Sentiment Analysis

Supported Labeling Types: Row Labeling

Sentiment analysis will provide a simple classification between “Positive” or “Negative”.

The model used for sentiment analysis is sourced from DistilBERT-base-uncased-finetuned-SST-2. This model is a fine-tuned checkpoint of DistilBERT-base-uncased, specifically trained on the SST-2 dataset.

ML Assisted with Sentiment Analysis

Model Details

• The model is using a distilled version developed by Hugging Face based on the Text Classification task pipeline.

• Trained on Stanford Sentiment Treebank (sst2) corpora which contains 67,349 movie review excerpts with human-annotated sentiment labels.

• The model reaches an accuracy of 91.3% on the development set.

• The model hosted locally within the Datasaur Intelligence container.

Usage

• This model is primarily used for sentiment classification and also be used for topic classification.

• The raw model supports masked language modeling and next sentence prediction, though it is primarily intended for fine-tuning on downstream tasks.

• To explore additional fine-tuned versions for different tasks, check out the Hugging Face model hub.