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Sentiment Analysis

Supported labeling types: Row labeling.

Sentiment analysis classifies text as “Positive” or “Negative”. The sentiment analysis model is based on DistilBERT-base-uncased-finetuned-SST-2, a fine-tuned version of DistilBERT-base-uncased, specifically trained on the SST-2 dataset.

Image of 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 achieves a 91.3% accuracy on the development set.

  • The model hosted locally within the Datasaur Intelligence container.

Usage

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

  • The base model supports masked language modeling and next sentence prediction but 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.

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