trainer: training complete at 2024-02-18 17:04:38.925963.

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8226736894908453
+      value: 0.835142785481386
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,14 +32,14 @@ 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 fancy_dataset dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4587
-- Claim: {'precision': 0.5772692208794035, 'recall': 0.5279868297271872, 'f1-score': 0.5515292961552635, 'support': 4252.0}
-- Majorclaim: {'precision': 0.6656682890303257, 'recall': 0.8148487626031164, 'f1-score': 0.7327426334226252, 'support': 2182.0}
-- O: {'precision': 0.9301160937855679, 'recall': 0.8810781671159029, 'f1-score': 0.9049332816566081, 'support': 9275.0}
-- Premise: {'precision': 0.8568813181564913, 'recall': 0.8823770491803279, 'f1-score': 0.8694423131284578, 'support': 12200.0}
-- Accuracy: 0.8227
-- Macro avg: {'precision': 0.757483730462947, 'recall': 0.7765727021566337, 'f1-score': 0.7646618810907386, 'support': 27909.0}
-- Weighted avg: {'precision': 0.8236703495364839, 'recall': 0.8226736894908453, 'f1-score': 0.8221147085496641, 'support': 27909.0}
+- Loss: 0.4315
+- Claim: {'precision': 0.5943734015345269, 'recall': 0.5465663217309501, 'f1-score': 0.5694682675814751, 'support': 4252.0}
+- Majorclaim: {'precision': 0.7267513314215486, 'recall': 0.8130155820348305, 'f1-score': 0.7674670127622755, 'support': 2182.0}
+- O: {'precision': 0.934245960502693, 'recall': 0.8976819407008086, 'f1-score': 0.9155990542695331, 'support': 9275.0}
+- Premise: {'precision': 0.8606674047129527, 'recall': 0.8921311475409837, 'f1-score': 0.876116879980681, 'support': 12200.0}
+- Accuracy: 0.8351
+- Macro avg: {'precision': 0.7790095245429304, 'recall': 0.7873487480018933, 'f1-score': 0.7821628036484911, 'support': 27909.0}
+- Weighted avg: {'precision': 0.8340793553924228, 'recall': 0.835142785481386, 'f1-score': 0.8340248400056594, 'support': 27909.0}
 
