---
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: 0.8615819209039548
    - name: Recall
      type: recall
      value: 0.8818669971086328
    - name: F1
      type: f1
      value: 0.8716064502959787
    - name: Accuracy
      type: accuracy
      value: 0.9709691438504998
---

<!-- 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: 0.1178
- Precision: 0.8616
- Recall: 0.8819
- F1: 0.8716
- Accuracy: 0.9710

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 225  | 0.1357          | 0.7953    | 0.8315 | 0.8130 | 0.9620   |
| No log        | 2.0   | 450  | 0.1056          | 0.8245    | 0.8691 | 0.8462 | 0.9687   |
| 0.21          | 3.0   | 675  | 0.1064          | 0.8487    | 0.8831 | 0.8656 | 0.9698   |
| 0.21          | 4.0   | 900  | 0.1198          | 0.8442    | 0.8839 | 0.8636 | 0.9704   |
| 0.0589        | 5.0   | 1125 | 0.1178          | 0.8616    | 0.8819 | 0.8716 | 0.9710   |


### Framework versions

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