---
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: 0.1618
- Precision: 0.7352
- Recall: 0.6436
- F1: 0.6863
- Accuracy: 0.9712

## 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: 5e-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   | 0.3028          | 0.0       | 0.0    | 0.0    | 0.9220   |
| No log        | 2.0   | 58   | 0.2800          | 0.0       | 0.0    | 0.0    | 0.9220   |
| No log        | 3.0   | 87   | 0.2136          | 0.2105    | 0.0277 | 0.0489 | 0.9302   |
| No log        | 4.0   | 116  | 0.1803          | 0.375     | 0.0727 | 0.1217 | 0.9391   |
| No log        | 5.0   | 145  | 0.1737          | 0.4923    | 0.2215 | 0.3055 | 0.9462   |
| No log        | 6.0   | 174  | 0.1354          | 0.6124    | 0.3772 | 0.4668 | 0.9584   |
| No log        | 7.0   | 203  | 0.1399          | 0.6062    | 0.4048 | 0.4855 | 0.9589   |
| No log        | 8.0   | 232  | 0.1444          | 0.6220    | 0.5294 | 0.5720 | 0.9623   |
| No log        | 9.0   | 261  | 0.1252          | 0.6439    | 0.6194 | 0.6314 | 0.9662   |
| No log        | 10.0  | 290  | 0.1757          | 0.7216    | 0.4394 | 0.5462 | 0.9604   |
| No log        | 11.0  | 319  | 0.1352          | 0.6707    | 0.5779 | 0.6208 | 0.9667   |
| No log        | 12.0  | 348  | 0.1276          | 0.6797    | 0.6021 | 0.6385 | 0.9677   |
| No log        | 13.0  | 377  | 0.1542          | 0.7328    | 0.5882 | 0.6526 | 0.9688   |
| No log        | 14.0  | 406  | 0.1418          | 0.7192    | 0.6471 | 0.6812 | 0.9712   |
| No log        | 15.0  | 435  | 0.1678          | 0.7162    | 0.5502 | 0.6223 | 0.9672   |
| No log        | 16.0  | 464  | 0.1559          | 0.7075    | 0.6194 | 0.6605 | 0.9689   |
| No log        | 17.0  | 493  | 0.1446          | 0.6568    | 0.6886 | 0.6723 | 0.9681   |
| 0.079         | 18.0  | 522  | 0.1582          | 0.7348    | 0.5848 | 0.6513 | 0.9693   |
| 0.079         | 19.0  | 551  | 0.1519          | 0.6977    | 0.6228 | 0.6581 | 0.9705   |
| 0.079         | 20.0  | 580  | 0.1503          | 0.7251    | 0.6298 | 0.6741 | 0.9703   |
| 0.079         | 21.0  | 609  | 0.1585          | 0.6834    | 0.6125 | 0.6460 | 0.9703   |
| 0.079         | 22.0  | 638  | 0.1594          | 0.7126    | 0.6263 | 0.6667 | 0.9705   |
| 0.079         | 23.0  | 667  | 0.1558          | 0.7008    | 0.6401 | 0.6691 | 0.9703   |
| 0.079         | 24.0  | 696  | 0.1570          | 0.7273    | 0.6367 | 0.6790 | 0.9708   |
| 0.079         | 25.0  | 725  | 0.1553          | 0.7022    | 0.6609 | 0.6809 | 0.9705   |
| 0.079         | 26.0  | 754  | 0.1592          | 0.7148    | 0.6332 | 0.6716 | 0.9701   |
| 0.079         | 27.0  | 783  | 0.1579          | 0.7170    | 0.6574 | 0.6859 | 0.9710   |
| 0.079         | 28.0  | 812  | 0.1597          | 0.7148    | 0.6505 | 0.6812 | 0.9708   |
| 0.079         | 29.0  | 841  | 0.1625          | 0.7309    | 0.6298 | 0.6766 | 0.9705   |
| 0.079         | 30.0  | 870  | 0.1618          | 0.7352    | 0.6436 | 0.6863 | 0.9712   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3