---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-base-finetuned-3d-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. -->

# roberta-base-finetuned-3d-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: 0.5817
- Accuracy: 0.7753
- Precision: 0.7757
- Recall: 0.7753
- F1: 0.7745

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6381
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7758        | 1.0   | 1595  | 0.7691          | 0.7069   | 0.7256    | 0.7069 | 0.7052 |
| 0.5496        | 2.0   | 3190  | 0.6961          | 0.7255   | 0.7441    | 0.7255 | 0.7252 |
| 0.4856        | 3.0   | 4785  | 0.6451          | 0.7368   | 0.7562    | 0.7368 | 0.7328 |
| 0.4257        | 4.0   | 6380  | 0.5817          | 0.7753   | 0.7757    | 0.7753 | 0.7745 |
| 0.351         | 5.0   | 7975  | 0.6637          | 0.7633   | 0.7717    | 0.7633 | 0.7637 |
| 0.2551        | 6.0   | 9570  | 0.7646          | 0.7696   | 0.7738    | 0.7696 | 0.7699 |
| 0.1845        | 7.0   | 11165 | 0.8529          | 0.7674   | 0.7730    | 0.7674 | 0.7680 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3