---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: tmp
  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. -->

# tmp

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: 0.6486
- Precision: 0.6540
- Recall: 0.6944
- F1: 0.6736

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log        | 1.0   | 38   | 0.7892          | 0.5800    | 0.6787 | 0.6255 |
| No log        | 2.0   | 76   | 0.5906          | 0.7267    | 0.7540 | 0.7401 |
| No log        | 3.0   | 114  | 0.5466          | 0.7219    | 0.7771 | 0.7485 |
| No log        | 4.0   | 152  | 0.5249          | 0.7266    | 0.7623 | 0.7440 |
| No log        | 5.0   | 190  | 0.5261          | 0.7228    | 0.7674 | 0.7445 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2