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

# consejo-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: 0.3066
- Precision: 0.7241
- Recall: 0.6774
- F1: 0.7
- Accuracy: 0.9313

## 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   | 15   | 1.5724          | 0.0       | 0.0    | 0.0    | 0.6985   |
| No log        | 2.0   | 30   | 1.3540          | 0.0       | 0.0    | 0.0    | 0.6985   |
| No log        | 3.0   | 45   | 1.0972          | 0.0       | 0.0    | 0.0    | 0.7099   |
| No log        | 4.0   | 60   | 0.8615          | 0.5833    | 0.2258 | 0.3256 | 0.7672   |
| No log        | 5.0   | 75   | 0.7381          | 0.5       | 0.3548 | 0.4151 | 0.8244   |
| No log        | 6.0   | 90   | 0.6111          | 0.5556    | 0.4839 | 0.5172 | 0.8473   |
| No log        | 7.0   | 105  | 0.5353          | 0.5185    | 0.4516 | 0.4828 | 0.8550   |
| No log        | 8.0   | 120  | 0.4786          | 0.5769    | 0.4839 | 0.5263 | 0.8626   |
| No log        | 9.0   | 135  | 0.4493          | 0.5357    | 0.4839 | 0.5085 | 0.8817   |
| No log        | 10.0  | 150  | 0.4269          | 0.4839    | 0.4839 | 0.4839 | 0.8779   |
| No log        | 11.0  | 165  | 0.3977          | 0.5938    | 0.6129 | 0.6032 | 0.8931   |
| No log        | 12.0  | 180  | 0.3669          | 0.5161    | 0.5161 | 0.5161 | 0.8969   |
| No log        | 13.0  | 195  | 0.3437          | 0.6786    | 0.6129 | 0.6441 | 0.9237   |
| No log        | 14.0  | 210  | 0.3389          | 0.6786    | 0.6129 | 0.6441 | 0.9198   |
| No log        | 15.0  | 225  | 0.3249          | 0.6786    | 0.6129 | 0.6441 | 0.9198   |
| No log        | 16.0  | 240  | 0.3102          | 0.6897    | 0.6452 | 0.6667 | 0.9275   |
| No log        | 17.0  | 255  | 0.3094          | 0.6667    | 0.6452 | 0.6557 | 0.9275   |
| No log        | 18.0  | 270  | 0.3159          | 0.7       | 0.6774 | 0.6885 | 0.9198   |
| No log        | 19.0  | 285  | 0.3094          | 0.7241    | 0.6774 | 0.7    | 0.9313   |
| No log        | 20.0  | 300  | 0.3066          | 0.7241    | 0.6774 | 0.7    | 0.9313   |


### Framework versions

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