trainer: training complete at 2023-11-27 17:15:50.390304.

README.md

@@ -17,17 +17,17 @@ 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 None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4163
-- B-claim: {'precision': 0.5555555555555556, 'recall': 0.551948051948052, 'f1-score': 0.5537459283387622, 'support': 154.0}
-- B-majorclaim: {'precision': 0.6285714285714286, 'recall': 0.6875, 'f1-score': 0.6567164179104478, 'support': 64.0}
-- B-premise: {'precision': 0.7458677685950413, 'recall': 0.8414918414918415, 'f1-score': 0.7907995618838992, 'support': 429.0}
-- I-claim: {'precision': 0.6674840608141246, 'recall': 0.6067766384306732, 'f1-score': 0.6356842596917328, 'support': 2243.0}
-- I-majorclaim: {'precision': 0.7213656387665198, 'recall': 0.7511467889908257, 'f1-score': 0.7359550561797752, 'support': 872.0}
-- I-premise: {'precision': 0.8961770096884001, 'recall': 0.9113300492610837, 'f1-score': 0.903690012542082, 'support': 7511.0}
-- O: {'precision': 0.9139617607825701, 'recall': 0.910117334514058, 'f1-score': 0.9120354963948973, 'support': 4517.0}
-- Accuracy: 0.8526
-- Macro avg: {'precision': 0.7327118889676628, 'recall': 0.751472957805219, 'f1-score': 0.741232390420228, 'support': 15790.0}
-- Weighted avg: {'precision': 0.8506339314975508, 'recall': 0.8525649145028499, 'f1-score': 0.8512623407634757, 'support': 15790.0}
+- Loss: 0.6204
+- B-claim: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0}
+- B-majorclaim: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0}
+- B-premise: {'precision': 0.6069364161849711, 'recall': 0.5526315789473685, 'f1-score': 0.5785123966942148, 'support': 380.0}
+- I-claim: {'precision': 0.5083841463414634, 'recall': 0.3100883310088331, 'f1-score': 0.3852151313889692, 'support': 2151.0}
+- I-majorclaim: {'precision': 0.4941275167785235, 'recall': 0.562559694364852, 'f1-score': 0.5261277355962484, 'support': 1047.0}
+- I-premise: {'precision': 0.8047369129323106, 'recall': 0.9100593516968498, 'f1-score': 0.8541636909012997, 'support': 6571.0}
+- O: {'precision': 0.854797733046707, 'recall': 0.8704477611940299, 'f1-score': 0.862551764937882, 'support': 5025.0}
+- Accuracy: 0.7676
+- Macro avg: {'precision': 0.46699753218342505, 'recall': 0.45796953103027616, 'f1-score': 0.45808153135980195, 'support': 15398.0}
+- Weighted avg: {'precision': 0.7419669119649179, 'recall': 0.7676321600207819, 'f1-score': 0.74985845104923, 'support': 15398.0}
 
