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-t4
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: 0.1139
- Precision: 0.9234
- Recall: 0.9548
- F1: 0.9388
- Accuracy: 0.9786
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1235 | 1.0 | 2447 | 0.0949 | 0.9076 | 0.9524 | 0.9294 | 0.9738 |
| 0.0859 | 2.0 | 4894 | 0.1034 | 0.9222 | 0.9597 | 0.9406 | 0.9778 |
| 0.063 | 3.0 | 7341 | 0.1005 | 0.9330 | 0.9600 | 0.9463 | 0.9807 |
| 0.059 | 4.0 | 9788 | 0.1065 | 0.9350 | 0.9577 | 0.9463 | 0.9806 |
| 0.0513 | 5.0 | 12235 | 0.1139 | 0.9234 | 0.9548 | 0.9388 | 0.9786 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3