 ## Model description
 
@@ -64,20 +64,22 @@ 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: 3
+- num_epochs: 5
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 41   | 0.5844          | {'precision': 0.4909433962264151, 'recall': 0.30597365945437444, 'f1-score': 0.37699217618081715, 'support': 4252.0} | {'precision': 0.5969423210562891, 'recall': 0.3936755270394134, 'f1-score': 0.4744545705606187, 'support': 2182.0} | {'precision': 0.825323567773653, 'recall': 0.886900269541779, 'f1-score': 0.8550046772684753, 'support': 9275.0}   | {'precision': 0.8012704829278856, 'recall': 0.9098360655737705, 'f1-score': 0.8521091620926572, 'support': 12200.0} | 0.7699   | {'precision': 0.6786199419960607, 'recall': 0.6240963804023343, 'f1-score': 0.639640146525642, 'support': 27909.0}  | {'precision': 0.7460100844931877, 'recall': 0.7698591852090724, 'f1-score': 0.7511602266394221, 'support': 27909.0} |
-| No log        | 2.0   | 82   | 0.4763          | {'precision': 0.5736620565243535, 'recall': 0.4487300094073377, 'f1-score': 0.5035629453681709, 'support': 4252.0}   | {'precision': 0.7095454545454546, 'recall': 0.7153987167736022, 'f1-score': 0.7124600638977636, 'support': 2182.0} | {'precision': 0.8935006435006435, 'recall': 0.8982210242587602, 'f1-score': 0.8958546158395613, 'support': 9275.0} | {'precision': 0.8362814916915537, 'recall': 0.8951639344262295, 'f1-score': 0.8647214854111407, 'support': 12200.0} | 0.8141   | {'precision': 0.7532474115655013, 'recall': 0.7393784212164823, 'f1-score': 0.7441497776291592, 'support': 27909.0} | {'precision': 0.8053779036606528, 'recall': 0.8141101436812498, 'f1-score': 0.80814042735527, 'support': 27909.0}   |
-| No log        | 3.0   | 123  | 0.4587          | {'precision': 0.5772692208794035, 'recall': 0.5279868297271872, 'f1-score': 0.5515292961552635, 'support': 4252.0}   | {'precision': 0.6656682890303257, 'recall': 0.8148487626031164, 'f1-score': 0.7327426334226252, 'support': 2182.0} | {'precision': 0.9301160937855679, 'recall': 0.8810781671159029, 'f1-score': 0.9049332816566081, 'support': 9275.0} | {'precision': 0.8568813181564913, 'recall': 0.8823770491803279, 'f1-score': 0.8694423131284578, 'support': 12200.0} | 0.8227   | {'precision': 0.757483730462947, 'recall': 0.7765727021566337, 'f1-score': 0.7646618810907386, 'support': 27909.0}  | {'precision': 0.8236703495364839, 'recall': 0.8226736894908453, 'f1-score': 0.8221147085496641, 'support': 27909.0} |
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                               | Majorclaim                                                                                                         | O                                                                                                                  | Premise                                                                                                             | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 41   | 0.5743          | {'precision': 0.5082508250825083, 'recall': 0.2535277516462841, 'f1-score': 0.33830221245881065, 'support': 4252.0} | {'precision': 0.5805350028457599, 'recall': 0.4674610449129239, 'f1-score': 0.5178979436405179, 'support': 2182.0} | {'precision': 0.8466549477820288, 'recall': 0.8828032345013477, 'f1-score': 0.8643513142615855, 'support': 9275.0} | {'precision': 0.7886490250696379, 'recall': 0.9282786885245902, 'f1-score': 0.8527861445783133, 'support': 12200.0} | 0.7743   | {'precision': 0.6810224501949838, 'recall': 0.6330176798962865, 'f1-score': 0.6433344037348069, 'support': 27909.0} | {'precision': 0.7489359214227731, 'recall': 0.7743380271596976, 'f1-score': 0.7520643421129422, 'support': 27909.0} |
+| No log        | 2.0   | 82   | 0.4563          | {'precision': 0.5752391997680487, 'recall': 0.4666039510818438, 'f1-score': 0.5152577587326321, 'support': 4252.0}  | {'precision': 0.7043734230445753, 'recall': 0.7676443629697525, 'f1-score': 0.7346491228070176, 'support': 2182.0} | {'precision': 0.9195569478630566, 'recall': 0.8861455525606469, 'f1-score': 0.9025421402295064, 'support': 9275.0} | {'precision': 0.8371119902617163, 'recall': 0.9018852459016393, 'f1-score': 0.868292297979798, 'support': 12200.0}  | 0.8198   | {'precision': 0.7590703902343492, 'recall': 0.7555697781284707, 'f1-score': 0.7551853299372385, 'support': 27909.0} | {'precision': 0.8142361553305312, 'recall': 0.8198430613780501, 'f1-score': 0.8154403512156749, 'support': 27909.0} |
+| No log        | 3.0   | 123  | 0.4417          | {'precision': 0.6114437791084497, 'recall': 0.43226716839134527, 'f1-score': 0.5064756131165611, 'support': 4252.0} | {'precision': 0.6908951798010712, 'recall': 0.8276810265811182, 'f1-score': 0.7531276063386154, 'support': 2182.0} | {'precision': 0.9402591445935099, 'recall': 0.8840970350404312, 'f1-score': 0.9113136252500555, 'support': 9275.0} | {'precision': 0.827903891509434, 'recall': 0.9207377049180328, 'f1-score': 0.8718565662837628, 'support': 12200.0}  | 0.8269   | {'precision': 0.7676254987531161, 'recall': 0.7661957337327319, 'f1-score': 0.7606933527472487, 'support': 27909.0} | {'precision': 0.821553021377153, 'recall': 0.8268658855566304, 'f1-score': 0.8200201629172901, 'support': 27909.0}  |
+| No log        | 4.0   | 164  | 0.4382          | {'precision': 0.5850725952813067, 'recall': 0.6065380997177798, 'f1-score': 0.5956120092378753, 'support': 4252.0}  | {'precision': 0.6956022944550669, 'recall': 0.8336388634280477, 'f1-score': 0.7583906608296852, 'support': 2182.0} | {'precision': 0.9404094704334897, 'recall': 0.8864690026954178, 'f1-score': 0.9126429126429128, 'support': 9275.0} | {'precision': 0.8778720250349996, 'recall': 0.8737704918032787, 'f1-score': 0.8758164564761943, 'support': 12200.0} | 0.8341   | {'precision': 0.7747390963012157, 'recall': 0.8001041144111309, 'f1-score': 0.7856155097966668, 'support': 27909.0} | {'precision': 0.8397961025237265, 'recall': 0.8341395248844459, 'f1-score': 0.8361845450923504, 'support': 27909.0} |
+| No log        | 5.0   | 205  | 0.4315          | {'precision': 0.5943734015345269, 'recall': 0.5465663217309501, 'f1-score': 0.5694682675814751, 'support': 4252.0}  | {'precision': 0.7267513314215486, 'recall': 0.8130155820348305, 'f1-score': 0.7674670127622755, 'support': 2182.0} | {'precision': 0.934245960502693, 'recall': 0.8976819407008086, 'f1-score': 0.9155990542695331, 'support': 9275.0}  | {'precision': 0.8606674047129527, 'recall': 0.8921311475409837, 'f1-score': 0.876116879980681, 'support': 12200.0}  | 0.8351   | {'precision': 0.7790095245429304, 'recall': 0.7873487480018933, 'f1-score': 0.7821628036484911, 'support': 27909.0} | {'precision': 0.8340793553924228, 'recall': 0.835142785481386, 'f1-score': 0.8340248400056594, 'support': 27909.0}  |
 
 
 ### Framework versions
 
-- Transformers 4.37.1
-- Pytorch 2.1.2+cu121
-- Datasets 2.16.1
-- Tokenizers 0.15.1
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2