---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: datos-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. -->

# datos-ner

This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0751
- Precision: 0.9516
- Recall: 0.9219
- F1: 0.9365
- Accuracy: 0.9805

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 38   | 0.6419          | 0.8947    | 0.2656 | 0.4096 | 0.8357   |
| No log        | 2.0   | 76   | 0.2665          | 0.8511    | 0.625  | 0.7207 | 0.9276   |
| No log        | 3.0   | 114  | 0.1322          | 0.9508    | 0.9062 | 0.9280 | 0.9749   |
| No log        | 4.0   | 152  | 0.0907          | 0.9524    | 0.9375 | 0.9449 | 0.9805   |
| No log        | 5.0   | 190  | 0.0760          | 0.9683    | 0.9531 | 0.9606 | 0.9833   |
| No log        | 6.0   | 228  | 0.0644          | 0.9531    | 0.9531 | 0.9531 | 0.9861   |
| No log        | 7.0   | 266  | 0.0728          | 0.9365    | 0.9219 | 0.9291 | 0.9805   |
| No log        | 8.0   | 304  | 0.0690          | 0.9365    | 0.9219 | 0.9291 | 0.9805   |
| No log        | 9.0   | 342  | 0.0709          | 0.9365    | 0.9219 | 0.9291 | 0.9805   |
| No log        | 10.0  | 380  | 0.0781          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| No log        | 11.0  | 418  | 0.0654          | 0.9365    | 0.9219 | 0.9291 | 0.9805   |
| No log        | 12.0  | 456  | 0.0746          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| No log        | 13.0  | 494  | 0.0721          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| 0.1874        | 14.0  | 532  | 0.0739          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| 0.1874        | 15.0  | 570  | 0.0765          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| 0.1874        | 16.0  | 608  | 0.0777          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| 0.1874        | 17.0  | 646  | 0.0756          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| 0.1874        | 18.0  | 684  | 0.0765          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| 0.1874        | 19.0  | 722  | 0.0758          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |
| 0.1874        | 20.0  | 760  | 0.0751          | 0.9516    | 0.9219 | 0.9365 | 0.9805   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2