|
|
--- |
|
|
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: [] |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
# XLM-Roberta_NER |
|
|
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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 |
|
|
|
