Transformers
Hugging Face Transformers is an open-source library providing pretrained transformer models, tokenizers, and tools for using and training models across machine-learning tasks.
Natural Language Processing in Action, Second Edition
Hugging Face Transformers is an open-source library providing pretrained transformer models and utilities for natural-language and other machine-learning tasks.
- How it is used
- Supports pretrained-model inference, text generation, semantic representations, and task-specific fine-tuning.
- How deep it goes
- Demonstrates installation, model loading, tokenization, generation, architecture inspection, and fine-tuning, but defers exhaustive compute planning.
- Examples
- Uses BERT architecture inspection, GPT-2 prompt generation, probability selection, nucleus sampling, and manuscript-line fine-tuning.
- What you can do after
- Builds inference and fine-tuning pipelines that load pretrained models, generate text, inspect outputs, and train on custom datasets.
Read how Natural Language Processing in Action, Second Edition explains transformers →
Hugging Face in Action
Transformers is an open-source library for working with pretrained machine-learning models, especially models for natural-language processing and computer vision.
- How it is used
- Applies pretrained transformer models to practical NLP, vision, audio, and reinforcement-learning workflows.
- How deep it goes
- Explains installation, pretrained models, pipelines, and task categories while stopping short of detailed transformer internals.
- Examples
- Demonstrates sentiment classification with DistilBERT and object detection with a hosted DETR model.
- What you can do after
- Apply it via the pipeline API to run pretrained models for common language and vision tasks.
Domain-Specific Small Language Models
The Hugging Face library for working with pretrained transformer models, tokenizers, pipelines, and related training and inference utilities.
- How it is used
- Supports pretrained-model fine-tuning, text inference, evaluation, and local deployment across supported hardware.
- How deep it goes
- Explains operational workflows and performance constraints, while deferring transformer internals and implementation details.
- Examples
- Uses DistilBERT question answering, GPT-Neo text generation, and BERT classification on an Android device.
- What you can do after
- Apply it via Python to fine-tune, evaluate, generate text, classify inputs, and deploy supported models locally.
Read how Domain-Specific Small Language Models explains transformers →
Learn Generative AI with PyTorch
A software library for working with pretrained transformer models and tokenizers from Hugging Face.
- How it is used
- Supports multilingual tokenization and imports pretrained transformer parameters into custom model implementations.
- How deep it goes
- Explains installation, tokenizer calls, vocabulary indexing, state-dictionary extraction, and selected weight transpositions.
- Examples
- Uses English-French phrases, XLM token sequences, vocabulary indexes, and GPT-2XL weight-shape comparisons.
- What you can do after
- Apply it via Hugging Face APIs to tokenize multilingual text or load pretrained GPT-2XL weights.
Read how Learn Generative AI with PyTorch explains transformers →
Data Analysis with LLMs
Hugging Face Transformers is an open-source library providing pretrained Transformer models and tools for natural-language, vision, and multimodal machine-learning tasks.
- How it is used
- Supports browsing, selecting, downloading, and running pretrained Transformer models for specialized machine-learning tasks.
- How deep it goes
- Demonstrates installation, pipeline creation, inference, and output inspection, while stopping short of comprehensive library features or model training.
- Examples
- Uses task filters, a Salesforce BLIP image-captioning model, and Cardiff University’s RoBERTa sentiment-classification model.
- What you can do after
- Apply it via Python to download and run a pretrained model through a task pipeline.
Transformers in Action
Transformers is an open-source software library for using pretrained transformer models across machine-learning tasks.
- How it is used
- Applies pretrained transformer models to NLP tasks and emerging computer vision and audio recognition applications.
- How deep it goes
- Explains pretrained-model use, fine-tuning, learning modes, and GPU requirements without detailing model internals or implementation code.
- Examples
- Uses translation, text generation, text classification, sentiment analysis, labeled sentence pairs, Google Colab, and GPU computation as examples.
- What you can do after
- Apply it via Hugging Face to adapt pretrained models for specific tasks while reducing training time and computational resources.
Related concepts
- PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
- Hugging Faceused by · Hugging Face is a platform and ecosystem for discovering, hosting, sharing, and using machine-learning models, datasets, applications, and related tools.
- pipelineuses · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
- Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
- relation extractionuses · Relation extraction is a natural-language-processing technique that identifies semantic relationships between entities expressed in text.
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