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NLP-reviews

This model is a fine-tuned version of bert-base-uncased on the Sentiment Labelled Sentences Data Set.

Model description

Given a sentence, this model will return the probabilities of it having a positive or negative sentiment, and the probabilities that it would be a review you would find from amazon.com, imdb.com, or yelp.com.

It is a multi-label classification model which is able to determine both the sentiment of text and a grouping the text belongs to.

Training and evaluation data

The data is obtained from the procured Sentiment Labelled Sentences Data Set.

Each entry has a sentiment score: 1 for positive or 0 for negative.

The data comes from one of three different websites:

• amazon.com
• imdb.com
• yelp.com

There are 500 positive and 500 negative sentences from each website, selected randomly from a larger dataset of reviews, and were chosen based on having clear positive or negative connotation.

This was split into a 90-10 train-test split for model training and evaluation.

The code used to train the model is at https://github.com/josephtkim/huggingface-sentiment-analysis.

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: 10

Training results

Framework versions

• Transformers 4.29.1
• Pytorch 2.0.0+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3