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--- |
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license: mit |
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base_model: SCUT-DLVCLab/lilt-roberta-en-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Data_extraction |
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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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# Data_extraction |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4277 |
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- Fsc Code: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} |
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- Ame: {'precision': 0.391304347826087, 'recall': 0.42857142857142855, 'f1': 0.4090909090909091, 'number': 42} |
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- Ccount No: {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} |
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- Ign: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} |
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- Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} |
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- Ther: {'precision': 0.5287356321839081, 'recall': 0.5348837209302325, 'f1': 0.5317919075144507, 'number': 86} |
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- Overall Precision: 0.6045 |
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- Overall Recall: 0.6149 |
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- Overall F1: 0.6097 |
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- Overall Accuracy: 0.9431 |
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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: 5e-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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- training_steps: 2500 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Fsc Code | Ame | Ccount No | Ign | Mount | Ther | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.1559 | 20.0 | 200 | 0.2349 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3448275862068966, 'recall': 0.47619047619047616, 'f1': 0.39999999999999997, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4329896907216495, 'recall': 0.4883720930232558, 'f1': 0.45901639344262296, 'number': 86} | 0.5155 | 0.5747 | 0.5435 | 0.9376 | |
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| 0.0138 | 40.0 | 400 | 0.2607 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3148148148148148, 'recall': 0.40476190476190477, 'f1': 0.3541666666666667, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 0.8, 'f1': 0.888888888888889, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5, 'recall': 0.5465116279069767, 'f1': 0.5222222222222221, 'number': 86} | 0.5550 | 0.6092 | 0.5808 | 0.9372 | |
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| 0.0031 | 60.0 | 600 | 0.3808 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.2786885245901639, 'recall': 0.40476190476190477, 'f1': 0.33009708737864074, 'number': 42} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4077669902912621, 'recall': 0.4883720930232558, 'f1': 0.44444444444444436, 'number': 86} | 0.4928 | 0.5920 | 0.5379 | 0.9372 | |
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| 0.0031 | 80.0 | 800 | 0.3239 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.2807017543859649, 'recall': 0.38095238095238093, 'f1': 0.32323232323232326, 'number': 42} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.45, 'recall': 0.5232558139534884, 'f1': 0.48387096774193555, 'number': 86} | 0.5248 | 0.6092 | 0.5638 | 0.9532 | |
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| 0.0007 | 100.0 | 1000 | 0.3718 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.375, 'recall': 0.42857142857142855, 'f1': 0.39999999999999997, 'number': 42} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4891304347826087, 'recall': 0.5232558139534884, 'f1': 0.5056179775280899, 'number': 86} | 0.5722 | 0.6149 | 0.5928 | 0.9467 | |
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| 0.0002 | 120.0 | 1200 | 0.4208 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.34, 'recall': 0.40476190476190477, 'f1': 0.36956521739130443, 'number': 42} | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4731182795698925, 'recall': 0.5116279069767442, 'f1': 0.4916201117318436, 'number': 86} | 0.5474 | 0.5977 | 0.5714 | 0.9408 | |
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| 0.0003 | 140.0 | 1400 | 0.4155 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3333333333333333, 'recall': 0.40476190476190477, 'f1': 0.3655913978494623, 'number': 42} | {'precision': 0.8, 'recall': 0.6666666666666666, 'f1': 0.7272727272727272, 'number': 6} | {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.46808510638297873, 'recall': 0.5116279069767442, 'f1': 0.4888888888888889, 'number': 86} | 0.5497 | 0.6034 | 0.5753 | 0.9397 | |
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| 0.0004 | 160.0 | 1600 | 0.4277 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.391304347826087, 'recall': 0.42857142857142855, 'f1': 0.4090909090909091, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5287356321839081, 'recall': 0.5348837209302325, 'f1': 0.5317919075144507, 'number': 86} | 0.6045 | 0.6149 | 0.6097 | 0.9431 | |
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| 0.0001 | 180.0 | 1800 | 0.3870 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.27586206896551724, 'recall': 0.38095238095238093, 'f1': 0.32, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.45, 'recall': 0.5232558139534884, 'f1': 0.48387096774193555, 'number': 86} | 0.5149 | 0.5977 | 0.5532 | 0.9476 | |
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| 0.0001 | 200.0 | 2000 | 0.3956 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3617021276595745, 'recall': 0.40476190476190477, 'f1': 0.3820224719101123, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5056179775280899, 'recall': 0.5232558139534884, 'f1': 0.5142857142857142, 'number': 86} | 0.5833 | 0.6034 | 0.5932 | 0.9526 | |
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| 0.0001 | 220.0 | 2200 | 0.4029 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.3469387755102041, 'recall': 0.40476190476190477, 'f1': 0.3736263736263736, 'number': 42} | {'precision': 0.6, 'recall': 0.5, 'f1': 0.5454545454545454, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.5, 'recall': 0.5348837209302325, 'f1': 0.5168539325842696, 'number': 86} | 0.5699 | 0.6092 | 0.5889 | 0.9508 | |
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| 0.0 | 240.0 | 2400 | 0.4031 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} | {'precision': 0.34, 'recall': 0.40476190476190477, 'f1': 0.36956521739130443, 'number': 42} | {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 0.4891304347826087, 'recall': 0.5232558139534884, 'f1': 0.5056179775280899, 'number': 86} | 0.5645 | 0.6034 | 0.5833 | 0.9499 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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