---
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: 0.8317152103559871
    - name: Recall
      type: recall
      value: 0.8682432432432432
    - name: F1
      type: f1
      value: 0.8495867768595041
    - name: Accuracy
      type: accuracy
      value: 0.9680139069969579
---

<!-- 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: 0.2072
- Precision: 0.8317
- Recall: 0.8682
- F1: 0.8496
- Accuracy: 0.9680

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2727        | 1.11  | 500  | 0.1414          | 0.7268    | 0.8012 | 0.7622 | 0.9594   |
| 0.1146        | 2.22  | 1000 | 0.1338          | 0.7697    | 0.8581 | 0.8115 | 0.9657   |
| 0.0725        | 3.33  | 1500 | 0.1444          | 0.7953    | 0.8625 | 0.8275 | 0.9668   |
| 0.0492        | 4.44  | 2000 | 0.1513          | 0.8085    | 0.8760 | 0.8409 | 0.9675   |
| 0.0388        | 5.56  | 2500 | 0.1604          | 0.8257    | 0.8731 | 0.8487 | 0.9674   |
| 0.0244        | 6.67  | 3000 | 0.1754          | 0.8278    | 0.8629 | 0.8450 | 0.9666   |
| 0.0169        | 7.78  | 3500 | 0.1877          | 0.8282    | 0.8653 | 0.8464 | 0.9677   |
| 0.0102        | 8.89  | 4000 | 0.1974          | 0.8252    | 0.8634 | 0.8439 | 0.9674   |
| 0.0068        | 10.0  | 4500 | 0.2072          | 0.8317    | 0.8682 | 0.8496 | 0.9680   |


### Framework versions

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