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: 0.0167
- Accuracy: 0.9969
- Precision: 0.9969
- Recall: 0.9969
- F1: 0.9969
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 | 0.0432 | 0.9899 | 0.9899 | 0.9899 | 0.9899 |
| No log | 2.0 | 428 | 0.0252 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
| 0.0885 | 3.0 | 642 | 0.0263 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
| 0.0885 | 4.0 | 856 | 0.0222 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
| 0.0086 | 5.0 | 1070 | 0.0167 | 0.9969 | 0.9969 | 0.9969 | 0.9969 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3