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--- |
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license: apache-2.0 |
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base_model: allenai/longformer-base-4096 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: longformer-one-step |
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results: [] |
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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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should probably proofread and complete it, then remove this comment. --> |
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# longformer-one-step |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5640 |
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- Claim: {'precision': 0.5519765739385066, 'recall': 0.32811140121845084, 'f1-score': 0.41157205240174677, 'support': 2298.0} |
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- Majorclaim: {'precision': 0.5541490857946554, 'recall': 0.701067615658363, 'f1-score': 0.6190102120974077, 'support': 1124.0} |
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- O: {'precision': 0.8899137758171245, 'recall': 0.8831840796019901, 'f1-score': 0.8865361566120655, 'support': 5025.0} |
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- Premise: {'precision': 0.830119375573921, 'recall': 0.9103726082578046, 'f1-score': 0.8683957732949088, 'support': 6951.0} |
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- Accuracy: 0.7993 |
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- Macro avg: {'precision': 0.7065397027810518, 'recall': 0.7056839261841521, 'f1-score': 0.6963785486015321, 'support': 15398.0} |
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- Weighted avg: {'precision': 0.7879778050681424, 'recall': 0.7993245876087803, 'f1-score': 0.7879350085702844, 'support': 15398.0} |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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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 | 36 | 0.7525 | {'precision': 0.41766381766381766, 'recall': 0.31897302001740646, 'f1-score': 0.36170737725141877, 'support': 2298.0} | {'precision': 0.43548387096774194, 'recall': 0.02402135231316726, 'f1-score': 0.045531197301854974, 'support': 1124.0} | {'precision': 0.7476681394207167, 'recall': 0.9092537313432836, 'f1-score': 0.8205818965517241, 'support': 5025.0} | {'precision': 0.8187416331994646, 'recall': 0.8798733995108617, 'f1-score': 0.8482074752097636, 'support': 6951.0} | 0.7433 | {'precision': 0.6048893653129352, 'recall': 0.5330303757961797, 'f1-score': 0.5190069865786904, 'support': 15398.0} | {'precision': 0.707714041883217, 'recall': 0.7432783478373814, 'f1-score': 0.7079942076273884, 'support': 15398.0} | |
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| No log | 2.0 | 72 | 0.6577 | {'precision': 0.4793814432989691, 'recall': 0.3237597911227154, 'f1-score': 0.38649350649350644, 'support': 2298.0} | {'precision': 0.41677503250975295, 'recall': 0.5702846975088968, 'f1-score': 0.48159278737791134, 'support': 1124.0} | {'precision': 0.7966573816155988, 'recall': 0.9106467661691542, 'f1-score': 0.849846782431052, 'support': 5025.0} | {'precision': 0.8743144424131627, 'recall': 0.8256365990504964, 'f1-score': 0.8492785793562707, 'support': 6951.0} | 0.7598 | {'precision': 0.6417820749593709, 'recall': 0.6575819634628157, 'f1-score': 0.6418029139146851, 'support': 15398.0} | {'precision': 0.7566331163186304, 'recall': 0.7598389401220937, 'f1-score': 0.7535581151939423, 'support': 15398.0} | |
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| No log | 3.0 | 108 | 0.5640 | {'precision': 0.5519765739385066, 'recall': 0.32811140121845084, 'f1-score': 0.41157205240174677, 'support': 2298.0} | {'precision': 0.5541490857946554, 'recall': 0.701067615658363, 'f1-score': 0.6190102120974077, 'support': 1124.0} | {'precision': 0.8899137758171245, 'recall': 0.8831840796019901, 'f1-score': 0.8865361566120655, 'support': 5025.0} | {'precision': 0.830119375573921, 'recall': 0.9103726082578046, 'f1-score': 0.8683957732949088, 'support': 6951.0} | 0.7993 | {'precision': 0.7065397027810518, 'recall': 0.7056839261841521, 'f1-score': 0.6963785486015321, 'support': 15398.0} | {'precision': 0.7879778050681424, 'recall': 0.7993245876087803, 'f1-score': 0.7879350085702844, 'support': 15398.0} | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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