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---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: GPT2-small-LoRA-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-LoRA-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/lora/)
It achieves the following results on the evaluation set:
- Loss: 1.5203
- Accuracy: 0.3374
## 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: 400
- eval_batch_size: 400
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3964 | 1.0 | 500 | 1.6029 | 0.2694 |
| 1.5727 | 2.0 | 1000 | 1.5317 | 0.3208 |
| 1.5344 | 3.0 | 1500 | 1.5118 | 0.3358 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |