gpt2-amazon-sentiment-classifier-V1.0
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0320
- Accuracy: 0.9680
- F1: 0.9680
Model description
Hi! I'd be happy to share some insights about the Amazon Sentiment Analysis model I developed. The model is based on GPT-2, a transformer-based language model, which I fine-tuned using Amazon user reviews from 2023. The purpose of fine-tuning GPT-2 was to adapt it specifically for understanding and generating text related to sentiment analysis in Amazon reviews.
During the fine-tuning process, I trained the model to recognize different sentiments (positive, negative, neutral) by leveraging real user feedback. The fine-tuned GPT-2 model can now predict the sentiment of new reviews by generating relevant responses or categorizing them based on the emotions conveyed in the text.
You can use my model by using API
import transformers
from transformers import pipeline
sentiment_model = pipeline(model="ashok2216/gpt2-amazon-sentiment-classifier")
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: 2
Training results
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
