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

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8764
- Accuracy: 0.8828

## 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    | 0.7179          | 0.5      |
| No log        | 2.0   | 8    | 0.6981          | 0.5312   |
| 0.717         | 3.0   | 12   | 0.6948          | 0.4688   |
| 0.717         | 4.0   | 16   | 0.7043          | 0.4453   |
| 0.6986        | 5.0   | 20   | 0.6971          | 0.4688   |
| 0.6986        | 6.0   | 24   | 0.7705          | 0.5156   |
| 0.6986        | 7.0   | 28   | 0.7463          | 0.625    |
| 0.6087        | 8.0   | 32   | 0.7016          | 0.6172   |
| 0.6087        | 9.0   | 36   | 0.5869          | 0.7656   |
| 0.5365        | 10.0  | 40   | 0.5156          | 0.8047   |
| 0.5365        | 11.0  | 44   | 0.4578          | 0.8203   |
| 0.5365        | 12.0  | 48   | 0.3511          | 0.9141   |
| 0.3599        | 13.0  | 52   | 0.3583          | 0.8828   |
| 0.3599        | 14.0  | 56   | 0.3759          | 0.8828   |
| 0.1271        | 15.0  | 60   | 0.4324          | 0.8906   |
| 0.1271        | 16.0  | 64   | 0.4806          | 0.8984   |
| 0.1271        | 17.0  | 68   | 0.5256          | 0.875    |
| 0.0516        | 18.0  | 72   | 0.6432          | 0.8906   |
| 0.0516        | 19.0  | 76   | 0.6976          | 0.875    |
| 0.0034        | 20.0  | 80   | 0.8148          | 0.875    |
| 0.0034        | 21.0  | 84   | 0.8401          | 0.8828   |
| 0.0034        | 22.0  | 88   | 0.8721          | 0.8828   |
| 0.0467        | 23.0  | 92   | 0.8001          | 0.8906   |
| 0.0467        | 24.0  | 96   | 0.8580          | 0.8828   |
| 0.0005        | 25.0  | 100  | 0.8849          | 0.875    |
| 0.0005        | 26.0  | 104  | 0.9024          | 0.875    |
| 0.0005        | 27.0  | 108  | 0.9125          | 0.875    |
| 0.0005        | 28.0  | 112  | 0.8686          | 0.8828   |
| 0.0005        | 29.0  | 116  | 0.8764          | 0.8828   |
| 0.0231        | 30.0  | 120  | 0.8764          | 0.8828   |


### Framework versions

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