---
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: 0.8533541341653667
    - name: Recall
      type: recall
      value: 0.8770710849812934
    - name: F1
      type: f1
      value: 0.8650500790722193
    - name: Accuracy
      type: accuracy
      value: 0.9670664608320468
---

<!-- 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: 0.1498
- Precision: 0.8534
- Recall: 0.8771
- F1: 0.8651
- Accuracy: 0.9671

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3961        | 1.0   | 581  | 0.1800          | 0.8004    | 0.8231 | 0.8116 | 0.9560   |
| 0.1772        | 2.0   | 1162 | 0.1518          | 0.8357    | 0.8648 | 0.8500 | 0.9642   |
| 0.1266        | 3.0   | 1743 | 0.1545          | 0.8377    | 0.8717 | 0.8544 | 0.9680   |
| 0.1043        | 4.0   | 2324 | 0.1472          | 0.8473    | 0.8691 | 0.8580 | 0.9656   |
| 0.0804        | 5.0   | 2905 | 0.1498          | 0.8534    | 0.8771 | 0.8651 | 0.9671   |


### Framework versions

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