---
license: apache-2.0
base_model: distilbert-base-uncased-finetuned-sst-2-english
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: 0.9151376146788991
---

<!-- 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-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3690
- Accuracy: 0.9151

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1335        | 0.06  | 500  | 0.5579          | 0.8911   |
| 0.1666        | 0.12  | 1000 | 0.5413          | 0.8876   |
| 0.1778        | 0.18  | 1500 | 0.7077          | 0.8544   |
| 0.1746        | 0.24  | 2000 | 0.5727          | 0.875    |
| 0.1632        | 0.3   | 2500 | 0.4972          | 0.8979   |
| 0.1675        | 0.36  | 3000 | 0.4742          | 0.8991   |
| 0.1573        | 0.42  | 3500 | 0.4943          | 0.8956   |
| 0.1525        | 0.48  | 4000 | 0.4907          | 0.8819   |
| 0.1394        | 0.53  | 4500 | 0.5010          | 0.8899   |
| 0.1458        | 0.59  | 5000 | 0.5461          | 0.8876   |
| 0.1588        | 0.65  | 5500 | 0.3364          | 0.9094   |
| 0.1373        | 0.71  | 6000 | 0.4198          | 0.9163   |
| 0.138         | 0.77  | 6500 | 0.3466          | 0.9128   |
| 0.1383        | 0.83  | 7000 | 0.4064          | 0.9094   |
| 0.1371        | 0.89  | 7500 | 0.4083          | 0.9002   |
| 0.1373        | 0.95  | 8000 | 0.3690          | 0.9151   |


### Framework versions

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