---
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: 0.8526912181303116
    - name: Recall
      type: recall
      value: 0.8962779156327544
    - name: F1
      type: f1
      value: 0.8739414468908783
    - name: Accuracy
      type: accuracy
      value: 0.9765807962529274
---

<!-- 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: 0.1428
- Precision: 0.8527
- Recall: 0.8963
- F1: 0.8739
- Accuracy: 0.9766

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2508        | 1.12  | 500  | 0.1431          | 0.7569    | 0.8481 | 0.7999 | 0.9672   |
| 0.1103        | 2.24  | 1000 | 0.1169          | 0.7717    | 0.8541 | 0.8108 | 0.9704   |
| 0.0731        | 3.36  | 1500 | 0.1134          | 0.8066    | 0.8715 | 0.8378 | 0.9749   |
| 0.0527        | 4.47  | 2000 | 0.1137          | 0.8360    | 0.8928 | 0.8635 | 0.9767   |
| 0.039         | 5.59  | 2500 | 0.1248          | 0.8364    | 0.8854 | 0.8602 | 0.9755   |
| 0.0265        | 6.71  | 3000 | 0.1252          | 0.8427    | 0.8878 | 0.8647 | 0.9769   |
| 0.0206        | 7.83  | 3500 | 0.1424          | 0.8473    | 0.8953 | 0.8707 | 0.9757   |
| 0.0148        | 8.95  | 4000 | 0.1428          | 0.8527    | 0.8963 | 0.8739 | 0.9766   |


### Framework versions

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