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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: alvaroalon2/biobert_chemical_ner |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: murat_chem_model |
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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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# murat_chem_model |
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This model is a fine-tuned version of [alvaroalon2/biobert_chemical_ner](https://huggingface.co/alvaroalon2/biobert_chemical_ner) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3827 |
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- Chemical: {'precision': 0.8843176605504587, 'recall': 0.8439124487004104, 'f1': 0.863642727145457, 'number': 7310} |
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- Overall Precision: 0.8843 |
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- Overall Recall: 0.8439 |
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- Overall F1: 0.8636 |
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- Overall Accuracy: 0.9447 |
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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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- num_epochs: 15 |
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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 | Chemical | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.6817 | 1.2048 | 100 | 0.3090 | {'precision': 0.8702002710435175, 'recall': 0.7905608755129959, 'f1': 0.828471077342126, 'number': 7310} | 0.8702 | 0.7906 | 0.8285 | 0.9204 | |
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| 1.6817 | 2.4096 | 200 | 0.2900 | {'precision': 0.8720861611094718, 'recall': 0.8086183310533516, 'f1': 0.8391538898353209, 'number': 7310} | 0.8721 | 0.8086 | 0.8392 | 0.9312 | |
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| 1.6817 | 3.6145 | 300 | 0.3071 | {'precision': 0.8830255057167986, 'recall': 0.824076607387141, 'f1': 0.8525332578545147, 'number': 7310} | 0.8830 | 0.8241 | 0.8525 | 0.9398 | |
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| 1.6817 | 4.8193 | 400 | 0.3099 | {'precision': 0.882646325363063, 'recall': 0.8231190150478797, 'f1': 0.8518439866921499, 'number': 7310} | 0.8826 | 0.8231 | 0.8518 | 0.9411 | |
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| 0.1018 | 6.0241 | 500 | 0.3891 | {'precision': 0.8816456613066782, 'recall': 0.8325581395348837, 'f1': 0.8563990712727785, 'number': 7310} | 0.8816 | 0.8326 | 0.8564 | 0.9402 | |
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| 0.1018 | 7.2289 | 600 | 0.3672 | {'precision': 0.8851640513552068, 'recall': 0.8488372093023255, 'f1': 0.8666201117318435, 'number': 7310} | 0.8852 | 0.8488 | 0.8666 | 0.9451 | |
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| 0.1018 | 8.4337 | 700 | 0.3459 | {'precision': 0.8812949640287769, 'recall': 0.8378932968536251, 'f1': 0.8590462833099579, 'number': 7310} | 0.8813 | 0.8379 | 0.8590 | 0.9449 | |
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| 0.1018 | 9.6386 | 800 | 0.3601 | {'precision': 0.880656108597285, 'recall': 0.8519835841313269, 'f1': 0.8660826032540675, 'number': 7310} | 0.8807 | 0.8520 | 0.8661 | 0.9462 | |
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| 0.1018 | 10.8434 | 900 | 0.3711 | {'precision': 0.881471972614463, 'recall': 0.8454172366621067, 'f1': 0.8630682214929124, 'number': 7310} | 0.8815 | 0.8454 | 0.8631 | 0.9443 | |
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| 0.0038 | 12.0482 | 1000 | 0.3779 | {'precision': 0.8816542644533486, 'recall': 0.8428180574555404, 'f1': 0.8617988529864317, 'number': 7310} | 0.8817 | 0.8428 | 0.8618 | 0.9437 | |
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| 0.0038 | 13.2530 | 1100 | 0.3829 | {'precision': 0.8837275985663082, 'recall': 0.8432284541723666, 'f1': 0.863003150157508, 'number': 7310} | 0.8837 | 0.8432 | 0.8630 | 0.9447 | |
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| 0.0038 | 14.4578 | 1200 | 0.3827 | {'precision': 0.8843176605504587, 'recall': 0.8439124487004104, 'f1': 0.863642727145457, 'number': 7310} | 0.8843 | 0.8439 | 0.8636 | 0.9447 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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