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---
license: apache-2.0
base_model: manucos/final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds-ner
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.
It achieves the following results on the evaluation set:
- Loss: 0.3148
- Precision: 0.8725
- Recall: 0.9022
- F1: 0.8871
- Accuracy: 0.9419
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 280 | 0.3056 | 0.7819 | 0.8444 | 0.8120 | 0.9198 |
| 0.4846 | 2.0 | 560 | 0.2545 | 0.8126 | 0.88 | 0.8450 | 0.9360 |
| 0.4846 | 3.0 | 840 | 0.2269 | 0.8671 | 0.8889 | 0.8778 | 0.9414 |
| 0.1366 | 4.0 | 1120 | 0.2519 | 0.8225 | 0.8993 | 0.8592 | 0.9372 |
| 0.1366 | 5.0 | 1400 | 0.2449 | 0.8472 | 0.9037 | 0.8746 | 0.9445 |
| 0.0788 | 6.0 | 1680 | 0.2839 | 0.8725 | 0.9022 | 0.8871 | 0.9419 |
| 0.0788 | 7.0 | 1960 | 0.2849 | 0.8631 | 0.8963 | 0.8794 | 0.9386 |
| 0.0441 | 8.0 | 2240 | 0.2987 | 0.8736 | 0.9007 | 0.8869 | 0.9397 |
| 0.0335 | 9.0 | 2520 | 0.3192 | 0.8602 | 0.9022 | 0.8807 | 0.9377 |
| 0.0335 | 10.0 | 2800 | 0.3094 | 0.8734 | 0.8993 | 0.8861 | 0.9414 |
| 0.0242 | 11.0 | 3080 | 0.3233 | 0.8748 | 0.9007 | 0.8876 | 0.9383 |
| 0.0242 | 12.0 | 3360 | 0.3215 | 0.8764 | 0.9037 | 0.8899 | 0.9417 |
| 0.0189 | 13.0 | 3640 | 0.3155 | 0.8684 | 0.8993 | 0.8836 | 0.9405 |
| 0.0189 | 14.0 | 3920 | 0.3156 | 0.8725 | 0.9022 | 0.8871 | 0.9425 |
| 0.0172 | 15.0 | 4200 | 0.3148 | 0.8725 | 0.9022 | 0.8871 | 0.9419 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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