, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2992          | {'precision': 0.7993138936535163, 'recall': 0.4467881112176414, 'f1-score': 0.5731857318573186, 'support': 1043.0} | {'precision': 0.8829387840233601, 'recall': 0.9759654178674352, 'f1-score': 0.9271243977222952, 'support': 17350.0} | {'precision': 0.9361160600661746, 'recall': 0.7973119445046607, 'f1-score': 0.8611566377897449, 'support': 9226.0} | 0.8963   | {'precision': 0.8727895792476836, 'recall': 0.7400218245299124, 'f1-score': 0.7871555891231196, 'support': 27619.0} | {'precision': 0.8975444101544748, 'recall': 0.8963032694883957, 'f1-score': 0.8917220811418657, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2008          | {'precision': 0.7899231426131511, 'recall': 0.8868648130393096, 'f1-score': 0.8355916892502258, 'support': 1043.0} | {'precision': 0.9329899761865205, 'recall': 0.9710086455331413, 'f1-score': 0.951619736210354, 'support': 17350.0}  | {'precision': 0.9478012155881301, 'recall': 0.862020377194884, 'f1-score': 0.9028779020264517, 'support': 9226.0}  | 0.9314   | {'precision': 0.8902381114626006, 'recall': 0.9066312785891116, 'f1-score': 0.8966964424956773, 'support': 27619.0} | {'precision': 0.9325348470110335, 'recall': 0.9314240196965857, 'f1-score': 0.9309560838275704, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1754          | {'precision': 0.8574144486692015, 'recall': 0.8648130393096836, 'f1-score': 0.8610978520286395, 'support': 1043.0} | {'precision': 0.9637472869126532, 'recall': 0.9469164265129683, 'f1-score': 0.9552577259644737, 'support': 17350.0} | {'precision': 0.9027310924369748, 'recall': 0.9314979406026447, 'f1-score': 0.916888936306412, 'support': 9226.0}  | 0.9387   | {'precision': 0.9079642760062764, 'recall': 0.9144091354750987, 'f1-score': 0.9110815047665084, 'support': 27619.0} | {'precision': 0.9393495693805003, 'recall': 0.9386654114920888, 'f1-score': 0.9388849680116025, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1737          | {'precision': 0.8583732057416268, 'recall': 0.8600191754554171, 'f1-score': 0.8591954022988505, 'support': 1043.0} | {'precision': 0.9443852068017284, 'recall': 0.9699135446685879, 'f1-score': 0.9569791577810003, 'support': 17350.0} | {'precision': 0.9394631639063392, 'recall': 0.8915022761760243, 'f1-score': 0.9148545687114177, 'support': 9226.0} | 0.9396   | {'precision': 0.9140738588165648, 'recall': 0.9071449987666765, 'f1-score': 0.9103430429304228, 'support': 27619.0} | {'precision': 0.9394928759838658, 'recall': 0.9395705854665267, 'f1-score': 0.939214940549245, 'support': 27619.0}  |
+| No log        | 5.0   | 205  | 0.2081          | {'precision': 0.8590225563909775, 'recall': 0.8763183125599233, 'f1-score': 0.8675842429995253, 'support': 1043.0} | {'precision': 0.9336273428886439, 'recall': 0.9761383285302594, 'f1-score': 0.9544096928712315, 'support': 17350.0} | {'precision': 0.9511586452762923, 'recall': 0.8675482332538478, 'f1-score': 0.9074315514993481, 'support': 9226.0} | 0.9361   | {'precision': 0.9146028481853046, 'recall': 0.9066682914480101, 'f1-score': 0.9098084957900351, 'support': 27619.0} | {'precision': 0.9366662292897221, 'recall': 0.9360947174046852, 'f1-score': 0.9354379967014502, 'support': 27619.0} |
+| No log        | 6.0   | 246  | 0.1913          | {'precision': 0.8325991189427313, 'recall': 0.9060402684563759, 'f1-score': 0.8677685950413224, 'support': 1043.0} | {'precision': 0.935986255057363, 'recall': 0.973371757925072, 'f1-score': 0.9543129997457125, 'support': 17350.0}   | {'precision': 0.9491766378391185, 'recall': 0.8684153479297637, 'f1-score': 0.9070017546838739, 'support': 9226.0} | 0.9358   | {'precision': 0.9059206706130709, 'recall': 0.9159424581037373, 'f1-score': 0.9096944498236362, 'support': 27619.0} | {'precision': 0.9364881446470265, 'recall': 0.9357688547738875, 'f1-score': 0.9352406451692542, 'support': 27619.0} |
+| No log        | 7.0   | 287  | 0.1970          | {'precision': 0.8484848484848485, 'recall': 0.8859060402684564, 'f1-score': 0.8667917448405252, 'support': 1043.0} | {'precision': 0.9405801971326165, 'recall': 0.9680115273775216, 'f1-score': 0.9540987331704823, 'support': 17350.0} | {'precision': 0.9371685496887249, 'recall': 0.8810969000650336, 'f1-score': 0.9082681564245809, 'support': 9226.0} | 0.9359   | {'precision': 0.9087445317687299, 'recall': 0.9116714892370039, 'f1-score': 0.9097195448118628, 'support': 27619.0} | {'precision': 0.9359626762970696, 'recall': 0.9358774756508201, 'f1-score': 0.9354921909391984, 'support': 27619.0} |
+| No log        | 8.0   | 328  | 0.2042          | {'precision': 0.8507734303912647, 'recall': 0.8964525407478428, 'f1-score': 0.8730158730158729, 'support': 1043.0} | {'precision': 0.9413907099232364, 'recall': 0.96835734870317, 'f1-score': 0.9546836378100406, 'support': 17350.0}   | {'precision': 0.9384296091317883, 'recall': 0.8821807934099285, 'f1-score': 0.9094362813565005, 'support': 9226.0} | 0.9369   | {'precision': 0.9101979164820965, 'recall': 0.9156635609536471, 'f1-score': 0.9123785973941381, 'support': 27619.0} | {'precision': 0.9369795097185314, 'recall': 0.936855063543213, 'f1-score': 0.9364848764747034, 'support': 27619.0}  |
+| No log        | 9.0   | 369  | 0.2107          | {'precision': 0.8516483516483516, 'recall': 0.8916586768935763, 'f1-score': 0.8711943793911008, 'support': 1043.0} | {'precision': 0.9517556380245504, 'recall': 0.960806916426513, 'f1-score': 0.9562598594579091, 'support': 17350.0}  | {'precision': 0.9260985352862849, 'recall': 0.9046173856492521, 'f1-score': 0.9152319333260226, 'support': 9226.0} | 0.9394   | {'precision': 0.9098341749863957, 'recall': 0.9190276596564471, 'f1-score': 0.9142287240583441, 'support': 27619.0} | {'precision': 0.9394045634181702, 'recall': 0.9394257576306166, 'f1-score': 0.9393422685892149, 'support': 27619.0} |
+| No log        | 10.0  | 410  | 0.2166          | {'precision': 0.8636788048552755, 'recall': 0.8868648130393096, 'f1-score': 0.8751182592242194, 'support': 1043.0} | {'precision': 0.948943661971831, 'recall': 0.9630547550432277, 'f1-score': 0.9559471365638768, 'support': 17350.0}  | {'precision': 0.9289709172259508, 'recall': 0.9001734229351832, 'f1-score': 0.9143454805680943, 'support': 9226.0} | 0.9392   | {'precision': 0.9138644613510191, 'recall': 0.9166976636725735, 'f1-score': 0.9151369587853968, 'support': 27619.0} | {'precision': 0.9390519284189125, 'recall': 0.939172308917774, 'f1-score': 0.9389978843359775, 'support': 27619.0}  |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e11.md

@@ -0,0 +1,90 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9396792063434593
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2269
+- B: {'precision': 0.8579285059578369, 'recall': 0.8974113135186961, 'f1-score': 0.8772258669165884, 'support': 1043.0}
+- I: {'precision': 0.9460510739049551, 'recall': 0.9672622478386167, 'f1-score': 0.9565390863233492, 'support': 17350.0}
+- O: {'precision': 0.9369666628740471, 'recall': 0.8925861695209192, 'f1-score': 0.9142381348875936, 'support': 9226.0}
+- Accuracy: 0.9397
+- Macro avg: {'precision': 0.9136487475789464, 'recall': 0.9190865769594107, 'f1-score': 0.9160010293758437, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9396886199949656, 'recall': 0.9396792063434593, 'f1-score': 0.9394134747592979, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 11
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2961          | {'precision': 0.8145695364238411, 'recall': 0.3537871524448706, 'f1-score': 0.49331550802139046, 'support': 1043.0} | {'precision': 0.8821044947335489, 'recall': 0.9750432276657061, 'f1-score': 0.9262483574244416, 'support': 17350.0} | {'precision': 0.9316474712068102, 'recall': 0.8066334272707566, 'f1-score': 0.8646450563494831, 'support': 9226.0} | 0.8953   | {'precision': 0.8761071674547334, 'recall': 0.7118212691271112, 'f1-score': 0.7614029739317717, 'support': 27619.0} | {'precision': 0.8961037177114005, 'recall': 0.8953256815960028, 'f1-score': 0.8893208431174447, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2112          | {'precision': 0.7865546218487395, 'recall': 0.8974113135186961, 'f1-score': 0.83833407971339, 'support': 1043.0}    | {'precision': 0.924818593485733, 'recall': 0.9770028818443804, 'f1-score': 0.9501947924549455, 'support': 17350.0}  | {'precision': 0.9595061728395061, 'recall': 0.8424019076522871, 'f1-score': 0.8971487937204201, 'support': 9226.0} | 0.9290   | {'precision': 0.8902931293913262, 'recall': 0.905605367671788, 'f1-score': 0.8952258886295853, 'support': 27619.0}  | {'precision': 0.931184438907382, 'recall': 0.9290343604040696, 'f1-score': 0.9282507283065632, 'support': 27619.0}  |
+| No log        | 3.0   | 123  | 0.1780          | {'precision': 0.8625954198473282, 'recall': 0.8667305848513902, 'f1-score': 0.8646580583452893, 'support': 1043.0}  | {'precision': 0.9670896584440227, 'recall': 0.94, 'f1-score': 0.9533524288303034, 'support': 17350.0}               | {'precision': 0.8919336561244463, 'recall': 0.9384348580099718, 'f1-score': 0.9145935667881477, 'support': 9226.0} | 0.9367   | {'precision': 0.9072062448052658, 'recall': 0.9150551476204539, 'f1-score': 0.9108680179879135, 'support': 27619.0} | {'precision': 0.9380380357112386, 'recall': 0.9367102357073029, 'f1-score': 0.9370557674878652, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1855          | {'precision': 0.845719661335842, 'recall': 0.8619367209971237, 'f1-score': 0.8537511870845204, 'support': 1043.0}   | {'precision': 0.9384769427601936, 'recall': 0.9723919308357348, 'f1-score': 0.9551334673196139, 'support': 17350.0} | {'precision': 0.9438162956055485, 'recall': 0.8776284413613701, 'f1-score': 0.909519797809604, 'support': 9226.0}  | 0.9366   | {'precision': 0.9093376332338613, 'recall': 0.9039856977314096, 'f1-score': 0.9061348174045795, 'support': 27619.0} | {'precision': 0.9367576562120075, 'recall': 0.9365654078713929, 'f1-score': 0.9360678446256514, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.1980          | {'precision': 0.8513761467889909, 'recall': 0.8897411313518696, 'f1-score': 0.8701359587435537, 'support': 1043.0}  | {'precision': 0.9398964883966832, 'recall': 0.9734293948126801, 'f1-score': 0.9563690931226817, 'support': 17350.0} | {'precision': 0.9483644859813084, 'recall': 0.8799046173856493, 'f1-score': 0.9128528055774204, 'support': 9226.0} | 0.9390   | {'precision': 0.9132123737223274, 'recall': 0.9143583811833996, 'f1-score': 0.9131192858145519, 'support': 27619.0} | {'precision': 0.9393823144374135, 'recall': 0.939027481081864, 'f1-score': 0.9385761814296439, 'support': 27619.0}  |
+| No log        | 6.0   | 246  | 0.1904          | {'precision': 0.828695652173913, 'recall': 0.9137104506232023, 'f1-score': 0.8691290469676243, 'support': 1043.0}   | {'precision': 0.9355999778503793, 'recall': 0.9738328530259366, 'f1-score': 0.9543336439888163, 'support': 17350.0} | {'precision': 0.9504161712247324, 'recall': 0.8663559505744635, 'f1-score': 0.906441369925153, 'support': 9226.0}  | 0.9357   | {'precision': 0.9049039337496749, 'recall': 0.917966418074534, 'f1-score': 0.9099680202938645, 'support': 27619.0}  | {'precision': 0.9365121393475814, 'recall': 0.935660233896955, 'f1-score': 0.9351177956523646, 'support': 27619.0}  |
+| No log        | 7.0   | 287  | 0.1881          | {'precision': 0.8546296296296296, 'recall': 0.8849472674976031, 'f1-score': 0.8695242581252944, 'support': 1043.0}  | {'precision': 0.9503849443969205, 'recall': 0.9605187319884726, 'f1-score': 0.9554249677511824, 'support': 17350.0} | {'precision': 0.9249222567747668, 'recall': 0.9026663776284414, 'f1-score': 0.9136588041689524, 'support': 9226.0} | 0.9383   | {'precision': 0.909978943600439, 'recall': 0.916044125704839, 'f1-score': 0.9128693433484765, 'support': 27619.0}   | {'precision': 0.9382631605052417, 'recall': 0.9383395488612911, 'f1-score': 0.9382292305648449, 'support': 27619.0} |
+| No log        | 8.0   | 328  | 0.2086          | {'precision': 0.8519195612431444, 'recall': 0.8935762224352828, 'f1-score': 0.8722508189050071, 'support': 1043.0}  | {'precision': 0.9404728634508971, 'recall': 0.9697982708933718, 'f1-score': 0.9549104735960954, 'support': 17350.0} | {'precision': 0.940699559879546, 'recall': 0.8803381747236072, 'f1-score': 0.9095184770436731, 'support': 9226.0}  | 0.9370   | {'precision': 0.9110306615245292, 'recall': 0.9145708893507539, 'f1-score': 0.9122265898482586, 'support': 27619.0} | {'precision': 0.937204476001968, 'recall': 0.9370360983381005, 'f1-score': 0.9366259383111303, 'support': 27619.0}  |
+| No log        | 9.0   | 369  | 0.2106          | {'precision': 0.8523245214220602, 'recall': 0.8964525407478428, 'f1-score': 0.8738317757009345, 'support': 1043.0}  | {'precision': 0.9503868912152936, 'recall': 0.9627665706051873, 'f1-score': 0.9565366775468134, 'support': 17350.0} | {'precision': 0.929689246590655, 'recall': 0.9014740949490571, 'f1-score': 0.9153642967202289, 'support': 9226.0}  | 0.9398   | {'precision': 0.9108002197426696, 'recall': 0.9202310687673624, 'f1-score': 0.9152442499893256, 'support': 27619.0} | {'precision': 0.9397697247356507, 'recall': 0.9397878272203918, 'f1-score': 0.9396599767925747, 'support': 27619.0} |
+| No log        | 10.0  | 410  | 0.2272          | {'precision': 0.8681214421252372, 'recall': 0.8772770853307766, 'f1-score': 0.8726752503576539, 'support': 1043.0}  | {'precision': 0.9461503725445924, 'recall': 0.9661095100864553, 'f1-score': 0.9560257799577938, 'support': 17350.0} | {'precision': 0.932873771047576, 'recall': 0.8947539562107089, 'f1-score': 0.9134163208852005, 'support': 9226.0}  | 0.9389   | {'precision': 0.9157151952391351, 'recall': 0.9127135172093136, 'f1-score': 0.9140391170668827, 'support': 27619.0} | {'precision': 0.938768711375149, 'recall': 0.9389188602049314, 'f1-score': 0.938644648425997, 'support': 27619.0}   |
+| No log        | 11.0  | 451  | 0.2269          | {'precision': 0.8579285059578369, 'recall': 0.8974113135186961, 'f1-score': 0.8772258669165884, 'support': 1043.0}  | {'precision': 0.9460510739049551, 'recall': 0.9672622478386167, 'f1-score': 0.9565390863233492, 'support': 17350.0} | {'precision': 0.9369666628740471, 'recall': 0.8925861695209192, 'f1-score': 0.9142381348875936, 'support': 9226.0} | 0.9397   | {'precision': 0.9136487475789464, 'recall': 0.9190865769594107, 'f1-score': 0.9160010293758437, 'support': 27619.0} | {'precision': 0.9396886199949656, 'recall': 0.9396792063434593, 'f1-score': 0.9394134747592979, 'support': 27619.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e12.md

