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--- |
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library_name: transformers |
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license: mit |
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base_model: emilyalsentzer/Bio_ClinicalBERT |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: BioMedRoBERTa-finetuned-ner-pablo-just-classifier |
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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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# BioMedRoBERTa-finetuned-ner-pablo-just-classifier |
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This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1228 |
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- Precision: 0.6701 |
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- Recall: 0.6809 |
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- F1: 0.6754 |
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- Accuracy: 0.9657 |
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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: 0.01 |
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- train_batch_size: 512 |
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- eval_batch_size: 512 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 2048 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 10 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 0.9697 | 16 | 0.2938 | 0.4425 | 0.5130 | 0.4751 | 0.9361 | |
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| No log | 2.0 | 33 | 0.1815 | 0.5546 | 0.5873 | 0.5705 | 0.9535 | |
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| No log | 2.9697 | 49 | 0.1617 | 0.5838 | 0.6189 | 0.6008 | 0.9575 | |
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| No log | 4.0 | 66 | 0.1482 | 0.6070 | 0.6396 | 0.6229 | 0.9602 | |
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| No log | 4.9697 | 82 | 0.1340 | 0.6465 | 0.6563 | 0.6513 | 0.9633 | |
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| No log | 6.0 | 99 | 0.1306 | 0.6561 | 0.6638 | 0.6599 | 0.9641 | |
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| No log | 6.9697 | 115 | 0.1290 | 0.6569 | 0.6705 | 0.6636 | 0.9645 | |
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| No log | 8.0 | 132 | 0.1246 | 0.6664 | 0.6794 | 0.6728 | 0.9654 | |
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| No log | 8.9697 | 148 | 0.1230 | 0.6699 | 0.6793 | 0.6745 | 0.9656 | |
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| No log | 9.6970 | 160 | 0.1228 | 0.6701 | 0.6809 | 0.6754 | 0.9657 | |
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### Framework versions |
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- Transformers 4.44.1 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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