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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: xlm-roberta-base-finetuned-panx-all |
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results: [] |
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language: |
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- en |
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- de |
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- it |
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- fr |
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metrics: |
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- f1 |
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library_name: transformers |
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--- |
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# xlm-roberta-base-finetuned-panx-all |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the XTREME PANX dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1758 |
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- F1 Score: 0.8558 |
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## Model description |
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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. |
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## Intended uses |
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Named Entity Recognition (NER) tasks across multiple languages. |
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Token classification tasks that benefit from multilingual training data. |
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## Limitations |
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Performance may vary on languages not seen during training. |
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The model is fine-tuned on specific datasets and may require further fine-tuning or adjustments for other tasks or domains. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.299 | 1.0 | 835 | 0.2074 | 0.8078 | |
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| 0.1587 | 2.0 | 1670 | 0.1705 | 0.8461 | |
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| 0.1012 | 3.0 | 2505 | 0.1758 | 0.8558 | |
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### Evaluation results |
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The model was evaluated on multiple languages, achieving the following F1-scores: |
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| Evaluated on | de | fr | it | en | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| Fine-tune on | | | | | |
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| de |0.8658 | 0.7021 | 0.6877 | 0.5830 | |
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| each |0.8658 | 0.8411 | 0.8180 | 0.6870 | |
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| all |0.8685 | 0.8654 | 0.8669 | 0.7678 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
