README.md

---
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: []
---

<!-- 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

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: 0.0133
- Accuracy: 0.9986
- Precision: 0.9982
- Recall: 0.9983
- F1: 0.9982

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1768        | 1.0   | 729  | 0.0408          | 0.9914   | 0.9939    | 0.9896 | 0.9917 |
| 0.0575        | 2.0   | 1458 | 0.0392          | 0.99     | 0.9885    | 0.9879 | 0.9880 |
| 0.0258        | 3.0   | 2187 | 0.0133          | 0.9986   | 0.9982    | 0.9983 | 0.9982 |
| 0.01          | 4.0   | 2916 | 0.0151          | 0.9986   | 0.9982    | 0.9983 | 0.9982 |
| 0.0044        | 5.0   | 3645 | 0.0133          | 0.9986   | 0.9982    | 0.9983 | 0.9982 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3