---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC_xlm-roberta-large
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: cnec
      type: cnec
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8548310328415041
    - name: Recall
      type: recall
      value: 0.8913151364764268
    - name: F1
      type: f1
      value: 0.8726919339164239
    - name: Accuracy
      type: accuracy
      value: 0.9753512880562061
---

<!-- 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. -->

# CNEC_xlm-roberta-large

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1540
- Precision: 0.8548
- Recall: 0.8913
- F1: 0.8727
- Accuracy: 0.9754

## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2864        | 0.56  | 500  | 0.1328          | 0.7015    | 0.8119 | 0.7527 | 0.9629   |
| 0.13          | 1.12  | 1000 | 0.1221          | 0.7836    | 0.8734 | 0.8261 | 0.9701   |
| 0.0972        | 1.68  | 1500 | 0.1140          | 0.7836    | 0.8610 | 0.8205 | 0.9710   |
| 0.0807        | 2.24  | 2000 | 0.1244          | 0.8032    | 0.8730 | 0.8366 | 0.9730   |
| 0.0626        | 2.8   | 2500 | 0.1135          | 0.8104    | 0.8844 | 0.8458 | 0.9755   |
| 0.0451        | 3.36  | 3000 | 0.1371          | 0.8305    | 0.8824 | 0.8556 | 0.9733   |
| 0.0397        | 3.92  | 3500 | 0.1251          | 0.8307    | 0.8814 | 0.8553 | 0.9736   |
| 0.0244        | 4.48  | 4000 | 0.1441          | 0.8370    | 0.8794 | 0.8577 | 0.9740   |
| 0.0257        | 5.04  | 4500 | 0.1319          | 0.8541    | 0.8888 | 0.8711 | 0.9759   |
| 0.0164        | 5.6   | 5000 | 0.1465          | 0.8421    | 0.8868 | 0.8639 | 0.9754   |
| 0.013         | 6.16  | 5500 | 0.1494          | 0.8473    | 0.8868 | 0.8666 | 0.9751   |
| 0.0108        | 6.72  | 6000 | 0.1540          | 0.8548    | 0.8913 | 0.8727 | 0.9754   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0