---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: GPT2-small-finetuned-amazon-reviews-en-classification
  results: []
datasets:
- mteb/amazon_reviews_multi
language:
- en
widget:
- text: It`s an amazing product
- text: I hate this product
- text: It's ok, but a bit expensive
library_name: transformers
pipeline_tag: text-classification
---

<!-- 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. -->

# GPT2-small-finetuned-amazon-reviews-en-classification

This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on [mteb/amazon_reviews_multi](https://huggingface.co/datasets/mteb/amazon_reviews_multi) dataset.

It is the result of the post [Fine tunning SML](https://maximofn.com/fine-tuning-sml/)

It achieves the following results on the evaluation set:
- Loss: 0.7974
- Accuracy: 0.6626

## Model description

This model provides classification of reviews in english

## Intended uses & limitations

Classifiction of reviews in english

## Training and evaluation data

It is training on [mteb/amazon_reviews_multi](https://huggingface.co/datasets/mteb/amazon_reviews_multi) dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 28
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8074        | 1.0   | 7143  | 0.8203          | 0.652    |
| 0.7519        | 2.0   | 14286 | 0.8022          | 0.6546   |
| 0.7181        | 3.0   | 21429 | 0.8102          | 0.6578   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1