---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-2
  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-2

This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1791
- Precision: 0.5224
- Recall: 0.6222
- F1: 0.5680
- Accuracy: 0.9631

## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 29   | 0.2584          | 0.0       | 0.0    | 0.0    | 0.9365   |
| No log        | 2.0   | 58   | 0.2386          | 0.1364    | 0.0133 | 0.0243 | 0.9458   |
| No log        | 3.0   | 87   | 0.2312          | 0.2368    | 0.04   | 0.0684 | 0.9466   |
| No log        | 4.0   | 116  | 0.1806          | 0.2809    | 0.2222 | 0.2481 | 0.9422   |
| No log        | 5.0   | 145  | 0.1446          | 0.4453    | 0.2711 | 0.3370 | 0.9558   |
| No log        | 6.0   | 174  | 0.1575          | 0.3778    | 0.3022 | 0.3358 | 0.9493   |
| No log        | 7.0   | 203  | 0.1255          | 0.5081    | 0.4178 | 0.4585 | 0.9601   |
| No log        | 8.0   | 232  | 0.1290          | 0.4599    | 0.4844 | 0.4719 | 0.9596   |
| No log        | 9.0   | 261  | 0.1383          | 0.4844    | 0.4844 | 0.4844 | 0.9597   |
| No log        | 10.0  | 290  | 0.1534          | 0.4313    | 0.6133 | 0.5064 | 0.9519   |
| No log        | 11.0  | 319  | 0.1575          | 0.4423    | 0.6133 | 0.5140 | 0.9560   |
| No log        | 12.0  | 348  | 0.1437          | 0.5888    | 0.5156 | 0.5498 | 0.9670   |
| No log        | 13.0  | 377  | 0.1605          | 0.5       | 0.5911 | 0.5418 | 0.9589   |
| No log        | 14.0  | 406  | 0.1529          | 0.5459    | 0.5289 | 0.5372 | 0.9640   |
| No log        | 15.0  | 435  | 0.1569          | 0.5097    | 0.5867 | 0.5455 | 0.9618   |
| No log        | 16.0  | 464  | 0.1656          | 0.4980    | 0.5644 | 0.5292 | 0.9607   |
| No log        | 17.0  | 493  | 0.1602          | 0.5583    | 0.5956 | 0.5763 | 0.9622   |
| 0.0843        | 18.0  | 522  | 0.1767          | 0.4897    | 0.6356 | 0.5532 | 0.9589   |
| 0.0843        | 19.0  | 551  | 0.1642          | 0.5551    | 0.6044 | 0.5787 | 0.9641   |
| 0.0843        | 20.0  | 580  | 0.1635          | 0.6418    | 0.5733 | 0.6056 | 0.9679   |
| 0.0843        | 21.0  | 609  | 0.1706          | 0.5423    | 0.6267 | 0.5814 | 0.9635   |
| 0.0843        | 22.0  | 638  | 0.1691          | 0.5437    | 0.6089 | 0.5744 | 0.9638   |
| 0.0843        | 23.0  | 667  | 0.1743          | 0.5357    | 0.6    | 0.5660 | 0.9631   |
| 0.0843        | 24.0  | 696  | 0.1800          | 0.5176    | 0.6533 | 0.5776 | 0.9627   |
| 0.0843        | 25.0  | 725  | 0.1789          | 0.5       | 0.6    | 0.5455 | 0.9620   |
| 0.0843        | 26.0  | 754  | 0.1754          | 0.5388    | 0.5867 | 0.5617 | 0.9638   |
| 0.0843        | 27.0  | 783  | 0.1797          | 0.5164    | 0.6311 | 0.5680 | 0.9627   |
| 0.0843        | 28.0  | 812  | 0.1816          | 0.5321    | 0.6267 | 0.5755 | 0.9633   |
| 0.0843        | 29.0  | 841  | 0.1793          | 0.5222    | 0.6267 | 0.5697 | 0.9631   |
| 0.0843        | 30.0  | 870  | 0.1791          | 0.5224    | 0.6222 | 0.5680 | 0.9631   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3