---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC1_1_extended_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: 0.8425832492431887
    - name: Recall
      type: recall
      value: 0.8925708177445216
    - name: F1
      type: f1
      value: 0.8668569945497016
    - name: Accuracy
      type: accuracy
      value: 0.968847721964929
---

<!-- 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. -->

# CNEC1_1_extended_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: 0.1815
- Precision: 0.8426
- Recall: 0.8926
- F1: 0.8669
- Accuracy: 0.9688

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3568        | 0.86  | 500  | 0.1652          | 0.7278    | 0.8290 | 0.7751 | 0.9578   |
| 0.175         | 1.72  | 1000 | 0.1474          | 0.7862    | 0.8530 | 0.8183 | 0.9662   |
| 0.1225        | 2.58  | 1500 | 0.1417          | 0.8013    | 0.8642 | 0.8316 | 0.9650   |
| 0.0994        | 3.44  | 2000 | 0.1673          | 0.8095    | 0.8744 | 0.8407 | 0.9654   |
| 0.0781        | 4.3   | 2500 | 0.1568          | 0.8383    | 0.8808 | 0.8590 | 0.9686   |
| 0.0638        | 5.16  | 3000 | 0.1653          | 0.8272    | 0.8851 | 0.8552 | 0.9683   |
| 0.0521        | 6.02  | 3500 | 0.1680          | 0.8419    | 0.8995 | 0.8698 | 0.9695   |
| 0.0394        | 6.88  | 4000 | 0.1761          | 0.8374    | 0.8920 | 0.8639 | 0.9685   |
| 0.0326        | 7.75  | 4500 | 0.1815          | 0.8426    | 0.8926 | 0.8669 | 0.9688   |


### Framework versions

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