@@ -0,0 +1,91 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9414895542923349
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2394
+- B: {'precision': 0.8633828996282528, 'recall': 0.8906999041227229, 'f1-score': 0.8768286927796131, 'support': 1043.0}
+- I: {'precision': 0.9488209014307527, 'recall': 0.9670317002881844, 'f1-score': 0.9578397510918277, 'support': 17350.0}
+- O: {'precision': 0.9363431151241535, 'recall': 0.8991979189247779, 'f1-score': 0.917394669910428, 'support': 9226.0}
+- Accuracy: 0.9415
+- Macro avg: {'precision': 0.9161823053943863, 'recall': 0.9189765077785618, 'f1-score': 0.9173543712606228, 'support': 27619.0}
+- Weighted avg: {'precision': 0.941426285682728, 'recall': 0.9414895542923349, 'f1-score': 0.9412699675080908, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 12
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2858          | {'precision': 0.8085106382978723, 'recall': 0.36433365292425696, 'f1-score': 0.5023132848645077, 'support': 1043.0} | {'precision': 0.8888126286890872, 'recall': 0.9703170028818444, 'f1-score': 0.9277782370284644, 'support': 17350.0} | {'precision': 0.9216617933723197, 'recall': 0.8199653154129634, 'f1-score': 0.8678444418951474, 'support': 9226.0} | 0.8972   | {'precision': 0.8729950201197597, 'recall': 0.7182053237396883, 'f1-score': 0.7659786545960398, 'support': 27619.0} | {'precision': 0.8967532281818084, 'recall': 0.8972084434628336, 'f1-score': 0.8916904301199235, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2181          | {'precision': 0.7889273356401384, 'recall': 0.8744007670182167, 'f1-score': 0.8294679399727148, 'support': 1043.0}  | {'precision': 0.921101216333623, 'recall': 0.9776945244956773, 'f1-score': 0.9485544930939999, 'support': 17350.0}  | {'precision': 0.9581210388964831, 'recall': 0.8356817689139389, 'f1-score': 0.8927227464829502, 'support': 9226.0} | 0.9264   | {'precision': 0.8893831969567482, 'recall': 0.8959256868092776, 'f1-score': 0.8902483931832217, 'support': 27619.0} | {'precision': 0.9284761222100719, 'recall': 0.9263550454397336, 'f1-score': 0.9254069870605068, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1805          | {'precision': 0.8276785714285714, 'recall': 0.8887823585810163, 'f1-score': 0.8571428571428571, 'support': 1043.0}  | {'precision': 0.9608084358523726, 'recall': 0.9453025936599424, 'f1-score': 0.952992446252179, 'support': 17350.0}  | {'precision': 0.9023226216990137, 'recall': 0.9221764578365489, 'f1-score': 0.9121415170195658, 'support': 9226.0} | 0.9354   | {'precision': 0.8969365429933193, 'recall': 0.9187538033591692, 'f1-score': 0.9074256068048673, 'support': 27619.0} | {'precision': 0.9362440211388452, 'recall': 0.9354429921430899, 'f1-score': 0.9357267308192845, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1988          | {'precision': 0.8492366412213741, 'recall': 0.8533077660594439, 'f1-score': 0.8512673362027738, 'support': 1043.0}  | {'precision': 0.9303964757709251, 'recall': 0.9738328530259366, 'f1-score': 0.9516192621796676, 'support': 17350.0} | {'precision': 0.9456663892521697, 'recall': 0.8621287665293735, 'f1-score': 0.9019674547825594, 'support': 9226.0} | 0.9320   | {'precision': 0.9084331687481564, 'recall': 0.8964231285382512, 'f1-score': 0.9016180177216668, 'support': 27619.0} | {'precision': 0.9324324116970188, 'recall': 0.9319671240812484, 'f1-score': 0.9312436282378297, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.2100          | {'precision': 0.8506375227686703, 'recall': 0.8954937679769894, 'f1-score': 0.8724894908921066, 'support': 1043.0}  | {'precision': 0.9365704772475028, 'recall': 0.9727377521613833, 'f1-score': 0.9543115634718687, 'support': 17350.0} | {'precision': 0.9460063521938595, 'recall': 0.8716670279644483, 'f1-score': 0.9073165228182998, 'support': 9226.0} | 0.9361   | {'precision': 0.9110714507366775, 'recall': 0.9132995160342737, 'f1-score': 0.9113725257274249, 'support': 27619.0} | {'precision': 0.9364773279927747, 'recall': 0.9360585104457076, 'f1-score': 0.9355231690053595, 'support': 27619.0} |
+| No log        | 6.0   | 246  | 0.2054          | {'precision': 0.8465073529411765, 'recall': 0.8830297219558965, 'f1-score': 0.8643829188174565, 'support': 1043.0}  | {'precision': 0.9236811957885549, 'recall': 0.9759077809798271, 'f1-score': 0.94907653933466, 'support': 17350.0}   | {'precision': 0.9496341463414634, 'recall': 0.8440277476696293, 'f1-score': 0.8937220245610008, 'support': 9226.0} | 0.9283   | {'precision': 0.9066075650237316, 'recall': 0.9009884168684509, 'f1-score': 0.9023938275710391, 'support': 27619.0} | {'precision': 0.929436277569623, 'recall': 0.9283464281834969, 'f1-score': 0.9273872602332724, 'support': 27619.0}  |
+| No log        | 7.0   | 287  | 0.1949          | {'precision': 0.851063829787234, 'recall': 0.8820709491850431, 'f1-score': 0.8662900188323918, 'support': 1043.0}   | {'precision': 0.9430497051390059, 'recall': 0.9677809798270893, 'f1-score': 0.9552552979661499, 'support': 17350.0} | {'precision': 0.9366769724035269, 'recall': 0.8866247561239974, 'f1-score': 0.910963862130408, 'support': 9226.0}  | 0.9374   | {'precision': 0.9102635024432556, 'recall': 0.9121588950453766, 'f1-score': 0.9108363929763166, 'support': 27619.0} | {'precision': 0.9374471815063824, 'recall': 0.9374343748868532, 'f1-score': 0.937100275222493, 'support': 27619.0}  |
+| No log        | 8.0   | 328  | 0.2038          | {'precision': 0.8602050326188257, 'recall': 0.8849472674976031, 'f1-score': 0.8724007561436674, 'support': 1043.0}  | {'precision': 0.9485302462830553, 'recall': 0.9634005763688761, 'f1-score': 0.9559075832094247, 'support': 17350.0} | {'precision': 0.9286194531600179, 'recall': 0.8982224149143724, 'f1-score': 0.913168044077135, 'support': 9226.0}  | 0.9387   | {'precision': 0.9124515773539663, 'recall': 0.9155234195936172, 'f1-score': 0.913825461143409, 'support': 27619.0}  | {'precision': 0.938543636514239, 'recall': 0.9386654114920888, 'f1-score': 0.9384770966362654, 'support': 27619.0}  |
+| No log        | 9.0   | 369  | 0.2182          | {'precision': 0.8558310376492194, 'recall': 0.8935762224352828, 'f1-score': 0.874296435272045, 'support': 1043.0}   | {'precision': 0.9498548579885024, 'recall': 0.9618443804034582, 'f1-score': 0.9558120221083077, 'support': 17350.0} | {'precision': 0.9273518580515567, 'recall': 0.9007153696076307, 'f1-score': 0.9138395557266179, 'support': 9226.0} | 0.9388   | {'precision': 0.9110125845630929, 'recall': 0.9187119908154573, 'f1-score': 0.9146493377023236, 'support': 27619.0} | {'precision': 0.9387871320740184, 'recall': 0.9388464462869763, 'f1-score': 0.9387129695753523, 'support': 27619.0} |
+| No log        | 10.0  | 410  | 0.2523          | {'precision': 0.861652739090065, 'recall': 0.8897411313518696, 'f1-score': 0.8754716981132075, 'support': 1043.0}   | {'precision': 0.938376753507014, 'recall': 0.9715850144092218, 'f1-score': 0.9546921900662626, 'support': 17350.0}  | {'precision': 0.9432268594077874, 'recall': 0.8769781053544331, 'f1-score': 0.9088968771062682, 'support': 9226.0} | 0.9369   | {'precision': 0.9144187840016221, 'recall': 0.9127680837051749, 'f1-score': 0.9130202550952461, 'support': 27619.0} | {'precision': 0.9370995142877685, 'recall': 0.9368912705021906, 'f1-score': 0.9364028048431936, 'support': 27619.0} |
+| No log        | 11.0  | 451  | 0.2504          | {'precision': 0.8530762167125804, 'recall': 0.8906999041227229, 'f1-score': 0.8714821763602252, 'support': 1043.0}  | {'precision': 0.9388493211662586, 'recall': 0.972507204610951, 'f1-score': 0.9553819149538532, 'support': 17350.0}  | {'precision': 0.9457817247020331, 'recall': 0.8773032733579016, 'f1-score': 0.9102564102564102, 'support': 9226.0} | 0.9376   | {'precision': 0.9125690875269573, 'recall': 0.9135034606971919, 'f1-score': 0.9123735005234962, 'support': 27619.0} | {'precision': 0.9379259353476508, 'recall': 0.9376154096817408, 'f1-score': 0.9371395696954526, 'support': 27619.0} |
+| No log        | 12.0  | 492  | 0.2394          | {'precision': 0.8633828996282528, 'recall': 0.8906999041227229, 'f1-score': 0.8768286927796131, 'support': 1043.0}  | {'precision': 0.9488209014307527, 'recall': 0.9670317002881844, 'f1-score': 0.9578397510918277, 'support': 17350.0} | {'precision': 0.9363431151241535, 'recall': 0.8991979189247779, 'f1-score': 0.917394669910428, 'support': 9226.0}  | 0.9415   | {'precision': 0.9161823053943863, 'recall': 0.9189765077785618, 'f1-score': 0.9173543712606228, 'support': 27619.0} | {'precision': 0.941426285682728, 'recall': 0.9414895542923349, 'f1-score': 0.9412699675080908, 'support': 27619.0}  |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e13.md