 ## Model description
 
@@ -56,11 +56,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | B-claim                                                                                                             | B-majorclaim                                                                                           | B-premise                                                                                                         | I-claim                                                                                                             | I-majorclaim                                                                                                      | I-premise                                                                                                          | O                                                                                                                  | Accuracy | Macro avg                                                                                                           | Weighted avg                                                                                                        |
-|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 196  | 0.4925          | {'precision': 0.4489795918367347, 'recall': 0.14285714285714285, 'f1-score': 0.21674876847290642, 'support': 154.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 64.0}                                    | {'precision': 0.6521739130434783, 'recall': 0.8741258741258742, 'f1-score': 0.7470119521912351, 'support': 429.0} | {'precision': 0.6343843843843844, 'recall': 0.37672759696834596, 'f1-score': 0.4727272727272727, 'support': 2243.0} | {'precision': 0.6209944751381216, 'recall': 0.6444954128440367, 'f1-score': 0.6325267304445695, 'support': 872.0} | {'precision': 0.845458984375, 'recall': 0.92211423245906, 'f1-score': 0.8821244348213716, 'support': 7511.0}       | {'precision': 0.8657378087397086, 'recall': 0.9079034757582466, 'f1-score': 0.8863194294359196, 'support': 4517.0} | 0.8126   | {'precision': 0.5811041653596325, 'recall': 0.5526033907161009, 'f1-score': 0.5482083697276107, 'support': 15790.0} | {'precision': 0.7963354614345164, 'recall': 0.8126029132362255, 'f1-score': 0.7976491141364899, 'support': 15790.0} |
-| No log        | 2.0   | 392  | 0.4278          | {'precision': 0.5454545454545454, 'recall': 0.5064935064935064, 'f1-score': 0.5252525252525252, 'support': 154.0}   | {'precision': 0.7090909090909091, 'recall': 0.609375, 'f1-score': 0.6554621848739497, 'support': 64.0} | {'precision': 0.6920222634508348, 'recall': 0.8694638694638694, 'f1-score': 0.7706611570247933, 'support': 429.0} | {'precision': 0.6465288818229995, 'recall': 0.5439144003566652, 'f1-score': 0.5907990314769976, 'support': 2243.0}  | {'precision': 0.7232558139534884, 'recall': 0.713302752293578, 'f1-score': 0.7182448036951501, 'support': 872.0}  | {'precision': 0.8695761223977928, 'recall': 0.9231793369724404, 'f1-score': 0.8955763642234421, 'support': 7511.0} | {'precision': 0.9275161588180979, 'recall': 0.8895284480850122, 'f1-score': 0.9081252118883489, 'support': 4517.0} | 0.8413   | {'precision': 0.7304920992840954, 'recall': 0.7221796162378674, 'f1-score': 0.7234458969193153, 'support': 15790.0} | {'precision': 0.8377504411405843, 'recall': 0.8412919569347689, 'f1-score': 0.8381000288341659, 'support': 15790.0} |
-| 0.598         | 3.0   | 588  | 0.4163          | {'precision': 0.5555555555555556, 'recall': 0.551948051948052, 'f1-score': 0.5537459283387622, 'support': 154.0}    | {'precision': 0.6285714285714286, 'recall': 0.6875, 'f1-score': 0.6567164179104478, 'support': 64.0}   | {'precision': 0.7458677685950413, 'recall': 0.8414918414918415, 'f1-score': 0.7907995618838992, 'support': 429.0} | {'precision': 0.6674840608141246, 'recall': 0.6067766384306732, 'f1-score': 0.6356842596917328, 'support': 2243.0}  | {'precision': 0.7213656387665198, 'recall': 0.7511467889908257, 'f1-score': 0.7359550561797752, 'support': 872.0} | {'precision': 0.8961770096884001, 'recall': 0.9113300492610837, 'f1-score': 0.903690012542082, 'support': 7511.0}  | {'precision': 0.9139617607825701, 'recall': 0.910117334514058, 'f1-score': 0.9120354963948973, 'support': 4517.0}  | 0.8526   | {'precision': 0.7327118889676628, 'recall': 0.751472957805219, 'f1-score': 0.741232390420228, 'support': 15790.0}   | {'precision': 0.8506339314975508, 'recall': 0.8525649145028499, 'f1-score': 0.8512623407634757, 'support': 15790.0} |
+| Training Loss | Epoch | Step | Validation Loss | B-claim                                                              | B-majorclaim                                                        | B-premise                                                                                                          | I-claim                                                                                                              | I-majorclaim                                                                                                        | I-premise                                                                                                          | O                                                                                                                  | Accuracy | Macro avg                                                                                                              | Weighted avg                                                                                                        |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------:|:-------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:--------:|:----------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 36   | 0.8375          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 380.0}                                               | {'precision': 0.3307174887892377, 'recall': 0.13714551371455136, 'f1-score': 0.1938876109102859, 'support': 2151.0}  | {'precision': 0.25, 'recall': 0.0009551098376313276, 'f1-score': 0.0019029495718363464, 'support': 1047.0}          | {'precision': 0.6899198931909212, 'recall': 0.9436919799117334, 'f1-score': 0.7970949289800116, 'support': 6571.0} | {'precision': 0.7767500906782735, 'recall': 0.8523383084577114, 'f1-score': 0.8127905873422525, 'support': 5025.0} | 0.7001   | {'precision': 0.29248392466549034, 'recall': 0.2763044159888039, 'f1-score': 0.25795372525776944, 'support': 15398.0}  | {'precision': 0.6111024900767319, 'recall': 0.7000909208988181, 'f1-score': 0.6326164514217569, 'support': 15398.0} |
+| No log        | 2.0   | 72   | 0.6930          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0} | {'precision': 0.6489795918367347, 'recall': 0.41842105263157897, 'f1-score': 0.5087999999999999, 'support': 380.0} | {'precision': 0.4376956793988729, 'recall': 0.32496513249651326, 'f1-score': 0.37299893276414087, 'support': 2151.0} | {'precision': 0.3946384039900249, 'recall': 0.6045845272206304, 'f1-score': 0.47755563938136547, 'support': 1047.0} | {'precision': 0.83792191631669, 'recall': 0.8198143357175468, 'f1-score': 0.8287692307692307, 'support': 6571.0}   | {'precision': 0.8073510773130546, 'recall': 0.887363184079602, 'f1-score': 0.8454683352294274, 'support': 5025.0}  | 0.7363   | {'precision': 0.446655238407911, 'recall': 0.4364497474494102, 'f1-score': 0.4333703054491663, 'support': 15398.0}     | {'precision': 0.7250426117598104, 'recall': 0.7362644499285621, 'f1-score': 0.7267168761345917, 'support': 15398.0} |
+| No log        | 3.0   | 108  | 0.6204          | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 147.0} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 77.0} | {'precision': 0.6069364161849711, 'recall': 0.5526315789473685, 'f1-score': 0.5785123966942148, 'support': 380.0}  | {'precision': 0.5083841463414634, 'recall': 0.3100883310088331, 'f1-score': 0.3852151313889692, 'support': 2151.0}   | {'precision': 0.4941275167785235, 'recall': 0.562559694364852, 'f1-score': 0.5261277355962484, 'support': 1047.0}   | {'precision': 0.8047369129323106, 'recall': 0.9100593516968498, 'f1-score': 0.8541636909012997, 'support': 6571.0} | {'precision': 0.854797733046707, 'recall': 0.8704477611940299, 'f1-score': 0.862551764937882, 'support': 5025.0}   | 0.7676   | {'precision': 0.46699753218342505, 'recall': 0.45796953103027616, 'f1-score': 0.45808153135980195, 'support': 15398.0} | {'precision': 0.7419669119649179, 'recall': 0.7676321600207819, 'f1-score': 0.74985845104923, 'support': 15398.0}   |
 
 
 ### Framework versions

pytorch_model.bin

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