---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: FULL-3epoch-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-3epoch-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: 0.2571
- Accuracy: 0.3011
- Precision: 0.7306
- Recall: 0.6846
- F1: 0.5945

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.9923 | 97   | 0.5290          | 0.2637   | 0.6408    | 0.5061 | 0.4053 |
| No log        | 1.9949 | 195  | 0.2969          | 0.2962   | 0.7163    | 0.6474 | 0.5560 |
| No log        | 2.9770 | 291  | 0.2571          | 0.3011   | 0.7306    | 0.6846 | 0.5945 |


### Framework versions

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