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--- |
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library_name: transformers |
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base_model: raulgdp/xml-roberta-large-finetuned-ner |
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tags: |
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- generated_from_trainer |
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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: la-xml-roberta-large-ner-finetuned-biomedical-t4 |
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results: [] |
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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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# xml-roberta-large-ner-finetuned-biomedical |
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This model is a fine-tuned version of [raulgdp/xml-roberta-large-finetuned-ner](https://huggingface.co/raulgdp/xml-roberta-large-finetuned-ner) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1139 |
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- Precision: 0.9234 |
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- Recall: 0.9548 |
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- F1: 0.9388 |
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- Accuracy: 0.9786 |
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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: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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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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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 5 |
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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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| 0.1235 | 1.0 | 2447 | 0.0949 | 0.9076 | 0.9524 | 0.9294 | 0.9738 | |
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| 0.0859 | 2.0 | 4894 | 0.1034 | 0.9222 | 0.9597 | 0.9406 | 0.9778 | |
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| 0.063 | 3.0 | 7341 | 0.1005 | 0.9330 | 0.9600 | 0.9463 | 0.9807 | |
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| 0.059 | 4.0 | 9788 | 0.1065 | 0.9350 | 0.9577 | 0.9463 | 0.9806 | |
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| 0.0513 | 5.0 | 12235 | 0.1139 | 0.9234 | 0.9548 | 0.9388 | 0.9786 | |
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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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