@@ -0,0 +1,92 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9388464462869763
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2612
+- B: {'precision': 0.8656716417910447, 'recall': 0.8897411313518696, 'f1-score': 0.8775413711583925, 'support': 1043.0}
+- I: {'precision': 0.9471240942028986, 'recall': 0.9642651296829972, 'f1-score': 0.9556177528988404, 'support': 17350.0}
+- O: {'precision': 0.9312169312169312, 'recall': 0.8965965748970302, 'f1-score': 0.9135788834281297, 'support': 9226.0}
+- Accuracy: 0.9388
+- Macro avg: {'precision': 0.9146708890702916, 'recall': 0.9168676119772989, 'f1-score': 0.9155793358284542, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9387344206602614, 'recall': 0.9388464462869763, 'f1-score': 0.9386263963728234, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 13
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2988          | {'precision': 0.8082474226804124, 'recall': 0.37583892617449666, 'f1-score': 0.513089005235602, 'support': 1043.0} | {'precision': 0.8794226460319942, 'recall': 0.9727377521613833, 'f1-score': 0.9237295093183, 'support': 17350.0}    | {'precision': 0.9257207604179781, 'recall': 0.7969867765011923, 'f1-score': 0.856543770749607, 'support': 9226.0}  | 0.8915   | {'precision': 0.8711302763767949, 'recall': 0.715187818279024, 'f1-score': 0.7644540951011697, 'support': 27619.0}  | {'precision': 0.8922004672916121, 'recall': 0.8914877439443861, 'f1-score': 0.885779052393972, 'support': 27619.0}  |
+| No log        | 2.0   | 82   | 0.1948          | {'precision': 0.7984293193717278, 'recall': 0.8772770853307766, 'f1-score': 0.8359981726815899, 'support': 1043.0} | {'precision': 0.9427846674182638, 'recall': 0.9639769452449568, 'f1-score': 0.9532630379025364, 'support': 17350.0} | {'precision': 0.9346158250314898, 'recall': 0.8846737481031867, 'f1-score': 0.9089592961746199, 'support': 9226.0} | 0.9342   | {'precision': 0.8919432706071605, 'recall': 0.9086425928929733, 'f1-score': 0.8994068355862487, 'support': 27619.0} | {'precision': 0.9346044882708322, 'recall': 0.9342119555378544, 'f1-score': 0.9340352028756634, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1740          | {'precision': 0.848089468779124, 'recall': 0.87248322147651, 'f1-score': 0.8601134215500945, 'support': 1043.0}    | {'precision': 0.9581905812670577, 'recall': 0.9510662824207493, 'f1-score': 0.9546151398571056, 'support': 17350.0} | {'precision': 0.9091689008042896, 'recall': 0.9189247778018643, 'f1-score': 0.9140208075036386, 'support': 9226.0} | 0.9374   | {'precision': 0.9051496502834904, 'recall': 0.9141580938997079, 'f1-score': 0.9095831229702794, 'support': 27619.0} | {'precision': 0.937657271434174, 'recall': 0.9373619609688982, 'f1-score': 0.9374860402341179, 'support': 27619.0}  |
+| No log        | 4.0   | 164  | 0.1780          | {'precision': 0.8725868725868726, 'recall': 0.8667305848513902, 'f1-score': 0.8696488696488696, 'support': 1043.0} | {'precision': 0.9619125269349484, 'recall': 0.9519884726224784, 'f1-score': 0.9569247704295936, 'support': 17350.0} | {'precision': 0.9102209944751382, 'recall': 0.9285714285714286, 'f1-score': 0.9193046464212898, 'support': 9226.0} | 0.9409   | {'precision': 0.9149067979989863, 'recall': 0.9157634953484323, 'f1-score': 0.9152927621665844, 'support': 27619.0} | {'precision': 0.9412719267698718, 'recall': 0.9409464499076723, 'f1-score': 0.9410620661819779, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.1934          | {'precision': 0.8405017921146953, 'recall': 0.8993288590604027, 'f1-score': 0.8689207966651227, 'support': 1043.0} | {'precision': 0.9406415620641562, 'recall': 0.9718155619596541, 'f1-score': 0.9559744861800141, 'support': 17350.0} | {'precision': 0.9460247143856377, 'recall': 0.8795794493821808, 'f1-score': 0.9115929004718041, 'support': 9226.0} | 0.9383   | {'precision': 0.9090560228548297, 'recall': 0.9169079568007459, 'f1-score': 0.9121627277723136, 'support': 27619.0} | {'precision': 0.9386581152797215, 'recall': 0.9382671349433361, 'f1-score': 0.93786153828516, 'support': 27619.0}   |
+| No log        | 6.0   | 246  | 0.2013          | {'precision': 0.8481481481481481, 'recall': 0.87823585810163, 'f1-score': 0.8629298162976919, 'support': 1043.0}   | {'precision': 0.9306275504577037, 'recall': 0.9726801152737752, 'f1-score': 0.9511892684026604, 'support': 17350.0} | {'precision': 0.9445568114217727, 'recall': 0.8605029265120312, 'f1-score': 0.9005728546310476, 'support': 9226.0} | 0.9316   | {'precision': 0.9077775033425416, 'recall': 0.9038062999624789, 'f1-score': 0.9048973131104666, 'support': 27619.0} | {'precision': 0.9321658156029167, 'recall': 0.9316412614504508, 'f1-score': 0.9309480706039573, 'support': 27619.0} |
+| No log        | 7.0   | 287  | 0.2083          | {'precision': 0.8447488584474886, 'recall': 0.8868648130393096, 'f1-score': 0.8652946679139383, 'support': 1043.0} | {'precision': 0.940964601271878, 'recall': 0.9636887608069165, 'f1-score': 0.9521911216150801, 'support': 17350.0}  | {'precision': 0.9294117647058824, 'recall': 0.8819640147409495, 'f1-score': 0.9050664590400979, 'support': 9226.0} | 0.9335   | {'precision': 0.9050417414750829, 'recall': 0.9108391961957253, 'f1-score': 0.9075174161897054, 'support': 27619.0} | {'precision': 0.933471951649382, 'recall': 0.933487816358304, 'f1-score': 0.9331677993323372, 'support': 27619.0}   |
+| No log        | 8.0   | 328  | 0.2452          | {'precision': 0.8446251129177959, 'recall': 0.8964525407478428, 'f1-score': 0.8697674418604651, 'support': 1043.0} | {'precision': 0.9315356136376195, 'recall': 0.9716426512968299, 'f1-score': 0.9511665303128614, 'support': 17350.0} | {'precision': 0.9429590017825312, 'recall': 0.8600693691740733, 'f1-score': 0.8996088657105606, 'support': 9226.0} | 0.9315   | {'precision': 0.9063732427793155, 'recall': 0.9093881870729152, 'f1-score': 0.9068476126279624, 'support': 27619.0} | {'precision': 0.932069468113675, 'recall': 0.9315326405735183, 'f1-score': 0.9308699858008703, 'support': 27619.0}  |
+| No log        | 9.0   | 369  | 0.2303          | {'precision': 0.8538812785388128, 'recall': 0.8964525407478428, 'f1-score': 0.8746492048643593, 'support': 1043.0} | {'precision': 0.9594571080563773, 'recall': 0.9534293948126801, 'f1-score': 0.9564337544447978, 'support': 17350.0} | {'precision': 0.9145750296240439, 'recall': 0.9202254498157382, 'f1-score': 0.9173915392511751, 'support': 9226.0} | 0.9402   | {'precision': 0.909304472073078, 'recall': 0.9233691284587536, 'f1-score': 0.9161581661867775, 'support': 27619.0}  | {'precision': 0.9404775053986587, 'recall': 0.9401861037691445, 'f1-score': 0.9403033817814589, 'support': 27619.0} |
+| No log        | 10.0  | 410  | 0.2620          | {'precision': 0.8548983364140481, 'recall': 0.8868648130393096, 'f1-score': 0.8705882352941177, 'support': 1043.0} | {'precision': 0.9359622327131353, 'recall': 0.9712968299711816, 'f1-score': 0.9533022203365862, 'support': 17350.0} | {'precision': 0.9423347398030942, 'recall': 0.8714502492954693, 'f1-score': 0.9055073769568645, 'support': 9226.0} | 0.9348   | {'precision': 0.9110651029767592, 'recall': 0.9098706307686535, 'f1-score': 0.9097992775291894, 'support': 27619.0} | {'precision': 0.9350296539294, 'recall': 0.9347550599225171, 'f1-score': 0.9342129733898971, 'support': 27619.0}    |
+| No log        | 11.0  | 451  | 0.2666          | {'precision': 0.84967919340055, 'recall': 0.8887823585810163, 'f1-score': 0.8687910028116214, 'support': 1043.0}   | {'precision': 0.9369943477779009, 'recall': 0.9745821325648415, 'f1-score': 0.9554186913775569, 'support': 17350.0} | {'precision': 0.9489507191700071, 'recall': 0.8724257533058747, 'f1-score': 0.9090806415179579, 'support': 9226.0} | 0.9372   | {'precision': 0.911874753449486, 'recall': 0.9119300814839107, 'f1-score': 0.9110967785690454, 'support': 27619.0}  | {'precision': 0.93769096157449, 'recall': 0.9372171331329882, 'f1-score': 0.9366682830652019, 'support': 27619.0}   |
+| No log        | 12.0  | 492  | 0.2533          | {'precision': 0.859925788497217, 'recall': 0.8887823585810163, 'f1-score': 0.8741159830268741, 'support': 1043.0}  | {'precision': 0.947344555914527, 'recall': 0.963342939481268, 'f1-score': 0.9552767696396423, 'support': 17350.0}   | {'precision': 0.9294223420993482, 'recall': 0.8963797962280512, 'f1-score': 0.9126020745972192, 'support': 9226.0} | 0.9382   | {'precision': 0.9122308955036974, 'recall': 0.9161683647634451, 'f1-score': 0.9139982757545786, 'support': 27619.0} | {'precision': 0.9380564528305959, 'recall': 0.9381585140664036, 'f1-score': 0.9379565394756785, 'support': 27619.0} |
+| 0.1259        | 13.0  | 533  | 0.2612          | {'precision': 0.8656716417910447, 'recall': 0.8897411313518696, 'f1-score': 0.8775413711583925, 'support': 1043.0} | {'precision': 0.9471240942028986, 'recall': 0.9642651296829972, 'f1-score': 0.9556177528988404, 'support': 17350.0} | {'precision': 0.9312169312169312, 'recall': 0.8965965748970302, 'f1-score': 0.9135788834281297, 'support': 9226.0} | 0.9388   | {'precision': 0.9146708890702916, 'recall': 0.9168676119772989, 'f1-score': 0.9155793358284542, 'support': 27619.0} | {'precision': 0.9387344206602614, 'recall': 0.9388464462869763, 'f1-score': 0.9386263963728234, 'support': 27619.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e14.md

@@ -0,0 +1,93 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9430826604873457
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2608
+- B: {'precision': 0.8667287977632805, 'recall': 0.8916586768935763, 'f1-score': 0.8790170132325141, 'support': 1043.0}
+- I: {'precision': 0.9495959767192179, 'recall': 0.9685878962536023, 'f1-score': 0.9589979170827745, 'support': 17350.0}
+- O: {'precision': 0.939315176856142, 'recall': 0.9009321482766096, 'f1-score': 0.9197233748271093, 'support': 9226.0}
+- Accuracy: 0.9431
+- Macro avg: {'precision': 0.9185466504462134, 'recall': 0.9203929071412628, 'f1-score': 0.9192461017141326, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9430323383837321, 'recall': 0.9430826604873457, 'f1-score': 0.9428580492538672, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 14
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2982          | {'precision': 0.8073929961089494, 'recall': 0.39789069990412274, 'f1-score': 0.5330764290301863, 'support': 1043.0} | {'precision': 0.881321540062435, 'recall': 0.9763112391930836, 'f1-score': 0.9263877495214657, 'support': 17350.0}  | {'precision': 0.93570069752695, 'recall': 0.7996965098634294, 'f1-score': 0.8623692361638712, 'support': 9226.0}   | 0.8955   | {'precision': 0.8748050778994448, 'recall': 0.724632816320212, 'f1-score': 0.773944471571841, 'support': 27619.0}   | {'precision': 0.8966948206093096, 'recall': 0.8954705094319129, 'f1-score': 0.8901497064529414, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2000          | {'precision': 0.7943201376936316, 'recall': 0.8849472674976031, 'f1-score': 0.83718820861678, 'support': 1043.0}    | {'precision': 0.9348308374930671, 'recall': 0.9714697406340058, 'f1-score': 0.9527981910684004, 'support': 17350.0} | {'precision': 0.9481428740951703, 'recall': 0.866030782570995, 'f1-score': 0.9052285730470742, 'support': 9226.0}  | 0.9330   | {'precision': 0.8924312830939564, 'recall': 0.9074825969008679, 'f1-score': 0.8984049909107515, 'support': 27619.0} | {'precision': 0.9339714359868645, 'recall': 0.9329809189326188, 'f1-score': 0.9325418998354885, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1755          | {'precision': 0.8634192932187201, 'recall': 0.8667305848513902, 'f1-score': 0.8650717703349282, 'support': 1043.0}  | {'precision': 0.9657913330193123, 'recall': 0.9454178674351585, 'f1-score': 0.9554960097862178, 'support': 17350.0} | {'precision': 0.9005006257822278, 'recall': 0.9358335139822241, 'f1-score': 0.9178271499946848, 'support': 9226.0} | 0.9392   | {'precision': 0.9099037506734201, 'recall': 0.9159939887562576, 'f1-score': 0.9127983100386103, 'support': 27619.0} | {'precision': 0.9401153091777049, 'recall': 0.939244722835729, 'f1-score': 0.9394981321590635, 'support': 27619.0}  |
+| No log        | 4.0   | 164  | 0.1881          | {'precision': 0.8497164461247637, 'recall': 0.8619367209971237, 'f1-score': 0.8557829604950024, 'support': 1043.0}  | {'precision': 0.9385960028948394, 'recall': 0.9717579250720461, 'f1-score': 0.9548891343131425, 'support': 17350.0} | {'precision': 0.9423121656199116, 'recall': 0.8781703880338174, 'f1-score': 0.9091113105924596, 'support': 9226.0} | 0.9363   | {'precision': 0.9102082048798383, 'recall': 0.9039550113676623, 'f1-score': 0.9065944684668681, 'support': 27619.0} | {'precision': 0.9364809349919582, 'recall': 0.9363481661175278, 'f1-score': 0.9358546312196439, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.2061          | {'precision': 0.8460846084608461, 'recall': 0.9012464046021093, 'f1-score': 0.872794800371402, 'support': 1043.0}   | {'precision': 0.9360735652559273, 'recall': 0.9739481268011527, 'f1-score': 0.9546353313372126, 'support': 17350.0} | {'precision': 0.9493850520340587, 'recall': 0.8701495772815955, 'f1-score': 0.9080420766881574, 'support': 9226.0} | 0.9365   | {'precision': 0.9105144085836107, 'recall': 0.9151147028949524, 'f1-score': 0.9118240694655907, 'support': 27619.0} | {'precision': 0.9371218760230721, 'recall': 0.9365292009124153, 'f1-score': 0.9359804545788388, 'support': 27619.0} |
+| No log        | 6.0   | 246  | 0.1957          | {'precision': 0.844061650045331, 'recall': 0.8926174496644296, 'f1-score': 0.8676607642124884, 'support': 1043.0}   | {'precision': 0.9341138786104821, 'recall': 0.9748703170028818, 'f1-score': 0.9540570268212201, 'support': 17350.0} | {'precision': 0.9499345938875015, 'recall': 0.865814003902016, 'f1-score': 0.9059257159058689, 'support': 9226.0}  | 0.9353   | {'precision': 0.9093700408477714, 'recall': 0.9111005901897758, 'f1-score': 0.9092145023131923, 'support': 27619.0} | {'precision': 0.9359979962379243, 'recall': 0.9353343712661574, 'f1-score': 0.9347163274329027, 'support': 27619.0} |
+| No log        | 7.0   | 287  | 0.1979          | {'precision': 0.8534562211981567, 'recall': 0.887823585810163, 'f1-score': 0.8703007518796992, 'support': 1043.0}   | {'precision': 0.9429021904386509, 'recall': 0.9651296829971182, 'f1-score': 0.9538864678572446, 'support': 17350.0} | {'precision': 0.9317378917378918, 'recall': 0.8861911987860395, 'f1-score': 0.908393978112327, 'support': 9226.0}  | 0.9358   | {'precision': 0.9093654344582331, 'recall': 0.9130481558644402, 'f1-score': 0.9108603992830903, 'support': 27619.0} | {'precision': 0.9357949828738935, 'recall': 0.9358412686918426, 'f1-score': 0.9355333916361219, 'support': 27619.0} |
+| No log        | 8.0   | 328  | 0.2078          | {'precision': 0.8595194085027726, 'recall': 0.8916586768935763, 'f1-score': 0.8752941176470588, 'support': 1043.0}  | {'precision': 0.9472519993193806, 'recall': 0.9625936599423631, 'f1-score': 0.9548612103713445, 'support': 17350.0} | {'precision': 0.9278014821468673, 'recall': 0.8956210708866248, 'f1-score': 0.9114273108316787, 'support': 9226.0} | 0.9375   | {'precision': 0.9115242966563403, 'recall': 0.9166244692408547, 'f1-score': 0.9138608796166939, 'support': 27619.0} | {'precision': 0.9374415223413826, 'recall': 0.9375429957637857, 'f1-score': 0.9373475554647807, 'support': 27619.0} |
+| No log        | 9.0   | 369  | 0.2136          | {'precision': 0.8539944903581267, 'recall': 0.8916586768935763, 'f1-score': 0.8724202626641651, 'support': 1043.0}  | {'precision': 0.9485901936360548, 'recall': 0.9656484149855907, 'f1-score': 0.9570432994401918, 'support': 17350.0} | {'precision': 0.934596301308074, 'recall': 0.8983308042488619, 'f1-score': 0.9161047861169449, 'support': 9226.0}  | 0.9404   | {'precision': 0.9123936617674184, 'recall': 0.9185459653760096, 'f1-score': 0.9151894494071007, 'support': 27619.0} | {'precision': 0.9403432995002486, 'recall': 0.940367138564032, 'f1-score': 0.9401722848749406, 'support': 27619.0}  |
+| No log        | 10.0  | 410  | 0.2702          | {'precision': 0.8539944903581267, 'recall': 0.8916586768935763, 'f1-score': 0.8724202626641651, 'support': 1043.0}  | {'precision': 0.9347357959251283, 'recall': 0.9757348703170029, 'f1-score': 0.9547954090409182, 'support': 17350.0} | {'precision': 0.9510630716237083, 'recall': 0.8678734012573163, 'f1-score': 0.9075658826863134, 'support': 9226.0} | 0.9365   | {'precision': 0.9132644526356545, 'recall': 0.9117556494892985, 'f1-score': 0.9115938514637989, 'support': 27619.0} | {'precision': 0.9371407441089408, 'recall': 0.9365292009124153, 'f1-score': 0.9359077995033341, 'support': 27619.0} |
+| No log        | 11.0  | 451  | 0.2582          | {'precision': 0.852157943067034, 'recall': 0.8897411313518696, 'f1-score': 0.8705440900562851, 'support': 1043.0}   | {'precision': 0.9372609876406363, 'recall': 0.9746974063400576, 'f1-score': 0.9556126917752098, 'support': 17350.0} | {'precision': 0.9494521032166844, 'recall': 0.87340125731628, 'f1-score': 0.9098402303392988, 'support': 9226.0}   | 0.9377   | {'precision': 0.9129570113081181, 'recall': 0.9126132650027358, 'f1-score': 0.9119990040569311, 'support': 27619.0} | {'precision': 0.9381195544538573, 'recall': 0.9376516166407184, 'f1-score': 0.9371100928107088, 'support': 27619.0} |
+| No log        | 12.0  | 492  | 0.2540          | {'precision': 0.8534798534798534, 'recall': 0.8935762224352828, 'f1-score': 0.8730679156908665, 'support': 1043.0}  | {'precision': 0.944104607441495, 'recall': 0.9696253602305476, 'f1-score': 0.9566948164576758, 'support': 17350.0}  | {'precision': 0.9408589802480478, 'recall': 0.8880338174723608, 'f1-score': 0.9136835061893611, 'support': 9226.0} | 0.9395   | {'precision': 0.9128144803897987, 'recall': 0.9170784667127304, 'f1-score': 0.9144820794459679, 'support': 27619.0} | {'precision': 0.9395980802367181, 'recall': 0.9394981715485716, 'f1-score': 0.9391690115394944, 'support': 27619.0} |
+| 0.1251        | 13.0  | 533  | 0.2575          | {'precision': 0.8613406795224977, 'recall': 0.8993288590604027, 'f1-score': 0.8799249530956847, 'support': 1043.0}  | {'precision': 0.9515141204491323, 'recall': 0.9670893371757925, 'f1-score': 0.9592385090327007, 'support': 17350.0} | {'precision': 0.9372751798561151, 'recall': 0.9037502709733363, 'f1-score': 0.9202074826178127, 'support': 9226.0} | 0.9434   | {'precision': 0.916709993275915, 'recall': 0.9233894890698439, 'f1-score': 0.9197903149153994, 'support': 27619.0}  | {'precision': 0.9433523707551661, 'recall': 0.9433723161591658, 'f1-score': 0.9432051881830659, 'support': 27619.0} |
+| 0.1251        | 14.0  | 574  | 0.2608          | {'precision': 0.8667287977632805, 'recall': 0.8916586768935763, 'f1-score': 0.8790170132325141, 'support': 1043.0}  | {'precision': 0.9495959767192179, 'recall': 0.9685878962536023, 'f1-score': 0.9589979170827745, 'support': 17350.0} | {'precision': 0.939315176856142, 'recall': 0.9009321482766096, 'f1-score': 0.9197233748271093, 'support': 9226.0}  | 0.9431   | {'precision': 0.9185466504462134, 'recall': 0.9203929071412628, 'f1-score': 0.9192461017141326, 'support': 27619.0} | {'precision': 0.9430323383837321, 'recall': 0.9430826604873457, 'f1-score': 0.9428580492538672, 'support': 27619.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e15.md

