---
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: 0.0166
- Accuracy: 0.9971
- Precision: 0.9971
- Recall: 0.9971
- F1: 0.9971

## 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  | 0.0492          | 0.9875   | 0.9875    | 0.9875 | 0.9875 |
| No log        | 2.0   | 428  | 0.0385          | 0.9899   | 0.9901    | 0.9899 | 0.9899 |
| 0.6012        | 3.0   | 642  | 0.0265          | 0.9935   | 0.9935    | 0.9935 | 0.9935 |
| 0.6012        | 4.0   | 856  | 0.0199          | 0.9966   | 0.9966    | 0.9966 | 0.9966 |
| 0.0105        | 5.0   | 1070 | 0.0166          | 0.9971   | 0.9971    | 0.9971 | 0.9971 |


### Framework versions

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