Sentiment Analysis with Hugging Face Models

Learn how sentiment analysis is performed with various Hugging Face models.

We'll cover the following

DistilBERT for SST
- Code playground
MiniLM-L12-H384-uncased
- Code playground
RoBERTa-large-mnli
- Code playground
BERT-base multilingual model
- Code playground

This lesson will explore Hugging Face’s transformer models list and enter some samples to evaluate their results. The idea is to test several models, not only one, and see which model fits our needs the best for a given project.

We will be running Hugging Face models. For each model we use, we can find the description of the model in the documentation provided by Hugging Face.

We will test several models. If you implement them, you might find that they require fine-tuning or even pretraining for the NLP tasks you wish to perform.

First let’s go through the list of Hugging Face models.

Then, select Text Classification in the Tasks pane:

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Course Introduction

Basics of Transformers

Architecture of the Transformer Model

Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

Machine Translation with the Transformer

The Rise of Transformers with GPT-3 Engines

Applying Transformers for AI Text Summarization

Matching Tokenizers and Datasets

Transformers for NLP—Exam 1

Semantic Role Labeling with BERT-Based Transformers

Your Data Speaks: Story, Questions, and Answers

Detecting Customer Emotions to Make Predictions

Analyzing Fake News with Transformers

Interpreting Black Box Transformer Models

From NLP to Task-Agnostic Transfomer Models

The Emergence of Transformer-Driven Copilots

Transformers for NLP—Exam 2

Conclusion

Appendix

Sentiment Analysis with Hugging Face Models