HarderThenHarder/transformers_tasks
Transformers Tasks
Transformers Tasks is a collection of toolkits and scripts dedicated to language model fine-tuning, natural language processing tasks, and transformer-based pipelines. The project functions as a natural language processing toolkit and transformer pipeline library, providing Python scripts and algorithms designed to adapt foundational language models and route text inputs through modular processing workflows.
The repository covers supervised fine-tuning pipelines and reinforcement learning alignment procedures that optimize generative text outputs through reward modeling and policy gradient loops. It incorporates token-level decoding strategies with probability sampling, beam search, and temperature-controlled generation parameters, alongside unified model abstractions for pre-trained neural network architectures.
Additional capabilities include prompt optimization strategies for few-shot and zero-shot downstream tasks, semantic similarity computation for text pairs, text categorization algorithms for sentiment and content organization, and information extraction engines for named entity recognition.
Features
- Natural Language Processing - Builds machine learning workflows for text classification, semantic matching, and extraction.
- Language Model Fine-Tuning - Provides a collection of training scripts for adapting foundational language models.
- Reinforcement Learning Alignment - Aligns generative language models with human preferences using reward modeling and policy optimization.
- Named Entity Recognition - Pulls structured data points and named entities out of unstructured documents using NLP tools.
- Natural Language Generation - Produces coherent text outputs based on prompts using generative language models and probabilistic decoding strategies.
- Pre-trained Transformer Architectures - Wraps complex pre-trained neural network architectures into unified interfaces for execution.
- Reinforcement Learning Alignment - Optimizes generative text outputs through reward modeling and policy gradient reinforcement learning loops.
- Supervised Fine-Tuning - Adapts foundational language models to specialized domain tasks using instruction datasets.
- Transformer-Based NLP Libraries - Delivers a library of Python scripts and algorithms for text classification and generation.
- Information Extraction - Identifies and extracts specific entities and structured data points out of unstructured documents.
- Supervised Fine-Tuning - Adapts pre-trained language models on custom datasets through supervised instruction tuning.
- Inference Pipeline Routing - Directs raw text inputs through specialized modular processing workflows tailored to specific tasks.
- Decoding Strategy Caption Generators - Generates continuous text tokens iteratively using probability sampling, beam search, and temperature control.
- Prompt Optimizers - Leverages optimized prompt templates to achieve high performance on few-shot tasks.
- Semantic Similarity Calculation - Calculates semantic similarity between text pairs using supervised models for search tasks.
- Text Classification - Classifies unstructured text inputs into predefined categories or sentiment labels.