@@ -0,0 +1,94 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9412361055794923
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2837
+- B: {'precision': 0.8616822429906542, 'recall': 0.8839884947267498, 'f1-score': 0.872692853762423, 'support': 1043.0}
+- I: {'precision': 0.9506446299767138, 'recall': 0.9647262247838617, 'f1-score': 0.957633664216037, 'support': 17350.0}
+- O: {'precision': 0.9322299261910088, 'recall': 0.9035334923043572, 'f1-score': 0.9176574196389256, 'support': 9226.0}
+- Accuracy: 0.9412
+- Macro avg: {'precision': 0.9148522663861257, 'recall': 0.9174160706049896, 'f1-score': 0.9159946458724618, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9411337198513156, 'recall': 0.9412361055794923, 'f1-score': 0.9410720907422853, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2927          | {'precision': 0.8069852941176471, 'recall': 0.42090124640460214, 'f1-score': 0.5532451165721487, 'support': 1043.0} | {'precision': 0.8852390417407678, 'recall': 0.9754466858789625, 'f1-score': 0.9281561917297357, 'support': 17350.0} | {'precision': 0.9349000879728541, 'recall': 0.8063082592672881, 'f1-score': 0.8658557876971424, 'support': 9226.0} | 0.8980   | {'precision': 0.8757081412770896, 'recall': 0.7342187305169509, 'f1-score': 0.7824190319996757, 'support': 27619.0} | {'precision': 0.8988729225389979, 'recall': 0.8980049965603389, 'f1-score': 0.8931869394398603, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.1958          | {'precision': 0.7986171132238548, 'recall': 0.8859060402684564, 'f1-score': 0.84, 'support': 1043.0}                | {'precision': 0.9361619307123394, 'recall': 0.9703170028818444, 'f1-score': 0.9529335182407381, 'support': 17350.0} | {'precision': 0.9455124425050124, 'recall': 0.8689572946022112, 'f1-score': 0.905619881389438, 'support': 9226.0}  | 0.9333   | {'precision': 0.8934304954804023, 'recall': 0.908393445917504, 'f1-score': 0.8995177998767253, 'support': 27619.0}  | {'precision': 0.9340912032116592, 'recall': 0.933270574604439, 'f1-score': 0.932863809956036, 'support': 27619.0}   |
+| No log        | 3.0   | 123  | 0.1754          | {'precision': 0.8552631578947368, 'recall': 0.87248322147651, 'f1-score': 0.8637873754152824, 'support': 1043.0}    | {'precision': 0.966759166322253, 'recall': 0.9437463976945245, 'f1-score': 0.9551141832181294, 'support': 17350.0}  | {'precision': 0.8988355167394468, 'recall': 0.9370257966616085, 'f1-score': 0.9175334323922734, 'support': 9226.0} | 0.9388   | {'precision': 0.9069526136521455, 'recall': 0.9177518052775477, 'f1-score': 0.9121449970085617, 'support': 27619.0} | {'precision': 0.9398590639347346, 'recall': 0.9388102393279989, 'f1-score': 0.9391116535227126, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1844          | {'precision': 0.861003861003861, 'recall': 0.8552253116011506, 'f1-score': 0.8581048581048581, 'support': 1043.0}   | {'precision': 0.9428187016481668, 'recall': 0.9693371757925072, 'f1-score': 0.9558940547914061, 'support': 17350.0} | {'precision': 0.9376786735277302, 'recall': 0.8887925428137872, 'f1-score': 0.912581381114017, 'support': 9226.0}  | 0.9381   | {'precision': 0.9138337453932527, 'recall': 0.904451676735815, 'f1-score': 0.908860098003427, 'support': 27619.0}   | {'precision': 0.9380120548386821, 'recall': 0.9381223071074261, 'f1-score': 0.937732757876541, 'support': 27619.0}  |
+| No log        | 5.0   | 205  | 0.2030          | {'precision': 0.8463611859838275, 'recall': 0.9031639501438159, 'f1-score': 0.8738404452690166, 'support': 1043.0}  | {'precision': 0.9367116741679169, 'recall': 0.9716426512968299, 'f1-score': 0.9538574702237813, 'support': 17350.0} | {'precision': 0.9452344576330943, 'recall': 0.8717754172989378, 'f1-score': 0.9070200169157033, 'support': 9226.0} | 0.9357   | {'precision': 0.9094357725949461, 'recall': 0.9155273395798611, 'f1-score': 0.9115726441361671, 'support': 27619.0} | {'precision': 0.9361466877844027, 'recall': 0.9356964408559325, 'f1-score': 0.9351898826482664, 'support': 27619.0} |
+| No log        | 6.0   | 246  | 0.1880          | {'precision': 0.8593012275731823, 'recall': 0.87248322147651, 'f1-score': 0.8658420551855375, 'support': 1043.0}    | {'precision': 0.9416148372275452, 'recall': 0.9685878962536023, 'f1-score': 0.954910929908799, 'support': 17350.0}  | {'precision': 0.9369907035464249, 'recall': 0.8848905267721656, 'f1-score': 0.9101956630804393, 'support': 9226.0} | 0.9370   | {'precision': 0.9126355894490508, 'recall': 0.9086538815007593, 'f1-score': 0.9103162160582586, 'support': 27619.0} | {'precision': 0.9369616871420418, 'recall': 0.9369998913791231, 'f1-score': 0.9366104162010322, 'support': 27619.0} |
+| No log        | 7.0   | 287  | 0.1950          | {'precision': 0.8525345622119815, 'recall': 0.8868648130393096, 'f1-score': 0.8693609022556391, 'support': 1043.0}  | {'precision': 0.9470030477480528, 'recall': 0.9670893371757925, 'f1-score': 0.9569408007300102, 'support': 17350.0} | {'precision': 0.9362522686025408, 'recall': 0.8946455668762194, 'f1-score': 0.9149761667220929, 'support': 9226.0} | 0.9399   | {'precision': 0.9119299595208584, 'recall': 0.9161999056971072, 'f1-score': 0.9137592899025807, 'support': 27619.0} | {'precision': 0.9398443048967325, 'recall': 0.9398602411383468, 'f1-score': 0.939615352760648, 'support': 27619.0}  |
+| No log        | 8.0   | 328  | 0.2260          | {'precision': 0.8517495395948435, 'recall': 0.8868648130393096, 'f1-score': 0.868952559887271, 'support': 1043.0}   | {'precision': 0.933457985041795, 'recall': 0.978328530259366, 'f1-score': 0.955366691056453, 'support': 17350.0}    | {'precision': 0.9556833153671098, 'recall': 0.8648384998916107, 'f1-score': 0.9079943100995733, 'support': 9226.0} | 0.9370   | {'precision': 0.9136302800012494, 'recall': 0.9100106143967621, 'f1-score': 0.9107711870144325, 'support': 27619.0} | {'precision': 0.9377966283301177, 'recall': 0.9369636844201455, 'f1-score': 0.9362788339465783, 'support': 27619.0} |
+| No log        | 9.0   | 369  | 0.2217          | {'precision': 0.8499079189686924, 'recall': 0.8849472674976031, 'f1-score': 0.8670737435415689, 'support': 1043.0}  | {'precision': 0.9531535648994516, 'recall': 0.9616138328530259, 'f1-score': 0.9573650083204224, 'support': 17350.0} | {'precision': 0.927455975191051, 'recall': 0.9076522870149577, 'f1-score': 0.9174472747192549, 'support': 9226.0}  | 0.9407   | {'precision': 0.910172486353065, 'recall': 0.9180711291218623, 'f1-score': 0.9139620088604153, 'support': 27619.0}  | {'precision': 0.9406704492415535, 'recall': 0.9406930011948297, 'f1-score': 0.9406209263707241, 'support': 27619.0} |
+| No log        | 10.0  | 410  | 0.2663          | {'precision': 0.8574091332712023, 'recall': 0.8820709491850431, 'f1-score': 0.8695652173913044, 'support': 1043.0}  | {'precision': 0.9361054205193511, 'recall': 0.9744668587896254, 'f1-score': 0.9549010194572307, 'support': 17350.0} | {'precision': 0.9483794932233353, 'recall': 0.8722089746368957, 'f1-score': 0.9087008074078257, 'support': 9226.0} | 0.9368   | {'precision': 0.9139646823379629, 'recall': 0.9095822608705214, 'f1-score': 0.9110556814187869, 'support': 27619.0} | {'precision': 0.937233642655096, 'recall': 0.9368188565842355, 'f1-score': 0.9362454418504176, 'support': 27619.0}  |
+| No log        | 11.0  | 451  | 0.2752          | {'precision': 0.8570110701107011, 'recall': 0.8906999041227229, 'f1-score': 0.8735307945463094, 'support': 1043.0}  | {'precision': 0.9348246340789838, 'recall': 0.9755043227665706, 'f1-score': 0.954731349598082, 'support': 17350.0}  | {'precision': 0.9505338078291815, 'recall': 0.8685237372642532, 'f1-score': 0.9076801087449027, 'support': 9226.0} | 0.9366   | {'precision': 0.9141231706729555, 'recall': 0.9115759880511822, 'f1-score': 0.911980750963098, 'support': 27619.0}  | {'precision': 0.9371336709666482, 'recall': 0.9365654078713929, 'f1-score': 0.9359476526130199, 'support': 27619.0} |
+| No log        | 12.0  | 492  | 0.2662          | {'precision': 0.8555657773689053, 'recall': 0.8916586768935763, 'f1-score': 0.8732394366197183, 'support': 1043.0}  | {'precision': 0.9461304151624549, 'recall': 0.9667435158501441, 'f1-score': 0.9563259022749302, 'support': 17350.0} | {'precision': 0.9358246251703771, 'recall': 0.8930197268588771, 'f1-score': 0.9139212423738213, 'support': 9226.0} | 0.9393   | {'precision': 0.9125069392339125, 'recall': 0.9171406398675325, 'f1-score': 0.9144955270894899, 'support': 27619.0} | {'precision': 0.9392677432450942, 'recall': 0.9392809297947066, 'f1-score': 0.9390231550383894, 'support': 27619.0} |
+| 0.1232        | 13.0  | 533  | 0.2681          | {'precision': 0.8646895273401297, 'recall': 0.8945349952061361, 'f1-score': 0.8793590951932139, 'support': 1043.0}  | {'precision': 0.9548364966841985, 'recall': 0.9626512968299712, 'f1-score': 0.9587279719878308, 'support': 17350.0} | {'precision': 0.9293766578249337, 'recall': 0.9114459137220897, 'f1-score': 0.9203239575352961, 'support': 9226.0} | 0.9430   | {'precision': 0.9163008939497539, 'recall': 0.9228774019193989, 'f1-score': 0.9194703415721136, 'support': 27619.0} | {'precision': 0.9429274571700438, 'recall': 0.9429740396104132, 'f1-score': 0.9429020124731535, 'support': 27619.0} |
+| 0.1232        | 14.0  | 574  | 0.2835          | {'precision': 0.8643592142188962, 'recall': 0.8859060402684564, 'f1-score': 0.875, 'support': 1043.0}               | {'precision': 0.9461283248045886, 'recall': 0.9697406340057637, 'f1-score': 0.9577889733299177, 'support': 17350.0} | {'precision': 0.9405726018022128, 'recall': 0.8937784522003035, 'f1-score': 0.9165786694825766, 'support': 9226.0} | 0.9412   | {'precision': 0.9170200469418992, 'recall': 0.9164750421581745, 'f1-score': 0.9164558809374981, 'support': 27619.0} | {'precision': 0.9411845439739722, 'recall': 0.9411998986205149, 'f1-score': 0.9408964297013043, 'support': 27619.0} |
+| 0.1232        | 15.0  | 615  | 0.2837          | {'precision': 0.8616822429906542, 'recall': 0.8839884947267498, 'f1-score': 0.872692853762423, 'support': 1043.0}   | {'precision': 0.9506446299767138, 'recall': 0.9647262247838617, 'f1-score': 0.957633664216037, 'support': 17350.0}  | {'precision': 0.9322299261910088, 'recall': 0.9035334923043572, 'f1-score': 0.9176574196389256, 'support': 9226.0} | 0.9412   | {'precision': 0.9148522663861257, 'recall': 0.9174160706049896, 'f1-score': 0.9159946458724618, 'support': 27619.0} | {'precision': 0.9411337198513156, 'recall': 0.9412361055794923, 'f1-score': 0.9410720907422853, 'support': 27619.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e4.md

