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--- |
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license: mit |
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base_model: deepset/gbert-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: gerskill-gbert |
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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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# gerskill-gbert |
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This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1095 |
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- Hard: {'precision': 0.7445544554455445, 'recall': 0.8245614035087719, 'f1': 0.7825182101977106, 'number': 456} |
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- Soft: {'precision': 0.7272727272727273, 'recall': 0.7804878048780488, 'f1': 0.7529411764705882, 'number': 82} |
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- Overall Precision: 0.7420 |
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- Overall Recall: 0.8178 |
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- Overall F1: 0.7781 |
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- Overall Accuracy: 0.9635 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Hard | Soft | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| No log | 1.0 | 178 | 0.1255 | {'precision': 0.583756345177665, 'recall': 0.756578947368421, 'f1': 0.659025787965616, 'number': 456} | {'precision': 0.5425531914893617, 'recall': 0.6219512195121951, 'f1': 0.5795454545454546, 'number': 82} | 0.5781 | 0.7361 | 0.6476 | 0.9515 | |
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| No log | 2.0 | 356 | 0.1071 | {'precision': 0.6994219653179191, 'recall': 0.7960526315789473, 'f1': 0.7446153846153847, 'number': 456} | {'precision': 0.6129032258064516, 'recall': 0.6951219512195121, 'f1': 0.6514285714285714, 'number': 82} | 0.6863 | 0.7807 | 0.7304 | 0.9585 | |
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| 0.1562 | 3.0 | 534 | 0.0990 | {'precision': 0.7150943396226415, 'recall': 0.831140350877193, 'f1': 0.7687626774847871, 'number': 456} | {'precision': 0.6777777777777778, 'recall': 0.7439024390243902, 'f1': 0.7093023255813954, 'number': 82} | 0.7097 | 0.8178 | 0.7599 | 0.9621 | |
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| 0.1562 | 4.0 | 712 | 0.1072 | {'precision': 0.7258687258687259, 'recall': 0.8245614035087719, 'f1': 0.7720739219712526, 'number': 456} | {'precision': 0.7222222222222222, 'recall': 0.7926829268292683, 'f1': 0.7558139534883721, 'number': 82} | 0.7253 | 0.8197 | 0.7696 | 0.9628 | |
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| 0.1562 | 5.0 | 890 | 0.1095 | {'precision': 0.7445544554455445, 'recall': 0.8245614035087719, 'f1': 0.7825182101977106, 'number': 456} | {'precision': 0.7272727272727273, 'recall': 0.7804878048780488, 'f1': 0.7529411764705882, 'number': 82} | 0.7420 | 0.8178 | 0.7781 | 0.9635 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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