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--- |
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library_name: transformers |
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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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datasets: |
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- biobert_json |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: xlm-roberta-finetuned-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: biobert_json |
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type: biobert_json |
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config: Biobert_json |
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split: validation |
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args: Biobert_json |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.937991068905093 |
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- name: Recall |
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type: recall |
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value: 0.9717163436200738 |
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- name: F1 |
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type: f1 |
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value: 0.9545559134836631 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9784621223416512 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-roberta-finetuned-ner |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the biobert_json dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0853 |
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- Precision: 0.9380 |
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- Recall: 0.9717 |
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- F1: 0.9546 |
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- Accuracy: 0.9785 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 306 | 0.1343 | 0.9035 | 0.9289 | 0.9160 | 0.9646 | |
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| 0.4365 | 2.0 | 612 | 0.0985 | 0.9254 | 0.9662 | 0.9453 | 0.9746 | |
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| 0.4365 | 3.0 | 918 | 0.0833 | 0.9413 | 0.9684 | 0.9547 | 0.9788 | |
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| 0.0949 | 4.0 | 1224 | 0.0853 | 0.9380 | 0.9717 | 0.9546 | 0.9785 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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