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--- |
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base_model: manucos/finetuned__beto-clinical-wl-es__augmented-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: finetuned__beto-clinical-wl-es__augmented-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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# finetuned__beto-clinical-wl-es__augmented-ultrasounds-ner |
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This model is a fine-tuned version of [manucos/finetuned__beto-clinical-wl-es__augmented-ultrasounds](https://huggingface.co/manucos/finetuned__beto-clinical-wl-es__augmented-ultrasounds) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3556 |
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- Precision: 0.8057 |
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- Recall: 0.8897 |
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- F1: 0.8456 |
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- Accuracy: 0.9216 |
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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: 10 |
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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 | 206 | 0.2470 | 0.7303 | 0.8441 | 0.7831 | 0.9080 | |
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| No log | 2.0 | 412 | 0.2471 | 0.7632 | 0.8644 | 0.8106 | 0.9182 | |
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| 0.3075 | 3.0 | 618 | 0.2838 | 0.7661 | 0.8553 | 0.8082 | 0.9106 | |
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| 0.3075 | 4.0 | 824 | 0.3057 | 0.7802 | 0.8765 | 0.8255 | 0.9170 | |
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| 0.0622 | 5.0 | 1030 | 0.2932 | 0.7844 | 0.8836 | 0.8310 | 0.9204 | |
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| 0.0622 | 6.0 | 1236 | 0.3435 | 0.8037 | 0.8907 | 0.8449 | 0.9211 | |
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| 0.0622 | 7.0 | 1442 | 0.3464 | 0.8013 | 0.8856 | 0.8413 | 0.9196 | |
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| 0.0255 | 8.0 | 1648 | 0.3470 | 0.7980 | 0.8877 | 0.8404 | 0.9224 | |
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| 0.0255 | 9.0 | 1854 | 0.3556 | 0.8055 | 0.8887 | 0.8450 | 0.9201 | |
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| 0.0159 | 10.0 | 2060 | 0.3556 | 0.8057 | 0.8897 | 0.8456 | 0.9216 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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