---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: twitter-roberta-base-sentiment
  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. -->

# twitter-roberta-base-sentiment

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9462
- Accuracy: 0.7222
- Macro Precision: 0.7068
- Macro Recall: 0.7491
- Macro F1: 0.7246

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|
| 0.9337        | 0.2667 | 1000 | 0.8398          | 0.6273   | 0.6577          | 0.6723       | 0.6322   |
| 0.8101        | 0.5333 | 2000 | 0.7526          | 0.6780   | 0.6598          | 0.7406       | 0.6851   |
| 0.7097        | 0.8    | 3000 | 0.8075          | 0.7068   | 0.6853          | 0.7515       | 0.7081   |
| 0.5513        | 1.0667 | 4000 | 0.8310          | 0.7113   | 0.7007          | 0.7316       | 0.7135   |
| 0.4368        | 1.3333 | 5000 | 0.9000          | 0.7154   | 0.7001          | 0.7487       | 0.7192   |
| 0.4084        | 1.6    | 6000 | 0.9042          | 0.7154   | 0.7035          | 0.7413       | 0.7194   |
| 0.3481        | 1.8667 | 7000 | 0.9868          | 0.7246   | 0.7121          | 0.7441       | 0.7255   |
| 0.3693        | 2.0    | 7500 | 0.9462          | 0.7222   | 0.7068          | 0.7491       | 0.7246   |


### Framework versions

- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2