---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC1_1_62types_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: 0.8268475544448411
    - name: Recall
      type: recall
      value: 0.8836322014498283
    - name: F1
      type: f1
      value: 0.8542973072666913
    - name: Accuracy
      type: accuracy
      value: 0.955501776025606
---

<!-- 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. -->

# CNEC1_1_62types_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: 0.2611
- Precision: 0.8268
- Recall: 0.8836
- F1: 0.8543
- Accuracy: 0.9555

## 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
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.601         | 1.7   | 500  | 0.4369          | 0.6449    | 0.6810 | 0.6625 | 0.9094   |
| 0.3697        | 3.4   | 1000 | 0.2542          | 0.7377    | 0.8176 | 0.7756 | 0.9440   |
| 0.2295        | 5.1   | 1500 | 0.2484          | 0.7583    | 0.8523 | 0.8026 | 0.9449   |
| 0.1531        | 6.8   | 2000 | 0.2301          | 0.7952    | 0.8710 | 0.8314 | 0.9496   |
| 0.1107        | 8.5   | 2500 | 0.2284          | 0.8192    | 0.8729 | 0.8452 | 0.9529   |
| 0.0801        | 10.2  | 3000 | 0.2435          | 0.8308    | 0.8901 | 0.8595 | 0.9561   |
| 0.0621        | 11.9  | 3500 | 0.2507          | 0.8156    | 0.8794 | 0.8463 | 0.9544   |
| 0.0454        | 13.61 | 4000 | 0.2611          | 0.8268    | 0.8836 | 0.8543 | 0.9555   |


### Framework versions

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