@@ -1,5 +1,4 @@
 ---
-license: apache-2.0
 base_model: allenai/longformer-base-4096
 tags:
 - generated_from_trainer
@@ -17,12 +16,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: spans
-      split: test
+      split: train[80%:100%]
       args: spans
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9360779676806765
+      value: 0.9313516057786306
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +31,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1786
-- B: {'precision': 0.8126064735945485, 'recall': 0.9008498583569405, 'f1-score': 0.8544558889386475, 'support': 1059.0}
-- I: {'precision': 0.9468377121729875, 'recall': 0.9617069701280228, 'f1-score': 0.9542144187884605, 'support': 17575.0}
-- O: {'precision': 0.9307744259342638, 'recall': 0.8915363881401617, 'f1-score': 0.9107329698771959, 'support': 9275.0}
-- Accuracy: 0.9361
-- Macro avg: {'precision': 0.8967395372339334, 'recall': 0.918031072208375, 'f1-score': 0.9064677592014346, 'support': 27909.0}
-- Weighted avg: {'precision': 0.9364060284323041, 'recall': 0.9360779676806765, 'f1-score': 0.9359789133327677, 'support': 27909.0}
+- Loss: 0.1886
+- B: {'precision': 0.8005115089514067, 'recall': 0.900287631831256, 'f1-score': 0.8474729241877257, 'support': 1043.0}
+- I: {'precision': 0.9321724709784411, 'recall': 0.9719308357348703, 'f1-score': 0.9516365688487585, 'support': 17350.0}
+- O: {'precision': 0.947941598851125, 'recall': 0.8585519184912205, 'f1-score': 0.9010351495848026, 'support': 9226.0}
+- Accuracy: 0.9314
+- Macro avg: {'precision': 0.8935418595936575, 'recall': 0.9102567953524489, 'f1-score': 0.9000482142070956, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9324680497596853, 'recall': 0.9313516057786306, 'f1-score': 0.9307997762237281, 'support': 27619.0}
 
 ## Model description
 
@@ -69,10 +68,10 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
 |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2836          | {'precision': 0.7968253968253968, 'recall': 0.4740321057601511, 'f1-score': 0.5944345766725874, 'support': 1059.0} | {'precision': 0.9161490683229814, 'recall': 0.9399715504978663, 'f1-score': 0.9279074339315303, 'support': 17575.0} | {'precision': 0.8717421866551314, 'recall': 0.8691105121293801, 'f1-score': 0.8704243602202786, 'support': 9275.0} | 0.8987   | {'precision': 0.8615722172678364, 'recall': 0.7610380561291326, 'f1-score': 0.7975887902747987, 'support': 27909.0} | {'precision': 0.8968636193428943, 'recall': 0.8987423411802644, 'f1-score': 0.8961505359950553, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.2022          | {'precision': 0.7857142857142857, 'recall': 0.8621340887629839, 'f1-score': 0.8221521837010356, 'support': 1059.0} | {'precision': 0.9355464420305919, 'recall': 0.9605120910384068, 'f1-score': 0.9478649035627053, 'support': 17575.0} | {'precision': 0.9268068482132598, 'recall': 0.8696495956873316, 'f1-score': 0.8973189453776839, 'support': 9275.0} | 0.9266   | {'precision': 0.8826891919860458, 'recall': 0.8974319251629076, 'f1-score': 0.889112010880475, 'support': 27909.0}  | {'precision': 0.9269566686171867, 'recall': 0.926582822745351, 'f1-score': 0.9262968240005718, 'support': 27909.0}  |
-| No log        | 3.0   | 123  | 0.1852          | {'precision': 0.7871125611745514, 'recall': 0.9112370160528801, 'f1-score': 0.8446389496717724, 'support': 1059.0} | {'precision': 0.9400952275495515, 'recall': 0.966145092460882, 'f1-score': 0.9529421668490613, 'support': 17575.0}  | {'precision': 0.940726133859181, 'recall': 0.8743935309973045, 'f1-score': 0.9063477872150201, 'support': 9275.0}  | 0.9336   | {'precision': 0.8893113075277613, 'recall': 0.9172585465036889, 'f1-score': 0.9013096345786179, 'support': 27909.0} | {'precision': 0.9345000078114988, 'recall': 0.9335698161883264, 'f1-score': 0.9333479148838716, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.1786          | {'precision': 0.8126064735945485, 'recall': 0.9008498583569405, 'f1-score': 0.8544558889386475, 'support': 1059.0} | {'precision': 0.9468377121729875, 'recall': 0.9617069701280228, 'f1-score': 0.9542144187884605, 'support': 17575.0} | {'precision': 0.9307744259342638, 'recall': 0.8915363881401617, 'f1-score': 0.9107329698771959, 'support': 9275.0} | 0.9361   | {'precision': 0.8967395372339334, 'recall': 0.918031072208375, 'f1-score': 0.9064677592014346, 'support': 27909.0}  | {'precision': 0.9364060284323041, 'recall': 0.9360779676806765, 'f1-score': 0.9359789133327677, 'support': 27909.0} |
+| No log        | 1.0   | 41   | 0.3465          | {'precision': 0.7459016393442623, 'recall': 0.174496644295302, 'f1-score': 0.2828282828282829, 'support': 1043.0}  | {'precision': 0.8462454712392674, 'recall': 0.9827665706051874, 'f1-score': 0.90941091762447, 'support': 17350.0}   | {'precision': 0.9458898422363686, 'recall': 0.7408411012356384, 'f1-score': 0.8309020179917336, 'support': 9226.0} | 0.8714   | {'precision': 0.8460123176066329, 'recall': 0.6327014387120425, 'f1-score': 0.6743804061481621, 'support': 27619.0} | {'precision': 0.8757418451178571, 'recall': 0.8714290886708426, 'f1-score': 0.8595232027867116, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2059          | {'precision': 0.7637130801687764, 'recall': 0.8676893576222435, 'f1-score': 0.8123877917414721, 'support': 1043.0} | {'precision': 0.9387513394619593, 'recall': 0.9593659942363112, 'f1-score': 0.9489467232975115, 'support': 17350.0} | {'precision': 0.9291049063541308, 'recall': 0.8764361586819857, 'f1-score': 0.9020023425734843, 'support': 9226.0} | 0.9282   | {'precision': 0.8771897753282888, 'recall': 0.9011638368468469, 'f1-score': 0.8877789525374893, 'support': 27619.0} | {'precision': 0.9289188728159687, 'recall': 0.9282016003475868, 'f1-score': 0.9281081765661735, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1926          | {'precision': 0.7828618968386023, 'recall': 0.9022051773729626, 'f1-score': 0.8383073496659242, 'support': 1043.0} | {'precision': 0.9354406344242153, 'recall': 0.9654178674351584, 'f1-score': 0.950192874971636, 'support': 17350.0}  | {'precision': 0.9381976266008695, 'recall': 0.8654888358985476, 'f1-score': 0.9003777414444383, 'support': 9226.0} | 0.9296   | {'precision': 0.8855000526212291, 'recall': 0.9110372935688895, 'f1-score': 0.896292655360666, 'support': 27619.0}  | {'precision': 0.9305996331758, 'recall': 0.9296498787066875, 'f1-score': 0.9293271294770207, 'support': 27619.0}    |
+| No log        | 4.0   | 164  | 0.1886          | {'precision': 0.8005115089514067, 'recall': 0.900287631831256, 'f1-score': 0.8474729241877257, 'support': 1043.0}  | {'precision': 0.9321724709784411, 'recall': 0.9719308357348703, 'f1-score': 0.9516365688487585, 'support': 17350.0} | {'precision': 0.947941598851125, 'recall': 0.8585519184912205, 'f1-score': 0.9010351495848026, 'support': 9226.0}  | 0.9314   | {'precision': 0.8935418595936575, 'recall': 0.9102567953524489, 'f1-score': 0.9000482142070956, 'support': 27619.0} | {'precision': 0.9324680497596853, 'recall': 0.9313516057786306, 'f1-score': 0.9307997762237281, 'support': 27619.0} |
 
 
 ### Framework versions

meta_data/README_s42_e5.md

@@ -1,5 +1,4 @@
 ---
-license: apache-2.0
 base_model: allenai/longformer-base-4096
 tags:
 - generated_from_trainer
@@ -17,12 +16,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: spans
-      split: test
+      split: train[80%:100%]
       args: spans
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9400193485972267
+      value: 0.935805061732865
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +31,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1716
-- B: {'precision': 0.8278829604130808, 'recall': 0.9084041548630784, 'f1-score': 0.8662764520486267, 'support': 1059.0}
-- I: {'precision': 0.949054915557544, 'recall': 0.9656330014224751, 'f1-score': 0.9572721888484643, 'support': 17575.0}
-- O: {'precision': 0.9364918217710095, 'recall': 0.8950943396226415, 'f1-score': 0.9153252480705623, 'support': 9275.0}
-- Accuracy: 0.9400
-- Macro avg: {'precision': 0.9044765659138781, 'recall': 0.9230438319693982, 'f1-score': 0.9129579629892177, 'support': 27909.0}
-- Weighted avg: {'precision': 0.9402819822611845, 'recall': 0.9400193485972267, 'f1-score': 0.9398791485752167, 'support': 27909.0}
+- Loss: 0.1821
+- B: {'precision': 0.8143972246313964, 'recall': 0.900287631831256, 'f1-score': 0.8551912568306012, 'support': 1043.0}
+- I: {'precision': 0.9392924896774913, 'recall': 0.9702593659942363, 'f1-score': 0.9545248355636199, 'support': 17350.0}
+- O: {'precision': 0.944873595505618, 'recall': 0.8750270973336224, 'f1-score': 0.9086100168823861, 'support': 9226.0}
+- Accuracy: 0.9358
+- Macro avg: {'precision': 0.8995211032715019, 'recall': 0.9151913650530382, 'f1-score': 0.9061087030922024, 'support': 27619.0}
+- Weighted avg: {'precision': 0.936440305345228, 'recall': 0.935805061732865, 'f1-score': 0.9354359822462803, 'support': 27619.0}
 
