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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wl-disease |
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model-index: |
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- name: WL_DISEASE_NER_v1 |
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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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# WL_DISEASE_NER_v1 |
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This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the wl-disease dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1489 |
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- Diso Precision: 0.7908 |
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- Diso Recall: 0.8397 |
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- Diso F1: 0.8145 |
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- Diso Number: 1765 |
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- Overall Precision: 0.7908 |
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- Overall Recall: 0.8397 |
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- Overall F1: 0.8145 |
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- Overall Accuracy: 0.9631 |
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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: 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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.1199 | 1.0 | 1714 | 0.1187 | 0.7739 | 0.7972 | 0.7854 | 1765 | 0.7739 | 0.7972 | 0.7854 | 0.9610 | |
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| 0.0916 | 2.0 | 3428 | 0.1237 | 0.7748 | 0.8266 | 0.7999 | 1765 | 0.7748 | 0.8266 | 0.7999 | 0.9620 | |
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| 0.0625 | 3.0 | 5142 | 0.1343 | 0.7900 | 0.8289 | 0.8090 | 1765 | 0.7900 | 0.8289 | 0.8090 | 0.9630 | |
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| 0.0485 | 4.0 | 6856 | 0.1489 | 0.7908 | 0.8397 | 0.8145 | 1765 | 0.7908 | 0.8397 | 0.8145 | 0.9631 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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