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--- |
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license: apache-2.0 |
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base_model: manucos/final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds |
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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: final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds-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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# final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds-ner |
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This model is a fine-tuned version of [manucos/final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds](https://huggingface.co/manucos/final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3148 |
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- Precision: 0.8725 |
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- Recall: 0.9022 |
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- F1: 0.8871 |
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- Accuracy: 0.9419 |
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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: 15 |
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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 | 1.0 | 280 | 0.3056 | 0.7819 | 0.8444 | 0.8120 | 0.9198 | |
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| 0.4846 | 2.0 | 560 | 0.2545 | 0.8126 | 0.88 | 0.8450 | 0.9360 | |
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| 0.4846 | 3.0 | 840 | 0.2269 | 0.8671 | 0.8889 | 0.8778 | 0.9414 | |
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| 0.1366 | 4.0 | 1120 | 0.2519 | 0.8225 | 0.8993 | 0.8592 | 0.9372 | |
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| 0.1366 | 5.0 | 1400 | 0.2449 | 0.8472 | 0.9037 | 0.8746 | 0.9445 | |
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| 0.0788 | 6.0 | 1680 | 0.2839 | 0.8725 | 0.9022 | 0.8871 | 0.9419 | |
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| 0.0788 | 7.0 | 1960 | 0.2849 | 0.8631 | 0.8963 | 0.8794 | 0.9386 | |
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| 0.0441 | 8.0 | 2240 | 0.2987 | 0.8736 | 0.9007 | 0.8869 | 0.9397 | |
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| 0.0335 | 9.0 | 2520 | 0.3192 | 0.8602 | 0.9022 | 0.8807 | 0.9377 | |
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| 0.0335 | 10.0 | 2800 | 0.3094 | 0.8734 | 0.8993 | 0.8861 | 0.9414 | |
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| 0.0242 | 11.0 | 3080 | 0.3233 | 0.8748 | 0.9007 | 0.8876 | 0.9383 | |
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| 0.0242 | 12.0 | 3360 | 0.3215 | 0.8764 | 0.9037 | 0.8899 | 0.9417 | |
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| 0.0189 | 13.0 | 3640 | 0.3155 | 0.8684 | 0.8993 | 0.8836 | 0.9405 | |
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| 0.0189 | 14.0 | 3920 | 0.3156 | 0.8725 | 0.9022 | 0.8871 | 0.9425 | |
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| 0.0172 | 15.0 | 4200 | 0.3148 | 0.8725 | 0.9022 | 0.8871 | 0.9419 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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