 ## Model description
 
@@ -67,13 +66,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2820          | {'precision': 0.8252595155709342, 'recall': 0.45042492917847027, 'f1-score': 0.582773365913256, 'support': 1059.0} | {'precision': 0.9113681210260908, 'recall': 0.9460597439544808, 'f1-score': 0.9283899606354169, 'support': 17575.0} | {'precision': 0.879608231539562, 'recall': 0.8617789757412398, 'f1-score': 0.8706023309007732, 'support': 9275.0}  | 0.8992   | {'precision': 0.8720786227121957, 'recall': 0.7527545496247302, 'f1-score': 0.793921885816482, 'support': 27909.0}  | {'precision': 0.8975459852217064, 'recall': 0.8992439714787345, 'f1-score': 0.896071058503503, 'support': 27909.0}  |
-| No log        | 2.0   | 82   | 0.1953          | {'precision': 0.812897366030881, 'recall': 0.8451369216241738, 'f1-score': 0.8287037037037038, 'support': 1059.0}  | {'precision': 0.9452124358178637, 'recall': 0.9531721194879089, 'f1-score': 0.9491755906850247, 'support': 17575.0} | {'precision': 0.9121629058888278, 'recall': 0.8934770889487871, 'f1-score': 0.902723311546841, 'support': 9275.0}  | 0.9292   | {'precision': 0.8900909025791909, 'recall': 0.8972620433536234, 'f1-score': 0.8935342019785232, 'support': 27909.0} | {'precision': 0.9292084210199053, 'recall': 0.9292342971801211, 'f1-score': 0.9291668258665118, 'support': 27909.0} |
-| No log        | 3.0   | 123  | 0.1858          | {'precision': 0.7883211678832117, 'recall': 0.9178470254957507, 'f1-score': 0.8481675392670156, 'support': 1059.0} | {'precision': 0.9373831775700935, 'recall': 0.9701849217638692, 'f1-score': 0.9535020271214875, 'support': 17575.0} | {'precision': 0.9481498939429649, 'recall': 0.8674932614555256, 'f1-score': 0.9060300658746692, 'support': 9275.0} | 0.9341   | {'precision': 0.8912847464654234, 'recall': 0.9185084029050485, 'f1-score': 0.9025665440877241, 'support': 27909.0} | {'precision': 0.9353051606615684, 'recall': 0.9340714464867964, 'f1-score': 0.9337287760841115, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.1704          | {'precision': 0.8296943231441049, 'recall': 0.8970727101038716, 'f1-score': 0.8620689655172413, 'support': 1059.0} | {'precision': 0.9604448520981427, 'recall': 0.9532859174964438, 'f1-score': 0.9568519946314857, 'support': 17575.0} | {'precision': 0.9158798283261803, 'recall': 0.9203234501347709, 'f1-score': 0.9180962624361388, 'support': 9275.0} | 0.9402   | {'precision': 0.9020063345228092, 'recall': 0.923560692578362, 'f1-score': 0.9123390741949553, 'support': 27909.0}  | {'precision': 0.9406732585029842, 'recall': 0.9401985022752517, 'f1-score': 0.9403757810823142, 'support': 27909.0} |
-| No log        | 5.0   | 205  | 0.1716          | {'precision': 0.8278829604130808, 'recall': 0.9084041548630784, 'f1-score': 0.8662764520486267, 'support': 1059.0} | {'precision': 0.949054915557544, 'recall': 0.9656330014224751, 'f1-score': 0.9572721888484643, 'support': 17575.0}  | {'precision': 0.9364918217710095, 'recall': 0.8950943396226415, 'f1-score': 0.9153252480705623, 'support': 9275.0} | 0.9400   | {'precision': 0.9044765659138781, 'recall': 0.9230438319693982, 'f1-score': 0.9129579629892177, 'support': 27909.0} | {'precision': 0.9402819822611845, 'recall': 0.9400193485972267, 'f1-score': 0.9398791485752167, 'support': 27909.0} |
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.3420          | {'precision': 0.7641196013289037, 'recall': 0.22051773729626079, 'f1-score': 0.3422619047619047, 'support': 1043.0} | {'precision': 0.8498853325356466, 'recall': 0.9825360230547551, 'f1-score': 0.9114093242087253, 'support': 17350.0} | {'precision': 0.9462809917355371, 'recall': 0.7446347279427704, 'f1-score': 0.8334344292126653, 'support': 9226.0} | 0.8743   | {'precision': 0.8534286418666958, 'recall': 0.6492294960979287, 'f1-score': 0.6957018860610984, 'support': 27619.0} | {'precision': 0.8788470144984097, 'recall': 0.8742894384300662, 'f1-score': 0.8638689664942287, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2028          | {'precision': 0.7734241908006815, 'recall': 0.8705656759348035, 'f1-score': 0.8191249436175011, 'support': 1043.0}  | {'precision': 0.9413330313154765, 'recall': 0.9580979827089338, 'f1-score': 0.9496415207518066, 'support': 17350.0} | {'precision': 0.9263601183701343, 'recall': 0.8821807934099285, 'f1-score': 0.9037308461025984, 'support': 9226.0} | 0.9294   | {'precision': 0.8803724468287641, 'recall': 0.903614817351222, 'f1-score': 0.8908324368239686, 'support': 27619.0}  | {'precision': 0.9299905129226795, 'recall': 0.9294326369528223, 'f1-score': 0.9293764613990178, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.2004          | {'precision': 0.7942905121746432, 'recall': 0.9069990412272292, 'f1-score': 0.8469113697403761, 'support': 1043.0}  | {'precision': 0.9219560115701577, 'recall': 0.9736599423631124, 'f1-score': 0.9471028508956354, 'support': 17350.0} | {'precision': 0.9505243676742752, 'recall': 0.835031432907002, 'f1-score': 0.8890427557555824, 'support': 9226.0}  | 0.9248   | {'precision': 0.8889236304730254, 'recall': 0.9052301388324479, 'f1-score': 0.8943523254638647, 'support': 27619.0} | {'precision': 0.9266779977951141, 'recall': 0.9248343531626778, 'f1-score': 0.9239245260972333, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1732          | {'precision': 0.8319928507596068, 'recall': 0.8926174496644296, 'f1-score': 0.8612395929694727, 'support': 1043.0}  | {'precision': 0.9531670965892806, 'recall': 0.9583861671469741, 'f1-score': 0.9557695071130909, 'support': 17350.0} | {'precision': 0.9240198785201547, 'recall': 0.9068935616735313, 'f1-score': 0.9153766205349817, 'support': 9226.0} | 0.9387   | {'precision': 0.9030599419563474, 'recall': 0.9192990594949784, 'f1-score': 0.9107952402058485, 'support': 27619.0} | {'precision': 0.9388545953290572, 'recall': 0.9387016184510663, 'f1-score': 0.9387066347418453, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.1821          | {'precision': 0.8143972246313964, 'recall': 0.900287631831256, 'f1-score': 0.8551912568306012, 'support': 1043.0}   | {'precision': 0.9392924896774913, 'recall': 0.9702593659942363, 'f1-score': 0.9545248355636199, 'support': 17350.0} | {'precision': 0.944873595505618, 'recall': 0.8750270973336224, 'f1-score': 0.9086100168823861, 'support': 9226.0}  | 0.9358   | {'precision': 0.8995211032715019, 'recall': 0.9151913650530382, 'f1-score': 0.9061087030922024, 'support': 27619.0} | {'precision': 0.936440305345228, 'recall': 0.935805061732865, 'f1-score': 0.9354359822462803, 'support': 27619.0}   |
 
 
 ### Framework versions

meta_data/README_s42_e6.md

@@ -1,5 +1,4 @@
 ---
-license: apache-2.0
 base_model: allenai/longformer-base-4096
 tags:
 - generated_from_trainer
@@ -17,12 +16,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: spans
-      split: test
+      split: train[80%:100%]
       args: spans
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9421333619979219
+      value: 0.9382671349433361
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +31,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1716
-- B: {'precision': 0.8420123565754634, 'recall': 0.9008498583569405, 'f1-score': 0.8704379562043796, 'support': 1059.0}
-- I: {'precision': 0.9520763187429854, 'recall': 0.965348506401138, 'f1-score': 0.9586664783161464, 'support': 17575.0}
-- O: {'precision': 0.9350156319785619, 'recall': 0.9028571428571428, 'f1-score': 0.9186550381218803, 'support': 9275.0}
-- Accuracy: 0.9421
-- Macro avg: {'precision': 0.9097014357656702, 'recall': 0.9230185025384072, 'f1-score': 0.9159198242141354, 'support': 27909.0}
-- Weighted avg: {'precision': 0.9422301900506126, 'recall': 0.9421333619979219, 'f1-score': 0.9420216643594235, 'support': 27909.0}
+- Loss: 0.1775
+- B: {'precision': 0.8277385159010601, 'recall': 0.8983700862895494, 'f1-score': 0.8616091954022989, 'support': 1043.0}
+- I: {'precision': 0.9442383361439011, 'recall': 0.9681844380403458, 'f1-score': 0.9560614684120662, 'support': 17350.0}
+- O: {'precision': 0.9404392319190525, 'recall': 0.886516366789508, 'f1-score': 0.9126820286782348, 'support': 9226.0}
+- Accuracy: 0.9383
+- Macro avg: {'precision': 0.9041386946546712, 'recall': 0.9176902970398011, 'f1-score': 0.9101175641641999, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9385697801465175, 'recall': 0.9382671349433361, 'f1-score': 0.9380038837155341, 'support': 27619.0}
 
 ## Model description
 
@@ -67,14 +66,14 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2779          | {'precision': 0.8035190615835777, 'recall': 0.5174693106704438, 'f1-score': 0.6295232624928202, 'support': 1059.0} | {'precision': 0.9134303762702555, 'recall': 0.9461735419630156, 'f1-score': 0.9295136948015652, 'support': 17575.0} | {'precision': 0.8836178230990911, 'recall': 0.8595148247978437, 'f1-score': 0.8713996830081434, 'support': 9275.0} | 0.9011   | {'precision': 0.8668557536509748, 'recall': 0.7743858924771011, 'f1-score': 0.8101455467675095, 'support': 27909.0} | {'precision': 0.8993522110577526, 'recall': 0.9011071697301946, 'f1-score': 0.8988175993771879, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.1973          | {'precision': 0.8130590339892666, 'recall': 0.8583569405099151, 'f1-score': 0.8350941662838769, 'support': 1059.0} | {'precision': 0.9326064325242452, 'recall': 0.9684779516358464, 'f1-score': 0.9502037626304918, 'support': 17575.0} | {'precision': 0.9385245901639344, 'recall': 0.8641509433962264, 'f1-score': 0.899803536345776, 'support': 9275.0}  | 0.9296   | {'precision': 0.8947300188924822, 'recall': 0.896995278513996, 'f1-score': 0.8950338217533815, 'support': 27909.0}  | {'precision': 0.9300370182514147, 'recall': 0.9296284352717761, 'f1-score': 0.9290864470218421, 'support': 27909.0} |
-| No log        | 3.0   | 123  | 0.1836          | {'precision': 0.788197251414713, 'recall': 0.9206798866855525, 'f1-score': 0.8493031358885017, 'support': 1059.0}  | {'precision': 0.938334252619967, 'recall': 0.9679658605974395, 'f1-score': 0.9529197591373757, 'support': 17575.0}  | {'precision': 0.943807070943573, 'recall': 0.8692183288409704, 'f1-score': 0.904978391423921, 'support': 9275.0}   | 0.9334   | {'precision': 0.8901128583260842, 'recall': 0.9192880253746541, 'f1-score': 0.9024004288165995, 'support': 27909.0} | {'precision': 0.9344561239043228, 'recall': 0.9333548317746964, 'f1-score': 0.9330556941560847, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.1709          | {'precision': 0.8227739726027398, 'recall': 0.9074598677998111, 'f1-score': 0.8630444544229906, 'support': 1059.0} | {'precision': 0.9512620158524931, 'recall': 0.9628449502133712, 'f1-score': 0.9570184368284129, 'support': 17575.0} | {'precision': 0.9324173369079535, 'recall': 0.8999460916442048, 'f1-score': 0.9158940034015471, 'support': 9275.0} | 0.9398   | {'precision': 0.9021511084543955, 'recall': 0.9234169698857958, 'f1-score': 0.9119856315509836, 'support': 27909.0} | {'precision': 0.9401239157768152, 'recall': 0.9398401949192017, 'f1-score': 0.9397857317009801, 'support': 27909.0} |
-| No log        | 5.0   | 205  | 0.1695          | {'precision': 0.8363954505686789, 'recall': 0.902738432483475, 'f1-score': 0.8683015440508628, 'support': 1059.0}  | {'precision': 0.9477175185329691, 'recall': 0.9674537695590327, 'f1-score': 0.9574839508953711, 'support': 17575.0} | {'precision': 0.9385835694050991, 'recall': 0.8930458221024259, 'f1-score': 0.9152486187845303, 'support': 9275.0} | 0.9403   | {'precision': 0.9075655128355824, 'recall': 0.9210793413816445, 'f1-score': 0.9136780379102548, 'support': 27909.0} | {'precision': 0.9404579446272334, 'recall': 0.9402701637464617, 'f1-score': 0.9400638758594909, 'support': 27909.0} |
-| No log        | 6.0   | 246  | 0.1716          | {'precision': 0.8420123565754634, 'recall': 0.9008498583569405, 'f1-score': 0.8704379562043796, 'support': 1059.0} | {'precision': 0.9520763187429854, 'recall': 0.965348506401138, 'f1-score': 0.9586664783161464, 'support': 17575.0}  | {'precision': 0.9350156319785619, 'recall': 0.9028571428571428, 'f1-score': 0.9186550381218803, 'support': 9275.0} | 0.9421   | {'precision': 0.9097014357656702, 'recall': 0.9230185025384072, 'f1-score': 0.9159198242141354, 'support': 27909.0} | {'precision': 0.9422301900506126, 'recall': 0.9421333619979219, 'f1-score': 0.9420216643594235, 'support': 27909.0} |
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2767          | {'precision': 0.8252032520325203, 'recall': 0.38926174496644295, 'f1-score': 0.5289902280130293, 'support': 1043.0} | {'precision': 0.8942471288813271, 'recall': 0.9693948126801153, 'f1-score': 0.9303058797499861, 'support': 17350.0} | {'precision': 0.9191008534679649, 'recall': 0.8287448515066117, 'f1-score': 0.8715873468224564, 'support': 9226.0} | 0.9005   | {'precision': 0.8795170781272708, 'recall': 0.7291338030510567, 'f1-score': 0.7769611515284907, 'support': 27619.0} | {'precision': 0.8999420381641764, 'recall': 0.9005032767297875, 'f1-score': 0.8955359963526497, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2284          | {'precision': 0.7671568627450981, 'recall': 0.900287631831256, 'f1-score': 0.8284075871195412, 'support': 1043.0}   | {'precision': 0.9168915272531031, 'recall': 0.9792507204610951, 'f1-score': 0.947045707915273, 'support': 17350.0}  | {'precision': 0.9646535282898919, 'recall': 0.8223498807717321, 'f1-score': 0.8878357030015798, 'support': 9226.0} | 0.9239   | {'precision': 0.8829006394293644, 'recall': 0.900629411021361, 'f1-score': 0.8877629993454645, 'support': 27619.0}  | {'precision': 0.9271916455225395, 'recall': 0.9238567652702849, 'f1-score': 0.9227866447586169, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1770          | {'precision': 0.8351648351648352, 'recall': 0.8744007670182167, 'f1-score': 0.8543325526932084, 'support': 1043.0}  | {'precision': 0.9442345644206371, 'recall': 0.9651873198847263, 'f1-score': 0.954595981188542, 'support': 17350.0}  | {'precision': 0.9328935395814377, 'recall': 0.8890093214827661, 'f1-score': 0.9104229104229105, 'support': 9226.0} | 0.9363   | {'precision': 0.90409764638897, 'recall': 0.909532469461903, 'f1-score': 0.906450481434887, 'support': 27619.0}     | {'precision': 0.9363272534108158, 'recall': 0.9363119591585503, 'f1-score': 0.9360538360419275, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1804          | {'precision': 0.8234265734265734, 'recall': 0.9031639501438159, 'f1-score': 0.8614540466392319, 'support': 1043.0}  | {'precision': 0.9435452033162258, 'recall': 0.9642651296829972, 'f1-score': 0.9537926512927226, 'support': 17350.0} | {'precision': 0.9335544373284538, 'recall': 0.8847821374376761, 'f1-score': 0.9085141903171953, 'support': 9226.0} | 0.9354   | {'precision': 0.9001754046904177, 'recall': 0.917403739088163, 'f1-score': 0.9079202960830499, 'support': 27619.0}  | {'precision': 0.9356716909523425, 'recall': 0.9354067851841124, 'f1-score': 0.9351805275513196, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.1774          | {'precision': 0.8283450704225352, 'recall': 0.9022051773729626, 'f1-score': 0.8636989444699403, 'support': 1043.0}  | {'precision': 0.9497974784642592, 'recall': 0.9595965417867435, 'f1-score': 0.9546718655924767, 'support': 17350.0} | {'precision': 0.9269600178691088, 'recall': 0.8996314762627358, 'f1-score': 0.9130913091309132, 'support': 9226.0} | 0.9374   | {'precision': 0.9017008555853011, 'recall': 0.9204777318074807, 'f1-score': 0.9104873730644435, 'support': 27619.0} | {'precision': 0.9375822182072485, 'recall': 0.9373981679278758, 'f1-score': 0.9373465833358712, 'support': 27619.0} |
+| No log        | 6.0   | 246  | 0.1775          | {'precision': 0.8277385159010601, 'recall': 0.8983700862895494, 'f1-score': 0.8616091954022989, 'support': 1043.0}  | {'precision': 0.9442383361439011, 'recall': 0.9681844380403458, 'f1-score': 0.9560614684120662, 'support': 17350.0} | {'precision': 0.9404392319190525, 'recall': 0.886516366789508, 'f1-score': 0.9126820286782348, 'support': 9226.0}  | 0.9383   | {'precision': 0.9041386946546712, 'recall': 0.9176902970398011, 'f1-score': 0.9101175641641999, 'support': 27619.0} | {'precision': 0.9385697801465175, 'recall': 0.9382671349433361, 'f1-score': 0.9380038837155341, 'support': 27619.0} |
 
