transformers safetensors xlm-roberta token-classification generated_from_trainer en de it fr base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-base
language:
- en
- de
- it
- fr
library_name: transformers
license: mit
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-panx-all
  results: []
---

# xlm-roberta-base-finetuned-panx-all

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the XTREME PANX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1758
- F1 Score: 0.8558

## Model description

This model is a fine-tuned version of xlm-roberta-base on a concatenated dataset combining multiple languages, specifically German (de) and French (fr). The model has been trained for token classification tasks and achieves competitive F1-scores across various languages.

## Intended uses

Named Entity Recognition (NER) tasks across multiple languages.
Token classification tasks that benefit from multilingual training data.

## Limitations

Performance may vary on languages not seen during training.
The model is fine-tuned on specific datasets and may require further fine-tuning or adjustments for other tasks or domains.

## Training and evaluation data

The model was fine-tuned on a combination of German and French datasets, with the training data shuffled and concatenated to form a multilingual corpus. Additionally, the model was evaluated on multiple languages, showing robust performance across different linguistic datasets.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.299         | 1.0   | 835  | 0.2074          | 0.8078   |
| 0.1587        | 2.0   | 1670 | 0.1705          | 0.8461   |
| 0.1012        | 3.0   | 2505 | 0.1758          | 0.8558   |

### Evaluation results

The model was evaluated on multiple languages, achieving the following F1-scores:

| Evaluated on  | de    | fr   |  it             | en       |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| Fine-tune on  |       |      |                 |          |
| de            |0.8658 | 0.7021 | 0.6877        | 0.5830   |
| each          |0.8658 | 0.8411 | 0.8180        | 0.6870   |
| all           |0.8685 | 0.8654 | 0.8669        | 	0.7678  |

### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1