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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_extra_data |
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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_extra_data |
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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: 0.0089 |
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- Chemical: {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} |
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- Micro avg: {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} |
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- Macro avg: {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} |
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- Weighted avg: {'precision': 0.9656699889258028, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539658, 'support': 900} |
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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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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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 | Micro avg | Macro avg | Weighted avg | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:| |
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| 0.0266 | 1.0 | 16198 | 0.0113 | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} | {'precision': 0.9423503325942351, 'recall': 0.9444444444444444, 'f1-score': 0.9433962264150944, 'support': 900} | |
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| 0.0092 | 2.0 | 32396 | 0.0077 | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} | {'precision': 0.9679203539823009, 'recall': 0.9722222222222222, 'f1-score': 0.9700665188470067, 'support': 900} | |
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| 0.0051 | 3.0 | 48594 | 0.0089 | {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} | {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} | {'precision': 0.9656699889258029, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539656, 'support': 900} | {'precision': 0.9656699889258028, 'recall': 0.9688888888888889, 'f1-score': 0.9672767609539658, 'support': 900} | |
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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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