---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-sst2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: sst2
      split: train
      args: sst2
    metrics:
    - type: accuracy
      value: 0.9071100917431193
      name: Accuracy
---

<!-- 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-uncased-finetuned-sst2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2842
- Accuracy: 0.9071

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.02  | 100  | 0.3316          | 0.8624   |
| No log        | 0.05  | 200  | 0.3357          | 0.8612   |
| No log        | 0.07  | 300  | 0.3996          | 0.8383   |
| No log        | 0.1   | 400  | 0.3012          | 0.8716   |
| 0.3421        | 0.12  | 500  | 0.3227          | 0.8693   |
| 0.3421        | 0.14  | 600  | 0.3643          | 0.8727   |
| 0.3421        | 0.17  | 700  | 0.2734          | 0.8853   |
| 0.3421        | 0.19  | 800  | 0.3077          | 0.8945   |
| 0.3421        | 0.21  | 900  | 0.2709          | 0.9002   |
| 0.2705        | 0.24  | 1000 | 0.2737          | 0.8899   |
| 0.2705        | 0.26  | 1100 | 0.3079          | 0.8979   |
| 0.2705        | 0.29  | 1200 | 0.2713          | 0.8968   |
| 0.2705        | 0.31  | 1300 | 0.2505          | 0.8933   |
| 0.2705        | 0.33  | 1400 | 0.2932          | 0.8922   |
| 0.239         | 0.36  | 1500 | 0.2842          | 0.9071   |
| 0.239         | 0.38  | 1600 | 0.2509          | 0.9014   |
| 0.239         | 0.4   | 1700 | 0.2819          | 0.8853   |
| 0.239         | 0.43  | 1800 | 0.2515          | 0.8956   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2