huggingface/tokenizers

Features

• Text Tokenizers - Converts raw text into a sequence of tokens and IDs using high-performance segmentation algorithms.
• Sequence Padding Utilities - Provides utilities for standardizing input sequence lengths through truncation and padding with special tokens.
• Vocabulary Builders - Generates token-to-index mappings from datasets to prepare text for machine learning models.
• Byte Pair Encodings - Builds token sets using byte-pair encoding by iteratively merging the most frequent character pairs.
• Vocabulary Learning - Creates a mapping of characters or subwords from raw text corpora using byte-pair encoding or wordpiece algorithms.
• Vocabulary Training - Analyzes text datasets to create custom subword vocabularies using byte-pair encoding and wordpiece algorithms.
• Pipeline Construction - Enables the construction of custom tokenization pipelines by combining normalization and model components.
• Subword Tokenization - Uses byte-pair encoding and wordpiece algorithms to handle out-of-vocabulary words via subword units.
• Vocabulary Trainers - Includes a trainer to analyze text datasets and create custom subword vocabularies using byte-pair encoding and wordpiece.
• Text Preprocessing Pipelines - Provides automated workflows for cleaning and normalizing raw text data to prepare it for machine learning models.
• Token Alignment Trackers - Tracks alignments during normalization to identify the exact original text characters corresponding to a specific token.
• Text Processing Pipelines - Utilizes a modular pipeline to sequentially apply normalization, pre-tokenization, and splitting for structured text processing.
• Machine Learning Pipelines - Orchestrates a preprocessing pipeline that includes normalization, padding, and truncation to prepare text datasets for models.
• Rust-Implemented Tools - Implements core tokenization logic in Rust to ensure memory safety and high-performance execution.
• Custom Text Normalizers - Cleans input text while tracking character offsets to enable mapping tokens back to the original sentence.
• Alignment-Aware Normalization - Cleans input text while tracking character offsets to maintain traceability between original text and processed tokens.
• Offset Tracking - Provides high-precision tracking of original character offsets through normalization to allow tokens to be mapped back to raw text.
• Large Language Model Input Tools - Prepares text for model consumption by applying final padding, truncation, and special token insertion.
• Input Buffer Formatting - Provides utilities to trim, pad, and inject special tokens into tokenized output to meet specific model input requirements.
• Token-to-Text Alignments - Tracks character offsets during normalization so processed tokens can be mapped back to original positions in the raw text.
• Python Bindings - Exposes a high-performance Rust implementation to Python via low-overhead foreign function interface bindings.
• Parallel Text Processing - Implements multi-threaded parallel processing of text to increase encoding throughput across multiple CPU cores.
• Multi-Pattern Matching Algorithms - Uses trie-based structures to efficiently find and replace the longest matching subwords in text streams.
• Artificial Intelligence - Modern NLP tokenization pipelines with high-performance implementations.
• Machine Learning Frameworks - High-performance tokenizer implementation for NLP.
• Model Serving Engines - High-performance tokenization library for research and production.
• Natural Language Processing - High-performance tokenization library for research and production.
• Core Frameworks and Libraries - Fast state-of-the-art tokenizers for training and inference.
• Lexical Analysis Tools - High-performance tokenization library for NLP models.
• Python NLP Libraries - High-performance tokenization for research and production.

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