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Theoreticallyhugo
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longformer-sep_tok

Token Classification
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Safetensors
longformer
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Eval Results
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Theoreticallyhugo commited on
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1 Parent(s): 33a214d

trainer: training complete at 2024-02-19 18:52:04.900427.

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  1. README.md +16 -16
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8870016984713758
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  ---
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  <!-- 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. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2615
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- - Claim: {'precision': 0.6071779744346116, 'recall': 0.5809031044214488, 'f1-score': 0.5937500000000001, 'support': 4252.0}
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- - Majorclaim: {'precision': 0.8395117540687161, 'recall': 0.8510540788267644, 'f1-score': 0.8452435138825672, 'support': 2182.0}
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- - O: {'precision': 1.0, 'recall': 0.9992978144474678, 'f1-score': 0.9996487839143033, 'support': 11393.0}
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- - Premise: {'precision': 0.8835139944992719, 'recall': 0.8952459016393443, 'f1-score': 0.8893412588551421, 'support': 12200.0}
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- - Accuracy: 0.8870
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- - Macro avg: {'precision': 0.83255093075065, 'recall': 0.8316252248337562, 'f1-score': 0.8319958891630032, 'support': 30027.0}
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- - Weighted avg: {'precision': 0.8853833592288615, 'recall': 0.8870016984713758, 'f1-score': 0.8861327572005248, 'support': 30027.0}
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  ## Model description
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@@ -68,13 +68,13 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
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- |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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- | No log | 1.0 | 41 | 0.3496 | {'precision': 0.514827018121911, 'recall': 0.2939793038570085, 'f1-score': 0.37425149700598803, 'support': 4252.0} | {'precision': 0.6678657074340527, 'recall': 0.7658111824014665, 'f1-score': 0.7134927412467975, 'support': 2182.0} | {'precision': 0.996649620878152, 'recall': 0.9921881857280787, 'f1-score': 0.9944138992742467, 'support': 11393.0} | {'precision': 0.826608505997819, 'recall': 0.9319672131147541, 'f1-score': 0.8761317665189751, 'support': 12200.0} | 0.8524 | {'precision': 0.7514877131079837, 'recall': 0.7459864712753269, 'f1-score': 0.7395724760115018, 'support': 30027.0} | {'precision': 0.8354407819133993, 'recall': 0.8523995071102675, 'f1-score': 0.8381231435918661, 'support': 30027.0} |
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- | No log | 2.0 | 82 | 0.2859 | {'precision': 0.5445935280189423, 'recall': 0.486829727187206, 'f1-score': 0.514094126412517, 'support': 4252.0} | {'precision': 0.8543130990415335, 'recall': 0.6127406049495875, 'f1-score': 0.7136375767280491, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.997366804178004, 'f1-score': 0.9986816663737036, 'support': 11393.0} | {'precision': 0.851030230109791, 'recall': 0.9276229508196722, 'f1-score': 0.8876774648992078, 'support': 12200.0} | 0.8688 | {'precision': 0.8124842142925667, 'recall': 0.7561400217836175, 'f1-score': 0.7785227086033695, 'support': 30027.0} | {'precision': 0.8643984304320985, 'recall': 0.8687847603823226, 'f1-score': 0.8642465352746717, 'support': 30027.0} |
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- | No log | 3.0 | 123 | 0.2615 | {'precision': 0.617028164454516, 'recall': 0.4482596425211665, 'f1-score': 0.5192753030922217, 'support': 4252.0} | {'precision': 0.8376436781609196, 'recall': 0.8015582034830431, 'f1-score': 0.8192037470725996, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9988589484771351, 'f1-score': 0.9994291485531112, 'support': 11393.0} | {'precision': 0.8495916852264291, 'recall': 0.9380327868852459, 'f1-score': 0.8916244643552784, 'support': 12200.0} | 0.8818 | {'precision': 0.8260658819604662, 'recall': 0.7966773953416476, 'f1-score': 0.8073831657683027, 'support': 30027.0} | {'precision': 0.872859786884143, 'recall': 0.8818396776234723, 'f1-score': 0.874538779080845, 'support': 30027.0} |
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- | No log | 4.0 | 164 | 0.2591 | {'precision': 0.6161943319838057, 'recall': 0.5369238005644402, 'f1-score': 0.5738343596832978, 'support': 4252.0} | {'precision': 0.860332541567696, 'recall': 0.8299725022914757, 'f1-score': 0.8448798693725216, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9992100412534012, 'f1-score': 0.9996048645563507, 'support': 11393.0} | {'precision': 0.8684641159510637, 'recall': 0.9135245901639344, 'f1-score': 0.8904246394758917, 'support': 12200.0} | 0.8866 | {'precision': 0.8362477473756413, 'recall': 0.8199077335683129, 'f1-score': 0.8271859332720154, 'support': 30027.0} | {'precision': 0.8820583514802954, 'recall': 0.8866353615079762, 'f1-score': 0.8837096744876479, 'support': 30027.0} |