 
 ### Framework versions

meta_data/README_s42_e7.md

@@ -17,12 +17,12 @@ model-index:
       name: essays_su_g
       type: essays_su_g
       config: spans
-      split: test
+      split: train[80%:100%]
       args: spans
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9420975312623168
+      value: 0.9382309279843586
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,13 +32,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1719
-- B: {'precision': 0.852017937219731, 'recall': 0.8970727101038716, 'f1-score': 0.8739650413983441, 'support': 1059.0}
-- I: {'precision': 0.9538791159224177, 'recall': 0.9626173541963016, 'f1-score': 0.9582283141230779, 'support': 17575.0}
-- O: {'precision': 0.9301170236255244, 'recall': 0.9083557951482479, 'f1-score': 0.919107620138548, 'support': 9275.0}
-- Accuracy: 0.9421
-- Macro avg: {'precision': 0.912004692255891, 'recall': 0.9226819531494738, 'f1-score': 0.91710032521999, 'support': 27909.0}
-- Weighted avg: {'precision': 0.9421171612017244, 'recall': 0.9420975312623168, 'f1-score': 0.9420299823117623, 'support': 27909.0}
+- Loss: 0.1841
+- B: {'precision': 0.8358744394618834, 'recall': 0.8935762224352828, 'f1-score': 0.8637627432808155, 'support': 1043.0}
+- I: {'precision': 0.9433073515392811, 'recall': 0.9695677233429395, 'f1-score': 0.9562572833470712, 'support': 17350.0}
+- O: {'precision': 0.9409526006227655, 'recall': 0.8843485800997182, 'f1-score': 0.9117729228362295, 'support': 9226.0}
+- Accuracy: 0.9382
+- Macro avg: {'precision': 0.9067114638746433, 'recall': 0.9158308419593135, 'f1-score': 0.9105976498213719, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9384636765600096, 'recall': 0.9382309279843586, 'f1-score': 0.9379045364930165, 'support': 27619.0}
 
 ## Model description
 
@@ -67,15 +67,15 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.2928          | {'precision': 0.8236434108527132, 'recall': 0.40132200188857414, 'f1-score': 0.5396825396825397, 'support': 1059.0} | {'precision': 0.9120444175691276, 'recall': 0.9440113798008535, 'f1-score': 0.9277526142146172, 'support': 17575.0} | {'precision': 0.8748098239513149, 'recall': 0.8679245283018868, 'f1-score': 0.87135357471451, 'support': 9275.0}   | 0.8981   | {'precision': 0.8701658841243853, 'recall': 0.7377526366637716, 'f1-score': 0.7795962428705557, 'support': 27909.0} | {'precision': 0.8963158883521046, 'recall': 0.8981332186749794, 'f1-score': 0.8942842957405419, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.1943          | {'precision': 0.8109318996415771, 'recall': 0.8545797922568461, 'f1-score': 0.8321839080459771, 'support': 1059.0}  | {'precision': 0.9395201599466845, 'recall': 0.9625604551920341, 'f1-score': 0.9509007616424496, 'support': 17575.0} | {'precision': 0.9288721975645841, 'recall': 0.88, 'f1-score': 0.9037758830694275, 'support': 9275.0}               | 0.9310   | {'precision': 0.8931080857176151, 'recall': 0.8990467491496267, 'f1-score': 0.895620184252618, 'support': 27909.0}  | {'precision': 0.9311022725713902, 'recall': 0.9310258339603712, 'f1-score': 0.9307350661061193, 'support': 27909.0} |
-| No log        | 3.0   | 123  | 0.1853          | {'precision': 0.799163179916318, 'recall': 0.9017941454202077, 'f1-score': 0.847382431233363, 'support': 1059.0}    | {'precision': 0.9557297671201291, 'recall': 0.9433854907539118, 'f1-score': 0.9495175099504624, 'support': 17575.0} | {'precision': 0.9017723681400811, 'recall': 0.9106199460916442, 'f1-score': 0.9061745614505659, 'support': 9275.0} | 0.9309   | {'precision': 0.8855551050588427, 'recall': 0.9185998607552546, 'f1-score': 0.9010248342114636, 'support': 27909.0} | {'precision': 0.9318572209382959, 'recall': 0.9309183417535563, 'f1-score': 0.9312378547962845, 'support': 27909.0} |
-| No log        | 4.0   | 164  | 0.1717          | {'precision': 0.825491873396065, 'recall': 0.9112370160528801, 'f1-score': 0.8662477558348295, 'support': 1059.0}   | {'precision': 0.9546820940389087, 'recall': 0.957724039829303, 'f1-score': 0.9562006476168834, 'support': 17575.0}  | {'precision': 0.9242507410253595, 'recall': 0.9077088948787062, 'f1-score': 0.915905134899913, 'support': 9275.0}  | 0.9393   | {'precision': 0.9014749028201111, 'recall': 0.9255566502536298, 'f1-score': 0.9127845127838753, 'support': 27909.0} | {'precision': 0.9396667497821657, 'recall': 0.9393385646207316, 'f1-score': 0.9393959970436956, 'support': 27909.0} |
-| No log        | 5.0   | 205  | 0.1734          | {'precision': 0.8358078602620087, 'recall': 0.9036827195467422, 'f1-score': 0.868421052631579, 'support': 1059.0}   | {'precision': 0.9562692176289717, 'recall': 0.9555618776671408, 'f1-score': 0.9559154167971085, 'support': 17575.0} | {'precision': 0.9189306672462508, 'recall': 0.9116981132075471, 'f1-score': 0.915300102830546, 'support': 9275.0}  | 0.9390   | {'precision': 0.903669248379077, 'recall': 0.9236475701404768, 'f1-score': 0.9132121907530778, 'support': 27909.0}  | {'precision': 0.9392896184942356, 'recall': 0.9390160880002867, 'f1-score': 0.9390977748647152, 'support': 27909.0} |
-| No log        | 6.0   | 246  | 0.1677          | {'precision': 0.8308759757155247, 'recall': 0.9046270066100094, 'f1-score': 0.8661844484629294, 'support': 1059.0}  | {'precision': 0.9521587587137396, 'recall': 0.9636984352773826, 'f1-score': 0.9578938438480898, 'support': 17575.0} | {'precision': 0.9325379125780553, 'recall': 0.9016711590296496, 'f1-score': 0.9168448171901551, 'support': 9275.0} | 0.9408   | {'precision': 0.9051908823357732, 'recall': 0.9233322003056804, 'f1-score': 0.9136410365003914, 'support': 27909.0} | {'precision': 0.9410361167307384, 'recall': 0.9408434555161418, 'f1-score': 0.9407721278437462, 'support': 27909.0} |
-| No log        | 7.0   | 287  | 0.1719          | {'precision': 0.852017937219731, 'recall': 0.8970727101038716, 'f1-score': 0.8739650413983441, 'support': 1059.0}   | {'precision': 0.9538791159224177, 'recall': 0.9626173541963016, 'f1-score': 0.9582283141230779, 'support': 17575.0} | {'precision': 0.9301170236255244, 'recall': 0.9083557951482479, 'f1-score': 0.919107620138548, 'support': 9275.0}  | 0.9421   | {'precision': 0.912004692255891, 'recall': 0.9226819531494738, 'f1-score': 0.91710032521999, 'support': 27909.0}    | {'precision': 0.9421171612017244, 'recall': 0.9420975312623168, 'f1-score': 0.9420299823117623, 'support': 27909.0} |
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                    | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2970          | {'precision': 0.8171557562076749, 'recall': 0.34707574304889743, 'f1-score': 0.48721399730820997, 'support': 1043.0} | {'precision': 0.8802934137966912, 'recall': 0.9752737752161383, 'f1-score': 0.9253527288636114, 'support': 17350.0} | {'precision': 0.9304752325873774, 'recall': 0.8021894645566876, 'f1-score': 0.861583236321304, 'support': 9226.0}  | 0.8937   | {'precision': 0.8759748008639145, 'recall': 0.7081796609405745, 'f1-score': 0.7580499874977084, 'support': 27619.0} | {'precision': 0.8946720981551954, 'recall': 0.893732575400992, 'f1-score': 0.8875050140583102, 'support': 27619.0}  |
+| No log        | 2.0   | 82   | 0.2228          | {'precision': 0.7610474631751227, 'recall': 0.8916586768935763, 'f1-score': 0.8211920529801324, 'support': 1043.0}   | {'precision': 0.9182955222264335, 'recall': 0.9775216138328531, 'f1-score': 0.946983444540607, 'support': 17350.0}  | {'precision': 0.9614026236125126, 'recall': 0.8261435074788641, 'f1-score': 0.8886557071237029, 'support': 9226.0} | 0.9237   | {'precision': 0.8802485363380229, 'recall': 0.898441266068431, 'f1-score': 0.8856104015481474, 'support': 27619.0}  | {'precision': 0.9267569578974372, 'recall': 0.9237119374343749, 'f1-score': 0.9227489636830115, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1807          | {'precision': 0.845437616387337, 'recall': 0.8705656759348035, 'f1-score': 0.8578176665092113, 'support': 1043.0}    | {'precision': 0.9587634878973461, 'recall': 0.9474351585014409, 'f1-score': 0.9530656616901, 'support': 17350.0}    | {'precision': 0.9035106382978724, 'recall': 0.9205506178192066, 'f1-score': 0.9119510361859765, 'support': 9226.0} | 0.9356   | {'precision': 0.9025705808608517, 'recall': 0.9128504840851503, 'f1-score': 0.907611454795096, 'support': 27619.0}  | {'precision': 0.9360269053132667, 'recall': 0.9355516130200224, 'f1-score': 0.9357345782375959, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.2177          | {'precision': 0.8223028105167725, 'recall': 0.8696069031639502, 'f1-score': 0.8452935694315005, 'support': 1043.0}   | {'precision': 0.9182645433864154, 'recall': 0.9771181556195966, 'f1-score': 0.9467776164414164, 'support': 17350.0} | {'precision': 0.9526943133846536, 'recall': 0.8316713635378279, 'f1-score': 0.8880787037037038, 'support': 9226.0} | 0.9245   | {'precision': 0.8977538890959472, 'recall': 0.8927988074404581, 'f1-score': 0.8933832965255403, 'support': 27619.0} | {'precision': 0.9261417645247878, 'recall': 0.9244722835729027, 'f1-score': 0.9233370852871574, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.1864          | {'precision': 0.8298059964726632, 'recall': 0.9022051773729626, 'f1-score': 0.8644924207625172, 'support': 1043.0}   | {'precision': 0.9426901899089786, 'recall': 0.9670317002881844, 'f1-score': 0.9547058154091271, 'support': 17350.0} | {'precision': 0.9384137216530448, 'recall': 0.8835898547582918, 'f1-score': 0.9101769664489477, 'support': 9226.0} | 0.9367   | {'precision': 0.9036366360115622, 'recall': 0.9176089108064795, 'f1-score': 0.909791734206864, 'support': 27619.0}  | {'precision': 0.9369987126692769, 'recall': 0.9367102357073029, 'f1-score': 0.9364243522452534, 'support': 27619.0} |
+| No log        | 6.0   | 246  | 0.1768          | {'precision': 0.8413417951042611, 'recall': 0.8897411313518696, 'f1-score': 0.8648648648648648, 'support': 1043.0}   | {'precision': 0.9434724091520862, 'recall': 0.9696829971181556, 'f1-score': 0.9563981581490535, 'support': 17350.0} | {'precision': 0.9409258406264395, 'recall': 0.885649252113592, 'f1-score': 0.9124511446119487, 'support': 9226.0}  | 0.9386   | {'precision': 0.908580014960929, 'recall': 0.9150244601945391, 'f1-score': 0.9112380558752889, 'support': 27619.0}  | {'precision': 0.9387648936131638, 'recall': 0.9385929975741337, 'f1-score': 0.938261209968861, 'support': 27619.0}  |
+| No log        | 7.0   | 287  | 0.1841          | {'precision': 0.8358744394618834, 'recall': 0.8935762224352828, 'f1-score': 0.8637627432808155, 'support': 1043.0}   | {'precision': 0.9433073515392811, 'recall': 0.9695677233429395, 'f1-score': 0.9562572833470712, 'support': 17350.0} | {'precision': 0.9409526006227655, 'recall': 0.8843485800997182, 'f1-score': 0.9117729228362295, 'support': 9226.0} | 0.9382   | {'precision': 0.9067114638746433, 'recall': 0.9158308419593135, 'f1-score': 0.9105976498213719, 'support': 27619.0} | {'precision': 0.9384636765600096, 'recall': 0.9382309279843586, 'f1-score': 0.9379045364930165, 'support': 27619.0} |
 
