---
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: 0.0270
- 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