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-ner-finetuned-biomedical-conT4-16
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: 0.0755
- Precision: 0.9291
- Recall: 0.9569
- F1: 0.9428
- Accuracy: 0.9798
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 306 | 0.1016 | 0.9102 | 0.9308 | 0.9204 | 0.9711 |
| 0.6706 | 2.0 | 612 | 0.0809 | 0.9237 | 0.9598 | 0.9414 | 0.9784 |
| 0.6706 | 3.0 | 918 | 0.0696 | 0.9371 | 0.9612 | 0.9490 | 0.9817 |
| 0.079 | 4.0 | 1224 | 0.0738 | 0.9318 | 0.9582 | 0.9448 | 0.9803 |
| 0.0564 | 5.0 | 1530 | 0.0755 | 0.9291 | 0.9569 | 0.9428 | 0.9798 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3