trainer: training complete at 2024-02-24 18:56:57.371925.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9420975312623168
+      value: 0.9360779676806765
 ---
 
 <!-- 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: 0.1786
+- 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}
 
 ## Model description
 
@@ -63,19 +63,16 @@ 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: 7
+- num_epochs: 4
 
 ### 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   | 0.2836          | {'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} |
 
 
 ### Framework versions

model.safetensors

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+oid sha256:031119bd36583c8375c8fb54e4564b86ebeb57c9711fec789f96a27378b591c9
 size 592318676