---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ner_ANAT_DISO
  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. -->

# ner_ANAT_DISO

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0746
- Anat Precision: 0.6512
- Anat Recall: 0.6573
- Anat F1: 0.6542
- Anat Number: 534
- Diso Precision: 0.8727
- Diso Recall: 0.8844
- Diso F1: 0.8785
- Diso Number: 2915
- Overall Precision: 0.8385
- Overall Recall: 0.8492
- Overall F1: 0.8438
- Overall Accuracy: 0.9838

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0625        | 1.0   | 1682 | 0.0591          | 0.5407         | 0.6723      | 0.5993  | 534         | 0.8516         | 0.8624      | 0.8570  | 2915        | 0.7945            | 0.8330         | 0.8133     | 0.9808           |
| 0.0397        | 2.0   | 3364 | 0.0633          | 0.6237         | 0.6798      | 0.6505  | 534         | 0.8576         | 0.8820      | 0.8696  | 2915        | 0.8196            | 0.8507         | 0.8348     | 0.9826           |
| 0.0181        | 3.0   | 5046 | 0.0698          | 0.6452         | 0.6948      | 0.6691  | 534         | 0.8670         | 0.8878      | 0.8773  | 2915        | 0.8312            | 0.8579         | 0.8443     | 0.9833           |
| 0.0121        | 4.0   | 6728 | 0.0746          | 0.6512         | 0.6573      | 0.6542  | 534         | 0.8727         | 0.8844      | 0.8785  | 2915        | 0.8385            | 0.8492         | 0.8438     | 0.9838           |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2