---
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: 0.8564668769716088
    - name: Recall
      type: recall
      value: 0.8971499380421314
    - name: F1
      type: f1
      value: 0.876336493847085
    - name: Accuracy
      type: accuracy
      value: 0.9708532522091844
---

<!-- 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: 0.2155
- Precision: 0.8565
- Recall: 0.8971
- F1: 0.8763
- Accuracy: 0.9709

## 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4393        | 1.0   | 900   | 0.1671          | 0.7756    | 0.8195 | 0.7969 | 0.9590   |
| 0.1716        | 2.0   | 1800  | 0.1409          | 0.8155    | 0.8583 | 0.8364 | 0.9662   |
| 0.1326        | 3.0   | 2700  | 0.1288          | 0.8203    | 0.8748 | 0.8467 | 0.9687   |
| 0.1027        | 4.0   | 3600  | 0.1408          | 0.8290    | 0.8732 | 0.8505 | 0.9683   |
| 0.0891        | 5.0   | 4500  | 0.1447          | 0.8485    | 0.9000 | 0.8735 | 0.9725   |
| 0.0715        | 6.0   | 5400  | 0.1393          | 0.8561    | 0.8868 | 0.8712 | 0.9713   |
| 0.0644        | 7.0   | 6300  | 0.1586          | 0.8517    | 0.8918 | 0.8713 | 0.9702   |
| 0.0535        | 8.0   | 7200  | 0.1526          | 0.8481    | 0.8810 | 0.8643 | 0.9696   |
| 0.0492        | 9.0   | 8100  | 0.1795          | 0.8529    | 0.8984 | 0.8751 | 0.9702   |
| 0.0391        | 10.0  | 9000  | 0.1903          | 0.8536    | 0.8938 | 0.8733 | 0.9693   |
| 0.0323        | 11.0  | 9900  | 0.1885          | 0.8615    | 0.9046 | 0.8825 | 0.9724   |
| 0.0274        | 12.0  | 10800 | 0.2099          | 0.8585    | 0.9025 | 0.8800 | 0.9696   |
| 0.0237        | 13.0  | 11700 | 0.1944          | 0.8624    | 0.9009 | 0.8812 | 0.9720   |
| 0.0245        | 14.0  | 12600 | 0.2129          | 0.8618    | 0.8967 | 0.8789 | 0.9711   |
| 0.0206        | 15.0  | 13500 | 0.2155          | 0.8565    | 0.8971 | 0.8763 | 0.9709   |


### Framework versions

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