---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC2_0_Supertypes_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: 0.8299725022914757
    - name: Recall
      type: recall
      value: 0.874034749034749
    - name: F1
      type: f1
      value: 0.8514339445228021
    - name: Accuracy
      type: accuracy
      value: 0.9687092568448501
---

<!-- 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_Supertypes_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: 0.1727
- Precision: 0.8300
- Recall: 0.8740
- F1: 0.8514
- Accuracy: 0.9687

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3664        | 0.56  | 500  | 0.1708          | 0.6886    | 0.8132 | 0.7457 | 0.9536   |
| 0.1841        | 1.11  | 1000 | 0.1512          | 0.7474    | 0.8470 | 0.7941 | 0.9631   |
| 0.1528        | 1.67  | 1500 | 0.1650          | 0.7530    | 0.8181 | 0.7842 | 0.9612   |
| 0.1313        | 2.22  | 2000 | 0.1598          | 0.7809    | 0.8687 | 0.8225 | 0.9656   |
| 0.1094        | 2.78  | 2500 | 0.1421          | 0.7791    | 0.8475 | 0.8118 | 0.9636   |
| 0.0897        | 3.33  | 3000 | 0.1395          | 0.7958    | 0.8634 | 0.8282 | 0.9669   |
| 0.0864        | 3.89  | 3500 | 0.1454          | 0.7897    | 0.8789 | 0.8319 | 0.9664   |
| 0.0674        | 4.44  | 4000 | 0.1524          | 0.8174    | 0.8663 | 0.8411 | 0.9675   |
| 0.0689        | 5.0   | 4500 | 0.1475          | 0.8178    | 0.8687 | 0.8425 | 0.9674   |
| 0.05          | 5.56  | 5000 | 0.1628          | 0.8257    | 0.8731 | 0.8487 | 0.9676   |
| 0.0521        | 6.11  | 5500 | 0.1614          | 0.8257    | 0.8644 | 0.8446 | 0.9668   |
| 0.0409        | 6.67  | 6000 | 0.1648          | 0.8258    | 0.8740 | 0.8492 | 0.9681   |
| 0.0345        | 7.22  | 6500 | 0.1684          | 0.8295    | 0.8711 | 0.8498 | 0.9682   |
| 0.0302        | 7.78  | 7000 | 0.1727          | 0.8300    | 0.8740 | 0.8514 | 0.9687   |


### Framework versions

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