metadata
library_name: transformers
base_model: raulgdp/xml-roberta-large-finetuned-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xml-roberta-large-finetuned-ner-biobert
results: []
xml-roberta-large-finetuned-ner-biobert
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: 0.0847
- Precision: 0.9493
- Recall: 0.9728
- F1: 0.9609
- Accuracy: 0.9815
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|---|---|---|---|---|---|---|---|
| 0.5713 | 1.0 | 612 | 0.1009 | 0.9281 | 0.9603 | 0.9439 | 0.9729 |
| 0.1048 | 2.0 | 1224 | 0.0903 | 0.9350 | 0.9730 | 0.9536 | 0.9779 |
| 0.0743 | 3.0 | 1836 | 0.0783 | 0.9520 | 0.9745 | 0.9631 | 0.9823 |
| 0.0568 | 4.0 | 2448 | 0.0855 | 0.9474 | 0.9712 | 0.9591 | 0.9802 |
| 0.0361 | 5.0 | 3060 | 0.0847 | 0.9493 | 0.9728 | 0.9609 | 0.9815 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3