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--- |
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base_model: medicalai/ClinicalBERT |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: JNLPBA_ClinicalBERT_NER |
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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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# JNLPBA_ClinicalBERT_NER |
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This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1723 |
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- Seqeval classification report: precision recall f1-score support |
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DNA 0.72 0.81 0.77 1351 |
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RNA 0.71 0.86 0.78 723 |
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cell_line 0.84 0.74 0.78 582 |
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cell_type 0.72 0.75 0.73 5623 |
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protein 0.85 0.85 0.85 3501 |
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micro avg 0.76 0.79 0.78 11780 |
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macro avg 0.77 0.80 0.78 11780 |
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weighted avg 0.76 0.79 0.78 11780 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.336 | 1.0 | 582 | 0.1930 | precision recall f1-score support |
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DNA 0.72 0.77 0.75 1351 |
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RNA 0.70 0.84 0.77 723 |
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cell_line 0.85 0.70 0.77 582 |
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cell_type 0.71 0.68 0.69 5623 |
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protein 0.85 0.80 0.83 3501 |
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micro avg 0.76 0.74 0.75 11780 |
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macro avg 0.77 0.76 0.76 11780 |
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weighted avg 0.76 0.74 0.75 11780 |
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| 0.1841 | 2.0 | 1164 | 0.1762 | precision recall f1-score support |
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DNA 0.73 0.78 0.76 1351 |
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RNA 0.70 0.87 0.78 723 |
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cell_line 0.86 0.71 0.78 582 |
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cell_type 0.71 0.73 0.72 5623 |
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protein 0.86 0.83 0.84 3501 |
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micro avg 0.76 0.77 0.77 11780 |
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macro avg 0.77 0.78 0.78 11780 |
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weighted avg 0.77 0.77 0.77 11780 |
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| 0.1582 | 3.0 | 1746 | 0.1723 | precision recall f1-score support |
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DNA 0.72 0.81 0.77 1351 |
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RNA 0.71 0.86 0.78 723 |
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cell_line 0.84 0.74 0.78 582 |
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cell_type 0.72 0.75 0.73 5623 |
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protein 0.85 0.85 0.85 3501 |
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micro avg 0.76 0.79 0.78 11780 |
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macro avg 0.77 0.80 0.78 11780 |
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weighted avg 0.76 0.79 0.78 11780 |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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