metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: intent_analysis_V1_TOTAL
results: []
intent_analysis_V1_TOTAL
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: 0.0166
- Accuracy: 0.9971
- Precision: 0.9971
- Recall: 0.9971
- F1: 0.9971
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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 | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 214 | 0.0492 | 0.9875 | 0.9875 | 0.9875 | 0.9875 |
| No log | 2.0 | 428 | 0.0385 | 0.9899 | 0.9901 | 0.9899 | 0.9899 |
| 0.6012 | 3.0 | 642 | 0.0265 | 0.9935 | 0.9935 | 0.9935 | 0.9935 |
| 0.6012 | 4.0 | 856 | 0.0199 | 0.9966 | 0.9966 | 0.9966 | 0.9966 |
| 0.0105 | 5.0 | 1070 | 0.0166 | 0.9971 | 0.9971 | 0.9971 | 0.9971 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3