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--- |
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library_name: transformers |
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license: mit |
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base_model: EleutherAI/gpt-neo-1.3B |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Ben10x/MedMentions-MTI881-NER |
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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: gpt-medmentions |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: Ben10x/MedMentions-MTI881-NER |
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type: Ben10x/MedMentions-MTI881-NER |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.4453316069630269 |
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- name: Recall |
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type: recall |
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value: 0.5247499576199356 |
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- name: F1 |
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type: f1 |
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value: 0.48178988326848243 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8454107464662687 |
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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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# gpt-medmentions |
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This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) on the Ben10x/MedMentions-MTI881-NER dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5111 |
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- Precision: 0.4453 |
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- Recall: 0.5247 |
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- F1: 0.4818 |
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- Accuracy: 0.8454 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5.0 |
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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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| 0.5307 | 1.0 | 5850 | 0.5369 | 0.4129 | 0.4711 | 0.4401 | 0.8341 | |
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| 0.3585 | 2.0 | 11700 | 0.5111 | 0.4453 | 0.5247 | 0.4818 | 0.8454 | |
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| 0.1758 | 3.0 | 17550 | 0.6349 | 0.4718 | 0.4900 | 0.4807 | 0.8497 | |
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| 0.0751 | 4.0 | 23400 | 0.9264 | 0.4628 | 0.5208 | 0.4901 | 0.8497 | |
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| 0.0387 | 5.0 | 29250 | 1.0903 | 0.4758 | 0.5181 | 0.4960 | 0.8518 | |
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### Framework versions |
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- Transformers 4.50.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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