bentrevett/pytorch-sentiment-analysis
Pytorch Sentiment Analysis
This project is a PyTorch sentiment analysis tutorial and a deep learning implementation for analyzing text. It provides a natural language processing sequence classification pipeline designed to clean text data and train neural networks to categorize sequences of words.
The implementation focuses on adapting pretrained language models for specific text classification tasks using custom datasets. It includes a process for fine-tuning large-scale language models and implementing recurrent networks and transformers for emotional tone detection.
The project covers the broader surface of text sequence classification and PyTorch text processing. This includes workflows for preparing raw text datasets using the TorchText library and building deep learning models to assign categories to text.
Features
- Sentiment Classifiers - Implements neural network architectures specifically designed for classifying the emotional tone of text.
- Pretrained Model Integrations - Integrates pretrained language models into a custom pipeline for sentiment classification tasks.
- Large Language Model Fine-Tuning - Adapts pretrained large language models to sentiment analysis tasks using custom datasets.
- Sequential Data Processing - Processes text sequences using recurrent neural networks to capture temporal dependencies and context.
- Pretrained Model Loading - Loads pretrained language models and adapts their vocabularies for fine-tuning on sentiment datasets.
- NLP Pipeline Assembly - Implements an end-to-end NLP pipeline for cleaning text and training sequence classifiers.
- Sequence Label Classification - Classifies entire text sequences into predefined categories using transformers and recurrent networks.
- Sequence Category Assignments - Assigns categorical labels to text sequences using recurrent networks and transformers.
- Sentiment Analysis Models - Provides a deep learning implementation of sentiment analysis models using PyTorch.
- Attention Mechanisms - Implements attention-based weighting to focus on the most relevant words for text classification.
- Batch Sequence Training - Groups multiple text sequences into tensors to optimize hardware utilization during model training.
- Vocabulary Mappers - Maps raw text strings to unique integer identifiers for numerical vectorization using a vocabulary dictionary.
- Supervised Model Weight Optimization - Refines neural network parameters using supervised gradient descent based on sentiment labels.
- Text Dataset Preparation - Prepares and cleans raw text corpora using TorchText for use in machine learning workflows.
- Text Preprocessing - Provides text preprocessing routines to scrub and simplify raw datasets for sequence classification.
- PyTorch Deep Learning Examples - Provides reference implementations of recurrent networks and transformers for text analysis using PyTorch.
- PyTorch NLP Tutorials - Offers annotated tutorials on building sentiment analysis models and text processing with PyTorch.
- Model Implementations - Tutorials for sentiment analysis using text processing tools.
- Natural Language Processing - Sentiment analysis using TorchText.
