---
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: []
---

<!-- 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. -->

# intent_analysis_V1_TOTAL

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: 0.0167
- 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