---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
model-index:
- name: distilbert_base_SST2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sst2
      type: sst2
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8990825688073395
---

<!-- 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. -->

# distilbert_base_SST2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4181
- Accuracy: 0.8991

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4378        | 0.06  | 500   | 0.3452          | 0.8601   |
| 0.343         | 0.12  | 1000  | 0.3483          | 0.8578   |
| 0.3342        | 0.18  | 1500  | 0.3373          | 0.8704   |
| 0.308         | 0.24  | 2000  | 0.4102          | 0.8819   |
| 0.2932        | 0.3   | 2500  | 0.3546          | 0.8830   |
| 0.3116        | 0.36  | 3000  | 0.3609          | 0.8716   |
| 0.2805        | 0.42  | 3500  | 0.3800          | 0.8945   |
| 0.2655        | 0.48  | 4000  | 0.4131          | 0.8842   |
| 0.2504        | 0.53  | 4500  | 0.4299          | 0.8830   |
| 0.2543        | 0.59  | 5000  | 0.5196          | 0.8727   |
| 0.2409        | 0.65  | 5500  | 0.4387          | 0.8807   |
| 0.2414        | 0.71  | 6000  | 0.4121          | 0.8922   |
| 0.2319        | 0.77  | 6500  | 0.3772          | 0.8830   |
| 0.247         | 0.83  | 7000  | 0.4179          | 0.8876   |
| 0.2233        | 0.89  | 7500  | 0.3544          | 0.8945   |
| 0.2202        | 0.95  | 8000  | 0.4160          | 0.8865   |
| 0.2242        | 1.01  | 8500  | 0.5125          | 0.8784   |
| 0.1296        | 1.07  | 9000  | 0.4212          | 0.8842   |
| 0.1429        | 1.13  | 9500  | 0.4675          | 0.8968   |
| 0.1466        | 1.19  | 10000 | 0.5034          | 0.8922   |
| 0.1626        | 1.25  | 10500 | 0.4431          | 0.8945   |
| 0.1459        | 1.31  | 11000 | 0.5001          | 0.8922   |
| 0.1489        | 1.37  | 11500 | 0.4739          | 0.8968   |
| 0.1591        | 1.43  | 12000 | 0.3852          | 0.8945   |
| 0.1211        | 1.48  | 12500 | 0.4648          | 0.8945   |
| 0.1275        | 1.54  | 13000 | 0.5281          | 0.8956   |
| 0.1302        | 1.6   | 13500 | 0.4411          | 0.8933   |
| 0.1313        | 1.66  | 14000 | 0.4914          | 0.8979   |
| 0.134         | 1.72  | 14500 | 0.3923          | 0.8979   |
| 0.1355        | 1.78  | 15000 | 0.4164          | 0.8956   |
| 0.1263        | 1.84  | 15500 | 0.4293          | 0.8945   |
| 0.1326        | 1.9   | 16000 | 0.4185          | 0.8933   |
| 0.1315        | 1.96  | 16500 | 0.4181          | 0.8991   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0