---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: FULL-12epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news
  results: []
---

<!-- 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. -->

# FULL-12epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0687
- Accuracy: 0.3222
- Precision: 0.6358
- Recall: 0.8475
- F1: 0.6074

## 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.087         | 1.0   | 1563  | 0.0917          | 0.3188   | 0.7203    | 0.8273 | 0.6562 |
| 0.08          | 2.0   | 3126  | 0.0747          | 0.3204   | 0.7147    | 0.8331 | 0.6569 |
| 0.0666        | 3.0   | 4689  | 0.0691          | 0.3211   | 0.7195    | 0.8376 | 0.6624 |
| 0.0583        | 4.0   | 6252  | 0.0667          | 0.3213   | 0.6889    | 0.8419 | 0.6433 |
| 0.0514        | 5.0   | 7815  | 0.0650          | 0.3216   | 0.7043    | 0.8433 | 0.6543 |
| 0.0463        | 6.0   | 9378  | 0.0642          | 0.3219   | 0.6780    | 0.8444 | 0.6362 |
| 0.0421        | 7.0   | 10941 | 0.0635          | 0.3220   | 0.6759    | 0.8458 | 0.6354 |
| 0.0385        | 8.0   | 12504 | 0.0644          | 0.3220   | 0.6330    | 0.8470 | 0.6066 |
| 0.0358        | 9.0   | 14067 | 0.0670          | 0.3221   | 0.6368    | 0.8467 | 0.6068 |
| 0.0331        | 10.0  | 15630 | 0.0676          | 0.3222   | 0.6442    | 0.8468 | 0.6130 |
| 0.0309        | 11.0  | 17193 | 0.0680          | 0.3222   | 0.6377    | 0.8472 | 0.6092 |
| 0.0298        | 12.0  | 18756 | 0.0687          | 0.3222   | 0.6358    | 0.8475 | 0.6074 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.19.2
- Tokenizers 0.19.1