metadata
library_name: transformers
base_model: raulgdp/xml-roberta-large-finetuned-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: la-xml-roberta-large-ner-finetuned-biomedical
results: []
xml-roberta-large-ner-finetuned-biomedical
This model is a fine-tuned version of raulgdp/xml-roberta-large-finetuned-ner on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0856
- Precision: 0.9255
- Recall: 0.9564
- F1: 0.9407
- Accuracy: 0.9788
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.6562 | 1.0 | 612 | 0.0902 | 0.9225 | 0.9397 | 0.9310 | 0.9740 |
| 0.1069 | 2.0 | 1224 | 0.0833 | 0.9143 | 0.9550 | 0.9342 | 0.9771 |
| 0.0788 | 3.0 | 1836 | 0.0873 | 0.9242 | 0.9576 | 0.9406 | 0.9785 |
| 0.0619 | 4.0 | 2448 | 0.0863 | 0.9282 | 0.9557 | 0.9417 | 0.9790 |
| 0.0466 | 5.0 | 3060 | 0.0856 | 0.9255 | 0.9564 | 0.9407 | 0.9788 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3