---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-sst-2-64-13-30
  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-sst-2-64-13-30

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6400
- Accuracy: 0.8984

## 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: 1.5e-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
- lr_scheduler_warmup_steps: 5
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 4    | 0.6936          | 0.5      |
| No log        | 2.0   | 8    | 0.6928          | 0.5156   |
| 0.6938        | 3.0   | 12   | 0.6921          | 0.6328   |
| 0.6938        | 4.0   | 16   | 0.6911          | 0.6328   |
| 0.6895        | 5.0   | 20   | 0.6894          | 0.5859   |
| 0.6895        | 6.0   | 24   | 0.6866          | 0.625    |
| 0.6895        | 7.0   | 28   | 0.6818          | 0.6641   |
| 0.6758        | 8.0   | 32   | 0.6727          | 0.6953   |
| 0.6758        | 9.0   | 36   | 0.6495          | 0.7656   |
| 0.615         | 10.0  | 40   | 0.5773          | 0.8125   |
| 0.615         | 11.0  | 44   | 0.4229          | 0.875    |
| 0.615         | 12.0  | 48   | 0.3311          | 0.8906   |
| 0.3514        | 13.0  | 52   | 0.3047          | 0.8906   |
| 0.3514        | 14.0  | 56   | 0.3420          | 0.8828   |
| 0.0929        | 15.0  | 60   | 0.4113          | 0.8906   |
| 0.0929        | 16.0  | 64   | 0.4550          | 0.8906   |
| 0.0929        | 17.0  | 68   | 0.5299          | 0.8906   |
| 0.0206        | 18.0  | 72   | 0.6554          | 0.8594   |
| 0.0206        | 19.0  | 76   | 0.7213          | 0.8594   |
| 0.007         | 20.0  | 80   | 0.7860          | 0.8516   |
| 0.007         | 21.0  | 84   | 0.8466          | 0.8438   |
| 0.007         | 22.0  | 88   | 0.8522          | 0.8516   |
| 0.0037        | 23.0  | 92   | 0.8023          | 0.8516   |
| 0.0037        | 24.0  | 96   | 0.6670          | 0.8828   |
| 0.0028        | 25.0  | 100  | 0.6224          | 0.8984   |
| 0.0028        | 26.0  | 104  | 0.6283          | 0.8906   |
| 0.0028        | 27.0  | 108  | 0.6333          | 0.8906   |
| 0.0026        | 28.0  | 112  | 0.6307          | 0.8906   |
| 0.0026        | 29.0  | 116  | 0.6348          | 0.8984   |
| 0.003         | 30.0  | 120  | 0.6400          | 0.8984   |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3