---
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: result
  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. -->

# result

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5662
- Accuracy: 0.8065

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5234        | 1.0   | 6463  | 0.5311          | 0.7852   |
| 0.4135        | 2.0   | 12926 | 0.5020          | 0.8039   |
| 0.3246        | 3.0   | 19389 | 0.5662          | 0.8065   |




### Testing results

        precision    recall  f1-score   support

           0      0.815     0.821     0.818      4449
           1      0.752     0.773     0.762      4071
           2      0.852     0.823     0.837      4245

    accuracy                          0.806     12765
   macro avg      0.806     0.806     0.806     12765
weighted avg      0.807     0.806     0.807     12765
### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.14.1