---
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: 0.1804
- Precision: 0.6443
- Recall: 0.5708
- F1: 0.6053
- Accuracy: 0.9691

## 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   | 0.2727          | 0.0       | 0.0    | 0.0    | 0.9392   |
| No log        | 2.0   | 58   | 0.2246          | 0.1163    | 0.0228 | 0.0382 | 0.9383   |
| No log        | 3.0   | 87   | 0.1744          | 0.3718    | 0.1324 | 0.1953 | 0.9480   |
| No log        | 4.0   | 116  | 0.1492          | 0.4734    | 0.3653 | 0.4124 | 0.9569   |
| No log        | 5.0   | 145  | 0.1472          | 0.4905    | 0.4703 | 0.4802 | 0.9581   |
| No log        | 6.0   | 174  | 0.1320          | 0.5403    | 0.5205 | 0.5302 | 0.9618   |
| No log        | 7.0   | 203  | 0.1423          | 0.5922    | 0.5571 | 0.5741 | 0.9667   |
| No log        | 8.0   | 232  | 0.1616          | 0.5838    | 0.5251 | 0.5529 | 0.9648   |
| No log        | 9.0   | 261  | 0.1443          | 0.6082    | 0.5388 | 0.5714 | 0.9676   |
| No log        | 10.0  | 290  | 0.1681          | 0.5990    | 0.5662 | 0.5822 | 0.9654   |
| No log        | 11.0  | 319  | 0.1611          | 0.4853    | 0.6027 | 0.5377 | 0.9599   |
| No log        | 12.0  | 348  | 0.1751          | 0.4887    | 0.5936 | 0.5361 | 0.9588   |
| No log        | 13.0  | 377  | 0.1796          | 0.4819    | 0.6073 | 0.5374 | 0.9593   |
| No log        | 14.0  | 406  | 0.1609          | 0.6760    | 0.5525 | 0.6080 | 0.9699   |
| No log        | 15.0  | 435  | 0.1821          | 0.5136    | 0.6027 | 0.5546 | 0.9606   |
| No log        | 16.0  | 464  | 0.1581          | 0.6462    | 0.5753 | 0.6087 | 0.9691   |
| No log        | 17.0  | 493  | 0.1582          | 0.6531    | 0.5845 | 0.6169 | 0.9692   |
| 0.0763        | 18.0  | 522  | 0.1641          | 0.5574    | 0.6210 | 0.5875 | 0.9648   |
| 0.0763        | 19.0  | 551  | 0.1681          | 0.5671    | 0.5982 | 0.5822 | 0.9663   |
| 0.0763        | 20.0  | 580  | 0.1710          | 0.5917    | 0.5890 | 0.5904 | 0.9667   |
| 0.0763        | 21.0  | 609  | 0.1794          | 0.6703    | 0.5662 | 0.6139 | 0.9702   |
| 0.0763        | 22.0  | 638  | 0.1759          | 0.6103    | 0.5936 | 0.6019 | 0.9672   |
| 0.0763        | 23.0  | 667  | 0.1762          | 0.6298    | 0.5982 | 0.6136 | 0.9687   |
| 0.0763        | 24.0  | 696  | 0.1811          | 0.6176    | 0.5753 | 0.5957 | 0.9681   |
| 0.0763        | 25.0  | 725  | 0.1793          | 0.6337    | 0.5845 | 0.6081 | 0.9696   |
| 0.0763        | 26.0  | 754  | 0.1794          | 0.6796    | 0.5616 | 0.615  | 0.9702   |
| 0.0763        | 27.0  | 783  | 0.1776          | 0.6293    | 0.5890 | 0.6085 | 0.9692   |
| 0.0763        | 28.0  | 812  | 0.1796          | 0.6443    | 0.5708 | 0.6053 | 0.9694   |
| 0.0763        | 29.0  | 841  | 0.1803          | 0.6410    | 0.5708 | 0.6039 | 0.9692   |
| 0.0763        | 30.0  | 870  | 0.1804          | 0.6443    | 0.5708 | 0.6053 | 0.9691   |


### Framework versions

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