---
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: 0.8750653423941454
    - name: Recall
      type: recall
      value: 0.89470871191876
    - name: F1
      type: f1
      value: 0.8847780126849896
    - name: Accuracy
      type: accuracy
      value: 0.9699164786446582
---

<!-- 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: 0.2020
- Precision: 0.8751
- Recall: 0.8947
- F1: 0.8848
- Accuracy: 0.9699

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3776        | 1.0   | 581  | 0.1732          | 0.7868    | 0.8423 | 0.8136 | 0.9580   |
| 0.1773        | 2.0   | 1162 | 0.1476          | 0.8243    | 0.8675 | 0.8453 | 0.9625   |
| 0.127         | 3.0   | 1743 | 0.1522          | 0.8373    | 0.8691 | 0.8529 | 0.9654   |
| 0.1057        | 4.0   | 2324 | 0.1516          | 0.8604    | 0.8728 | 0.8665 | 0.9665   |
| 0.0852        | 5.0   | 2905 | 0.1555          | 0.8501    | 0.8883 | 0.8688 | 0.9700   |
| 0.069         | 6.0   | 3486 | 0.1847          | 0.8637    | 0.8910 | 0.8771 | 0.9681   |
| 0.0452        | 7.0   | 4067 | 0.1751          | 0.8666    | 0.8851 | 0.8757 | 0.9682   |
| 0.0385        | 8.0   | 4648 | 0.1968          | 0.8626    | 0.8888 | 0.8755 | 0.9690   |
| 0.0326        | 9.0   | 5229 | 0.1932          | 0.8717    | 0.8936 | 0.8826 | 0.9704   |
| 0.026         | 10.0  | 5810 | 0.2020          | 0.8751    | 0.8947 | 0.8848 | 0.9699   |


### Framework versions

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