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
results: []
intent_analysis
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: 0.0270
- Accuracy: 0.9961
- Precision: 0.9961
- Recall: 0.9960
- F1: 0.9961
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2718 | 1.0 | 895 | 0.0845 | 0.9843 | 0.9844 | 0.9841 | 0.9842 |
| 0.063 | 2.0 | 1790 | 0.0769 | 0.9870 | 0.9870 | 0.9868 | 0.9869 |
| 0.0416 | 3.0 | 2685 | 0.0442 | 0.9948 | 0.9948 | 0.9947 | 0.9948 |
| 0.0149 | 4.0 | 3580 | 0.0291 | 0.9961 | 0.9961 | 0.9961 | 0.9961 |
| 0.0125 | 5.0 | 4475 | 0.0270 | 0.9961 | 0.9961 | 0.9960 | 0.9961 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3