metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: biobert_json
type: biobert_json
config: Biobert_json
split: validation
args: Biobert_json
metrics:
- name: Precision
type: precision
value: 0.9391471552991743
- name: Recall
type: recall
value: 0.9724190431574633
- name: F1
type: f1
value: 0.9554935412411175
- name: Accuracy
type: accuracy
value: 0.9793838188053188
xlm-roberta-finetuned-ner
This model is a fine-tuned version of xlm-roberta-base on the biobert_json dataset. It achieves the following results on the evaluation set:
- Loss: 0.0847
- Precision: 0.9391
- Recall: 0.9724
- F1: 0.9555
- Accuracy: 0.9794
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: 32
- eval_batch_size: 32
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 306 | 0.1266 | 0.9112 | 0.9321 | 0.9215 | 0.9664 |
| 0.4341 | 2.0 | 612 | 0.0979 | 0.9275 | 0.9662 | 0.9465 | 0.9739 |
| 0.4341 | 3.0 | 918 | 0.0868 | 0.9379 | 0.9690 | 0.9532 | 0.9775 |
| 0.0949 | 4.0 | 1224 | 0.0834 | 0.9396 | 0.9719 | 0.9555 | 0.9791 |
| 0.07 | 5.0 | 1530 | 0.0847 | 0.9391 | 0.9724 | 0.9555 | 0.9794 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3