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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-en |
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results: [] |
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--- |
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# xlm-roberta-base-finetuned-panx-en |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3905 |
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- F1 Score: 0.6861 |
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## Model description |
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This model is a fine-tuned version of xlm-roberta-base on the English subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in English text. |
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## Intended uses & limitations |
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### Intended uses: |
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Named Entity Recognition (NER) tasks specifically for English. |
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Token classification tasks involving English text. |
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### Limitations: |
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The model's performance is optimized for English and may not generalize well to other languages without further fine-tuning. |
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The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively.d |
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## Training and evaluation data |
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The model was fine-tuned on the English subset of the PAN-X dataset, which includes labeled examples of named entities in English text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance |
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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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| 1.0479 | 1.0 | 50 | 0.4854 | 0.5857 | |
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| 0.4604 | 2.0 | 100 | 0.3995 | 0.6605 | |
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| 0.3797 | 3.0 | 150 | 0.3905 | 0.6861 | |
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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 |
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