---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC2_0_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: 0.8543457497612226
    - name: Recall
      type: recall
      value: 0.8878411910669975
    - name: F1
      type: f1
      value: 0.8707714772450719
    - name: Accuracy
      type: accuracy
      value: 0.9749707259953162
---

<!-- 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. -->

# CNEC2_0_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: 0.1492
- Precision: 0.8543
- Recall: 0.8878
- F1: 0.8708
- Accuracy: 0.9750

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4615        | 0.56  | 500  | 0.1970          | 0.6988    | 0.7057 | 0.7022 | 0.9497   |
| 0.2127        | 1.12  | 1000 | 0.1455          | 0.7812    | 0.8238 | 0.8019 | 0.9626   |
| 0.1765        | 1.68  | 1500 | 0.1284          | 0.8042    | 0.8357 | 0.8197 | 0.9674   |
| 0.149         | 2.24  | 2000 | 0.1307          | 0.8250    | 0.8541 | 0.8393 | 0.9698   |
| 0.1286        | 2.8   | 2500 | 0.1214          | 0.8178    | 0.8600 | 0.8384 | 0.9717   |
| 0.1172        | 3.36  | 3000 | 0.1271          | 0.8285    | 0.8536 | 0.8409 | 0.9716   |
| 0.1115        | 3.92  | 3500 | 0.1290          | 0.8305    | 0.8586 | 0.8443 | 0.9726   |
| 0.0978        | 4.48  | 4000 | 0.1251          | 0.8321    | 0.8630 | 0.8473 | 0.9736   |
| 0.0907        | 5.04  | 4500 | 0.1301          | 0.8417    | 0.8710 | 0.8561 | 0.9742   |
| 0.0812        | 5.6   | 5000 | 0.1235          | 0.8365    | 0.8630 | 0.8495 | 0.9721   |
| 0.0755        | 6.16  | 5500 | 0.1269          | 0.8201    | 0.8685 | 0.8436 | 0.9727   |
| 0.0647        | 6.72  | 6000 | 0.1325          | 0.8490    | 0.8789 | 0.8637 | 0.9742   |
| 0.0589        | 7.28  | 6500 | 0.1373          | 0.8529    | 0.8774 | 0.8650 | 0.9746   |
| 0.0559        | 7.84  | 7000 | 0.1419          | 0.8463    | 0.8824 | 0.8639 | 0.9747   |
| 0.0549        | 8.4   | 7500 | 0.1400          | 0.8444    | 0.8834 | 0.8634 | 0.9745   |
| 0.0479        | 8.96  | 8000 | 0.1462          | 0.8530    | 0.8844 | 0.8684 | 0.9752   |
| 0.0462        | 9.52  | 8500 | 0.1492          | 0.8543    | 0.8878 | 0.8708 | 0.9750   |


### Framework versions

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