 
 ### Framework versions

meta_data/README_s42_e8.md

@@ -0,0 +1,87 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9362395452405953
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1974
+- B: {'precision': 0.8404351767905711, 'recall': 0.8887823585810163, 'f1-score': 0.863932898415657, 'support': 1043.0}
+- I: {'precision': 0.9420745397395599, 'recall': 0.9673775216138328, 'f1-score': 0.954558380253654, 'support': 17350.0}
+- O: {'precision': 0.9364367816091954, 'recall': 0.8830479080858443, 'f1-score': 0.9089590538882071, 'support': 9226.0}
+- Accuracy: 0.9362
+- Macro avg: {'precision': 0.9063154993797754, 'recall': 0.9130692627602311, 'f1-score': 0.9091501108525061, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9363529780585962, 'recall': 0.9362395452405953, 'f1-score': 0.9359037670307043, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                   | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2955          | {'precision': 0.7986798679867987, 'recall': 0.46404602109300097, 'f1-score': 0.5870224378411159, 'support': 1043.0} | {'precision': 0.8854450261780105, 'recall': 0.9747550432276657, 'f1-score': 0.9279561042524005, 'support': 17350.0} | {'precision': 0.9346644761784405, 'recall': 0.8016475178842402, 'f1-score': 0.8630608553591224, 'support': 9226.0} | 0.8976   | {'precision': 0.8729297901144165, 'recall': 0.7468161940683024, 'f1-score': 0.7926797991508797, 'support': 27619.0} | {'precision': 0.8986099700829504, 'recall': 0.8976429269705637, 'f1-score': 0.893403174010308, 'support': 27619.0}  |
+| No log        | 2.0   | 82   | 0.2031          | {'precision': 0.784197111299915, 'recall': 0.8849472674976031, 'f1-score': 0.8315315315315316, 'support': 1043.0}   | {'precision': 0.9307149161518093, 'recall': 0.9724495677233429, 'f1-score': 0.9511246406223575, 'support': 17350.0} | {'precision': 0.9504450324753428, 'recall': 0.8564925211359202, 'f1-score': 0.9010262257696694, 'support': 9226.0} | 0.9304   | {'precision': 0.8884523533090224, 'recall': 0.9046297854522888, 'f1-score': 0.8945607993078529, 'support': 27619.0} | {'precision': 0.9317725932125427, 'recall': 0.9304102248452153, 'f1-score': 0.9298731982018269, 'support': 27619.0} |
+| No log        | 3.0   | 123  | 0.1754          | {'precision': 0.8527204502814258, 'recall': 0.8715244487056567, 'f1-score': 0.8620199146514935, 'support': 1043.0}  | {'precision': 0.9616262064931267, 'recall': 0.947492795389049, 'f1-score': 0.9545071853679779, 'support': 17350.0}  | {'precision': 0.9036794248255445, 'recall': 0.9264036418816388, 'f1-score': 0.9149004495825305, 'support': 9226.0} | 0.9376   | {'precision': 0.906008693866699, 'recall': 0.9151402953254482, 'f1-score': 0.9104758498673339, 'support': 27619.0}  | {'precision': 0.9381566488916958, 'recall': 0.9375792027227633, 'f1-score': 0.9377840611522629, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.2248          | {'precision': 0.8219800181653043, 'recall': 0.8676893576222435, 'f1-score': 0.8442164179104478, 'support': 1043.0}  | {'precision': 0.9191395059726502, 'recall': 0.9801152737752161, 'f1-score': 0.9486485732615547, 'support': 17350.0} | {'precision': 0.9589622053137083, 'recall': 0.8332972035551701, 'f1-score': 0.8917241779272748, 'support': 9226.0} | 0.9268   | {'precision': 0.9000272431505542, 'recall': 0.8937006116508766, 'f1-score': 0.8948630563664257, 'support': 27619.0} | {'precision': 0.9287729785218931, 'recall': 0.9268257359064412, 'f1-score': 0.9256894795439954, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.1931          | {'precision': 0.848987108655617, 'recall': 0.8839884947267498, 'f1-score': 0.8661343353687178, 'support': 1043.0}   | {'precision': 0.9373124374791597, 'recall': 0.9721037463976945, 'f1-score': 0.9543911272068809, 'support': 17350.0} | {'precision': 0.9444899871179295, 'recall': 0.8741599826577064, 'f1-score': 0.9079650999155643, 'support': 9226.0} | 0.9361   | {'precision': 0.910263177750902, 'recall': 0.9100840745940503, 'f1-score': 0.909496854163721, 'support': 27619.0}   | {'precision': 0.9363745597502171, 'recall': 0.9360585104457076, 'f1-score': 0.935549809212859, 'support': 27619.0}  |
+| No log        | 6.0   | 246  | 0.1742          | {'precision': 0.8382222222222222, 'recall': 0.9041227229146692, 'f1-score': 0.8699261992619925, 'support': 1043.0}  | {'precision': 0.9481431159420289, 'recall': 0.9653025936599423, 'f1-score': 0.956645913063346, 'support': 17350.0}  | {'precision': 0.9353340883352208, 'recall': 0.8951875135486668, 'f1-score': 0.9148205582631811, 'support': 9226.0} | 0.9396   | {'precision': 0.9072331421664908, 'recall': 0.9215376100410927, 'f1-score': 0.9137975568628399, 'support': 27619.0} | {'precision': 0.9397132821011885, 'recall': 0.9395705854665267, 'f1-score': 0.9393994745651696, 'support': 27619.0} |
+| No log        | 7.0   | 287  | 0.1985          | {'precision': 0.8421052631578947, 'recall': 0.8897411313518696, 'f1-score': 0.8652680652680652, 'support': 1043.0}  | {'precision': 0.9399821009061416, 'recall': 0.9685878962536023, 'f1-score': 0.9540706256386964, 'support': 17350.0} | {'precision': 0.9385345526102559, 'recall': 0.8788207240407544, 'f1-score': 0.9076966134900645, 'support': 9226.0} | 0.9356   | {'precision': 0.9068739722247642, 'recall': 0.9123832505487423, 'f1-score': 0.9090117681322755, 'support': 27619.0} | {'precision': 0.935802347028403, 'recall': 0.9356240269379775, 'f1-score': 0.9352260727385245, 'support': 27619.0}  |
+| No log        | 8.0   | 328  | 0.1974          | {'precision': 0.8404351767905711, 'recall': 0.8887823585810163, 'f1-score': 0.863932898415657, 'support': 1043.0}   | {'precision': 0.9420745397395599, 'recall': 0.9673775216138328, 'f1-score': 0.954558380253654, 'support': 17350.0}  | {'precision': 0.9364367816091954, 'recall': 0.8830479080858443, 'f1-score': 0.9089590538882071, 'support': 9226.0} | 0.9362   | {'precision': 0.9063154993797754, 'recall': 0.9130692627602311, 'f1-score': 0.9091501108525061, 'support': 27619.0} | {'precision': 0.9363529780585962, 'recall': 0.9362395452405953, 'f1-score': 0.9359037670307043, 'support': 27619.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/README_s42_e9.md

@@ -0,0 +1,88 @@
+---
+license: apache-2.0
+base_model: allenai/longformer-base-4096
+tags:
+- generated_from_trainer
+datasets:
+- essays_su_g
+metrics:
+- accuracy
+model-index:
+- name: longformer-spans
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: essays_su_g
+      type: essays_su_g
+      config: spans
+      split: train[80%:100%]
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9389550671639089
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# longformer-spans
+
+This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the essays_su_g dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2009
+- B: {'precision': 0.8471337579617835, 'recall': 0.8926174496644296, 'f1-score': 0.8692810457516339, 'support': 1043.0}
+- I: {'precision': 0.9459794744558475, 'recall': 0.9669164265129683, 'f1-score': 0.9563333713373617, 'support': 17350.0}
+- O: {'precision': 0.9362622353744594, 'recall': 0.8916106655105137, 'f1-score': 0.9133910726182546, 'support': 9226.0}
+- Accuracy: 0.9390
+- Macro avg: {'precision': 0.9097918225973635, 'recall': 0.9170481805626371, 'f1-score': 0.9130018299024169, 'support': 27619.0}
+- Weighted avg: {'precision': 0.9390006797830427, 'recall': 0.9389550671639089, 'f1-score': 0.9387012621528006, 'support': 27619.0}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 9
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | B                                                                                                                  | I                                                                                                                   | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.2937          | {'precision': 0.8082191780821918, 'recall': 0.3959731543624161, 'f1-score': 0.5315315315315315, 'support': 1043.0} | {'precision': 0.8851326600031398, 'recall': 0.9748703170028818, 'f1-score': 0.9278367481280343, 'support': 17350.0} | {'precision': 0.932616577072134, 'recall': 0.8085844352915673, 'f1-score': 0.8661828737300435, 'support': 9226.0}  | 0.8975   | {'precision': 0.8753228050524885, 'recall': 0.7264759688856217, 'f1-score': 0.7751837177965365, 'support': 27619.0} | {'precision': 0.8980898944155006, 'recall': 0.8974618921756762, 'f1-score': 0.8922755407669418, 'support': 27619.0} |
+| No log        | 2.0   | 82   | 0.2221          | {'precision': 0.7776852622814321, 'recall': 0.8954937679769894, 'f1-score': 0.8324420677361853, 'support': 1043.0} | {'precision': 0.9200997398091935, 'recall': 0.978328530259366, 'f1-score': 0.948321135258953, 'support': 17350.0}   | {'precision': 0.9626097867001254, 'recall': 0.8315629742033384, 'f1-score': 0.8923005350081413, 'support': 9226.0} | 0.9262   | {'precision': 0.8867982629302503, 'recall': 0.9017950908132312, 'f1-score': 0.8910212460010932, 'support': 27619.0} | {'precision': 0.9289219054398928, 'recall': 0.926174010644846, 'f1-score': 0.9252316705665227, 'support': 27619.0}  |
+| No log        | 3.0   | 123  | 0.1732          | {'precision': 0.8459409594095941, 'recall': 0.8791946308724832, 'f1-score': 0.8622472966619651, 'support': 1043.0} | {'precision': 0.963898493817031, 'recall': 0.9479538904899135, 'f1-score': 0.9558597041815592, 'support': 17350.0}  | {'precision': 0.9060388513513513, 'recall': 0.9301972685887708, 'f1-score': 0.9179591400149748, 'support': 9226.0} | 0.9394   | {'precision': 0.9052927681926587, 'recall': 0.9191152633170558, 'f1-score': 0.9120220469528331, 'support': 27619.0} | {'precision': 0.9401162145970984, 'recall': 0.9394257576306166, 'f1-score': 0.9396640292460493, 'support': 27619.0} |
+| No log        | 4.0   | 164  | 0.1893          | {'precision': 0.8392523364485981, 'recall': 0.8609779482262704, 'f1-score': 0.8499763369616659, 'support': 1043.0} | {'precision': 0.9343029364596582, 'recall': 0.9737752161383285, 'f1-score': 0.9536307961504812, 'support': 17350.0} | {'precision': 0.946491849751949, 'recall': 0.8685237372642532, 'f1-score': 0.9058331449242596, 'support': 9226.0}  | 0.9344   | {'precision': 0.9066823742200686, 'recall': 0.9010923005429508, 'f1-score': 0.9031467593454688, 'support': 27619.0} | {'precision': 0.9347851095370013, 'recall': 0.9343567833737645, 'f1-score': 0.9337498181589879, 'support': 27619.0} |
+| No log        | 5.0   | 205  | 0.1928          | {'precision': 0.8462946020128088, 'recall': 0.8868648130393096, 'f1-score': 0.8661048689138576, 'support': 1043.0} | {'precision': 0.9407601426660722, 'recall': 0.9729682997118155, 'f1-score': 0.9565931886439621, 'support': 17350.0} | {'precision': 0.9475646702400373, 'recall': 0.8814220680685021, 'f1-score': 0.91329739442947, 'support': 9226.0}   | 0.9391   | {'precision': 0.9115398049729727, 'recall': 0.9137517269398758, 'f1-score': 0.9119984839957632, 'support': 27619.0} | {'precision': 0.939465780542029, 'recall': 0.9391361019587965, 'f1-score': 0.9387132395183093, 'support': 27619.0}  |
+| No log        | 6.0   | 246  | 0.1784          | {'precision': 0.8283712784588442, 'recall': 0.9069990412272292, 'f1-score': 0.8659038901601832, 'support': 1043.0} | {'precision': 0.9433644229688729, 'recall': 0.9677233429394813, 'f1-score': 0.9553886423125071, 'support': 17350.0} | {'precision': 0.9398548219840995, 'recall': 0.8841318014307392, 'f1-score': 0.9111421390672997, 'support': 9226.0} | 0.9375   | {'precision': 0.9038635078039389, 'recall': 0.9196180618658166, 'f1-score': 0.9108115571799966, 'support': 27619.0} | {'precision': 0.9378494720868902, 'recall': 0.9375067888048083, 'f1-score': 0.9372290117887677, 'support': 27619.0} |
+| No log        | 7.0   | 287  | 0.1897          | {'precision': 0.8537037037037037, 'recall': 0.8839884947267498, 'f1-score': 0.8685821950070655, 'support': 1043.0} | {'precision': 0.9477176070314715, 'recall': 0.96328530259366, 'f1-score': 0.9554380448763755, 'support': 17350.0}   | {'precision': 0.9293575920934412, 'recall': 0.8969217429004986, 'f1-score': 0.9128516271373415, 'support': 9226.0} | 0.9381   | {'precision': 0.9102596342762054, 'recall': 0.9147318467403028, 'f1-score': 0.9122906223402608, 'support': 27619.0} | {'precision': 0.9380342007173714, 'recall': 0.9381223071074261, 'f1-score': 0.9379322357785074, 'support': 27619.0} |
+| No log        | 8.0   | 328  | 0.1994          | {'precision': 0.8458029197080292, 'recall': 0.8887823585810163, 'f1-score': 0.8667601683029453, 'support': 1043.0} | {'precision': 0.941661062542031, 'recall': 0.9684726224783862, 'f1-score': 0.9548786725009946, 'support': 17350.0}  | {'precision': 0.938241732918539, 'recall': 0.8826143507478864, 'f1-score': 0.9095783300753979, 'support': 9226.0}  | 0.9368   | {'precision': 0.9085685717228663, 'recall': 0.9132897772690963, 'f1-score': 0.9104057236264459, 'support': 27619.0} | {'precision': 0.9368988778835639, 'recall': 0.9367826496252579, 'f1-score': 0.9364186066370198, 'support': 27619.0} |
+| No log        | 9.0   | 369  | 0.2009          | {'precision': 0.8471337579617835, 'recall': 0.8926174496644296, 'f1-score': 0.8692810457516339, 'support': 1043.0} | {'precision': 0.9459794744558475, 'recall': 0.9669164265129683, 'f1-score': 0.9563333713373617, 'support': 17350.0} | {'precision': 0.9362622353744594, 'recall': 0.8916106655105137, 'f1-score': 0.9133910726182546, 'support': 9226.0} | 0.9390   | {'precision': 0.9097918225973635, 'recall': 0.9170481805626371, 'f1-score': 0.9130018299024169, 'support': 27619.0} | {'precision': 0.9390006797830427, 'recall': 0.9389550671639089, 'f1-score': 0.9387012621528006, 'support': 27619.0} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

meta_data/meta_s42_e10_cvi0.json

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meta_data/meta_s42_e10_cvi1.json

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meta_data/meta_s42_e10_cvi3.json

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meta_data/meta_s42_e10_cvi4.json

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meta_data/meta_s42_e11_cvi0.json

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meta_data/meta_s42_e11_cvi1.json

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meta_data/meta_s42_e11_cvi2.json

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meta_data/meta_s42_e11_cvi3.json

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meta_data/meta_s42_e11_cvi4.json

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meta_data/meta_s42_e12_cvi0.json

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meta_data/meta_s42_e12_cvi1.json

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meta_data/meta_s42_e12_cvi2.json

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meta_data/meta_s42_e12_cvi3.json

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meta_data/meta_s42_e12_cvi4.json

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meta_data/meta_s42_e13_cvi0.json

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meta_data/meta_s42_e13_cvi1.json

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meta_data/meta_s42_e13_cvi2.json

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meta_data/meta_s42_e13_cvi3.json

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meta_data/meta_s42_e13_cvi4.json

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meta_data/meta_s42_e14_cvi0.json

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meta_data/meta_s42_e14_cvi1.json

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meta_data/meta_s42_e14_cvi2.json

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meta_data/meta_s42_e14_cvi3.json

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meta_data/meta_s42_e14_cvi4.json

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meta_data/meta_s42_e15_cvi0.json

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meta_data/meta_s42_e15_cvi1.json

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meta_data/meta_s42_e15_cvi2.json

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meta_data/meta_s42_e15_cvi3.json

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meta_data/meta_s42_e15_cvi4.json

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meta_data/meta_s42_e16_cvi0.json

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meta_data/meta_s42_e4_cvi0.json

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meta_data/meta_s42_e4_cvi1.json

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meta_data/meta_s42_e4_cvi2.json

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meta_data/meta_s42_e4_cvi3.json

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meta_data/meta_s42_e4_cvi4.json

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meta_data/meta_s42_e5_cvi0.json

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