trainer: training complete at 2024-02-19 20:26:04.126221.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9400193485972267
+      value: 0.9421333619979219
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,12 +33,12 @@ 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}
+- 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}
 
 ## Model description
 
@@ -63,17 +63,18 @@ 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: 5
+- num_epochs: 6
 
 ### 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} |
+| 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} |
 
 
 ### Framework versions

meta_data/README_s42_e6.md

@@ -0,0 +1,85 @@
+---
+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: test
+      args: spans
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9421333619979219
+---
+
+<!-- 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: 0.1716
+- 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}
+
+## 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: 6
+
+### 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} |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2