metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLM-Roberta_NER
results: []
XLM-Roberta_NER
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0251
- Precision: 0.9509
- Recall: 0.9721
- F1: 0.9614
- Accuracy: 0.9947
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 OptimizerNames.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 |
|---|---|---|---|---|---|---|---|
| 0.1665 | 1.0 | 5313 | 0.1461 | 0.7095 | 0.7489 | 0.7287 | 0.9649 |
| 0.0816 | 2.0 | 10626 | 0.0652 | 0.8559 | 0.8845 | 0.8700 | 0.9854 |
| 0.037 | 3.0 | 15939 | 0.0428 | 0.8999 | 0.9380 | 0.9186 | 0.9901 |
| 0.0245 | 4.0 | 21252 | 0.0283 | 0.9463 | 0.9640 | 0.9551 | 0.9941 |
| 0.0193 | 5.0 | 26565 | 0.0251 | 0.9509 | 0.9721 | 0.9614 | 0.9947 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2