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- | No log | 5.0 | 205 | 0.2615 | {'precision': 0.6071779744346116, 'recall': 0.5809031044214488, 'f1-score': 0.5937500000000001, 'support': 4252.0} | {'precision': 0.8395117540687161, 'recall': 0.8510540788267644, 'f1-score': 0.8452435138825672, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9992978144474678, 'f1-score': 0.9996487839143033, 'support': 11393.0} | {'precision': 0.8835139944992719, 'recall': 0.8952459016393443, 'f1-score': 0.8893412588551421, 'support': 12200.0} | 0.8870 | {'precision': 0.83255093075065, 'recall': 0.8316252248337562, 'f1-score': 0.8319958891630032, 'support': 30027.0} | {'precision': 0.8853833592288615, 'recall': 0.8870016984713758, 'f1-score': 0.8861327572005248, 'support': 30027.0} |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8921304159589702
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2544
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+ - Claim: {'precision': 0.618502404396611, 'recall': 0.6352304797742239, 'f1-score': 0.6267548439494142, 'support': 4252.0}
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+ - Majorclaim: {'precision': 0.8701787394167451, 'recall': 0.847846012832264, 'f1-score': 0.8588672237697308, 'support': 2182.0}
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+ - O: {'precision': 1.0, 'recall': 0.9994733608356008, 'f1-score': 0.9997366110623354, 'support': 11393.0}
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+ - Premise: {'precision': 0.8932246645262205, 'recall': 0.889344262295082, 'f1-score': 0.8912802398652812, 'support': 12200.0}
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+ - Accuracy: 0.8921
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+ - Macro avg: {'precision': 0.8454764520848941, 'recall': 0.8429735289342927, 'f1-score': 0.8441597296616904, 'support': 30027.0}
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+ - Weighted avg: {'precision': 0.8931609265035342, 'recall': 0.8921304159589702, 'f1-score': 0.8926175780107263, 'support': 30027.0}
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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+ | No log | 1.0 | 41 | 0.3491 | {'precision': 0.5206252713851498, 'recall': 0.28198494825964254, 'f1-score': 0.3658276125095347, 'support': 4252.0} | {'precision': 0.626488632262721, 'recall': 0.7956003666361137, 'f1-score': 0.7009892994144962, 'support': 2182.0} | {'precision': 0.9948045086297992, 'recall': 0.9915737733696129, 'f1-score': 0.9931865136929096, 'support': 11393.0} | {'precision': 0.8307714937118482, 'recall': 0.9259016393442623, 'f1-score': 0.8757607473737257, 'support': 12200.0} | 0.8502 | {'precision': 0.7431724764973796, 'recall': 0.7487651819024079, 'f1-score': 0.7339410432476665, 'support': 30027.0} | {'precision': 0.8342464062220922, 'recall': 0.8501681819695607, 'f1-score': 0.8354052262355796, 'support': 30027.0} |
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+ | No log | 2.0 | 82 | 0.2801 | {'precision': 0.5533096401599289, 'recall': 0.5858419567262465, 'f1-score': 0.5691112634224355, 'support': 4252.0} | {'precision': 0.7518549051937345, 'recall': 0.8359303391384051, 'f1-score': 0.7916666666666666, 'support': 2182.0} | {'precision': 0.9982446901878181, 'recall': 0.9983323093127359, 'f1-score': 0.9982884978277087, 'support': 11393.0} | {'precision': 0.8966253737718923, 'recall': 0.8602459016393442, 'f1-score': 0.8780589834762602, 'support': 12200.0} | 0.8720 | {'precision': 0.8000086523283435, 'recall': 0.820087626704183, 'f1-score': 0.8092813528482677, 'support': 30027.0} | {'precision': 0.8760466016724828, 'recall': 0.8720151863323009, 'f1-score': 0.8736503218070512, 'support': 30027.0} |
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+ | No log | 3.0 | 123 | 0.2615 | {'precision': 0.6233933161953727, 'recall': 0.45625587958607716, 'f1-score': 0.5268875611080934, 'support': 4252.0} | {'precision': 0.8267020335985853, 'recall': 0.8570119156736938, 'f1-score': 0.8415841584158417, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9987711752830686, 'f1-score': 0.9993852099069033, 'support': 11393.0} | {'precision': 0.8530962784390538, 'recall': 0.9281967213114755, 'f1-score': 0.8890633587186937, 'support': 12200.0} | 0.8830 | {'precision': 0.825797907058253, 'recall': 0.8100589229635788, 'f1-score': 0.814230072037383, 'support': 30027.0} | {'precision': 0.8743899428757883, 'recall': 0.8829719918739801, 'f1-score': 0.8761858066517598, 'support': 30027.0} |
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+ | No log | 4.0 | 164 | 0.2551 | {'precision': 0.6193501099438065, 'recall': 0.5961900282220132, 'f1-score': 0.6075494307968844, 'support': 4252.0} | {'precision': 0.8261056247316445, 'recall': 0.8817598533455545, 'f1-score': 0.8530259365994236, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9994733608356008, 'f1-score': 0.9997366110623354, 'support': 11393.0} | {'precision': 0.8906531347192667, 'recall': 0.8919672131147541, 'f1-score': 0.8913096895732656, 'support': 12200.0} | 0.8901 | {'precision': 0.8340272173486795, 'recall': 0.8423476138794807, 'f1-score': 0.8379054170079773, 'support': 30027.0} | {'precision': 0.8890334493695863, 'recall': 0.890132214340427, 'f1-score': 0.88948546961186, 'support': 30027.0} |
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+ | No log | 5.0 | 205 | 0.2544 | {'precision': 0.618502404396611, 'recall': 0.6352304797742239, 'f1-score': 0.6267548439494142, 'support': 4252.0} | {'precision': 0.8701787394167451, 'recall': 0.847846012832264, 'f1-score': 0.8588672237697308, 'support': 2182.0} | {'precision': 1.0, 'recall': 0.9994733608356008, 'f1-score': 0.9997366110623354, 'support': 11393.0} | {'precision': 0.8932246645262205, 'recall': 0.889344262295082, 'f1-score': 0.8912802398652812, 'support': 12200.0} | 0.8921 | {'precision': 0.8454764520848941, 'recall': 0.8429735289342927, 'f1-score': 0.8441597296616904, 'support': 30027.0} | {'precision': 0.8931609265035342, 'recall': 0.8921304159589702, 'f1-score': 0.8926175780107263, 'support': 30027.0} |
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  ### Framework versions
model.safetensors CHANGED
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