---
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: 0.848714069591528
    - name: Recall
      type: recall
      value: 0.8995189738107964
    - name: F1
      type: f1
      value: 0.8733783082511676
    - name: Accuracy
      type: accuracy
      value: 0.9711435696473103
---

<!-- 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: 0.1689
- Precision: 0.8487
- Recall: 0.8995
- F1: 0.8734
- Accuracy: 0.9711

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3372        | 1.72  | 500  | 0.1525          | 0.7806    | 0.8632 | 0.8198 | 0.9639   |
| 0.117         | 3.44  | 1000 | 0.1341          | 0.8162    | 0.8899 | 0.8514 | 0.9702   |
| 0.077         | 5.15  | 1500 | 0.1457          | 0.8204    | 0.8765 | 0.8475 | 0.9672   |
| 0.0548        | 6.87  | 2000 | 0.1759          | 0.8449    | 0.8910 | 0.8673 | 0.9690   |
| 0.037         | 8.59  | 2500 | 0.1689          | 0.8487    | 0.8995 | 0.8734 | 0.9711   |


### Framework versions

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