{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-tokenizers","slug":"pypi-tokenizers","name":"tokenizers","type":"repo","url":"https://pypi.org/project/tokenizers/","page_url":"https://unfragile.ai/pypi-tokenizers","categories":["frameworks-sdks"],"tags":["NLP","tokenizer","BPE","transformer","deep","learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-tokenizers__cap_0","uri":"capability://data.processing.analysis.high.performance.bpe.tokenization.with.rust.core","name":"high-performance bpe tokenization with rust core","description":"Implements Byte Pair Encoding (BPE) algorithm in Rust with FFI bindings to Python and Node.js, achieving 10-100x faster tokenization than pure Python implementations. The Rust core uses efficient data structures and memory management to process text into token IDs and offsets, with the tokenization pipeline flowing through normalizers, pre-tokenizers, and post-processors as composable stages.","intents":["I need to tokenize large text corpora quickly for LLM training without Python performance bottlenecks","I want to use the same BPE tokenizer across Python, Node.js, and Rust projects with identical behavior","I need to understand token-to-character offset mappings for span extraction and entity alignment"],"best_for":["ML engineers training transformer models at scale","Teams building production NLP pipelines requiring sub-millisecond tokenization latency","Developers migrating from NLTK/spaCy to modern transformer-era tokenization"],"limitations":["BPE training requires loading entire corpus into memory; no streaming training mode for datasets >100GB","Offset tracking adds ~5-15% memory overhead compared to token-only output","Custom BPE merge rules cannot be injected mid-tokenization; requires retraining"],"requires":["Python 3.7+ (for Python bindings via PyO3)","Node.js 12+ (for Node.js bindings via napi-rs)","Rust 1.56+ (for native compilation from source)"],"input_types":["raw text strings","file paths to text documents","pre-normalized text"],"output_types":["token IDs (integer arrays)","token strings","character offset mappings (start/end positions)","Encoding objects with metadata"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_1","uri":"capability://data.processing.analysis.wordpiece.tokenization.with.subword.vocabulary.matching","name":"wordpiece tokenization with subword vocabulary matching","description":"Implements WordPiece algorithm (used by BERT, DistilBERT) that greedily matches the longest subword tokens from a vocabulary, prefixing continuation tokens with '##' to indicate non-initial positions. The algorithm processes pre-tokenized words character-by-character, falling back to [UNK] tokens for out-of-vocabulary subwords, enabling efficient representation of rare words and morphological variants.","intents":["I need BERT-compatible tokenization for fine-tuning on downstream NLP tasks","I want to build a custom WordPiece tokenizer from my domain-specific vocabulary","I need to handle rare words and morphological variants without expanding vocabulary size"],"best_for":["NLP practitioners fine-tuning BERT/DistilBERT models","Teams building domain-specific language models (biomedical, legal, code)","Researchers comparing tokenization strategies for multilingual models"],"limitations":["WordPiece greedy matching is not optimal for all languages; CJK languages require pre-segmentation","No built-in support for dynamic vocabulary expansion; requires retraining for new domains","[UNK] token loss is irreversible; cannot reconstruct original text from tokens with unknown subwords"],"requires":["Pre-trained vocabulary file (typically 30K-100K tokens)","Python 3.7+ or Node.js 12+","Pre-tokenizer to split text into words (whitespace or language-specific)"],"input_types":["raw text strings","pre-tokenized word lists"],"output_types":["token IDs with ## prefix markers in token strings","Encoding objects with token-to-character mappings"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_10","uri":"capability://tool.use.integration.multi.language.binding.support.with.pyo3.python.and.napi.rs.node.js","name":"multi-language binding support with pyo3 (python) and napi-rs (node.js)","description":"Provides language-specific bindings that expose the Rust core to Python and Node.js via PyO3 and napi-rs FFI technologies. PyO3 bindings use Arc<RwLock> for thread-safe shared state and integrate with tokio for async support; napi-rs bindings compile to native addons for multiple platforms (Linux gnu/musl, Windows, macOS, Android). Both bindings maintain API parity with the Rust core while providing idiomatic interfaces for each language.","intents":["I need to use the same tokenizer across Python, Node.js, and Rust projects with identical behavior","I want to parallelize tokenization across multiple threads in Python without GIL contention","I need to deploy tokenizers in Node.js/Electron applications with native performance"],"best_for":["Polyglot teams using Python for ML and Node.js for web services","ML engineers building production systems requiring sub-millisecond tokenization latency","Teams deploying models across multiple runtime environments (Python, Node.js, Rust)"],"limitations":["PyO3 bindings require Python 3.7+; no support for Python 2.x or PyPy","napi-rs bindings require Node.js 12+; no support for older Node versions","Custom Rust components cannot be exposed to Python/Node.js without additional binding code","Async support in Python requires tokio runtime integration; not compatible with asyncio-only code"],"requires":["Python 3.7+ (for Python bindings)","Node.js 12+ (for Node.js bindings)","Rust 1.56+ (for compilation from source)","C compiler (gcc/clang/MSVC) for native extension compilation"],"input_types":["text strings","file paths","configuration dictionaries"],"output_types":["Encoding objects (Python/Node.js native objects)","token IDs and strings","offset mappings"],"categories":["tool-use-integration","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_11","uri":"capability://data.processing.analysis.batch.tokenization.with.parallel.processing.support","name":"batch tokenization with parallel processing support","description":"Supports efficient batch tokenization of multiple texts simultaneously, with optional parallelization across CPU cores. The batch API accepts lists of strings and returns lists of Encoding objects, with internal parallelization via Rayon (Rust) or thread pools. Batch processing reduces per-text overhead and enables better CPU cache utilization compared to sequential tokenization.","intents":["I need to tokenize large datasets (millions of documents) efficiently for training","I want to parallelize tokenization across CPU cores without manual threading","I need to reduce tokenization latency for batch inference in production systems"],"best_for":["ML engineers processing large training corpora (>1M documents)","Teams building batch inference pipelines for NLP models","Data engineers optimizing ETL pipelines with tokenization steps"],"limitations":["Batch API requires loading all texts into memory; no streaming batch mode for >100GB datasets","Parallelization overhead is significant for small batches (<100 texts); sequential processing is faster","Thread pool size is fixed at initialization; no dynamic adjustment based on system load","No built-in support for variable-length batches or dynamic batching strategies"],"requires":["Python 3.7+ or Node.js 12+","Multi-core CPU (parallelization benefit requires ≥4 cores)","Sufficient RAM for batch size (typically 100-1000 texts per batch)"],"input_types":["lists of text strings","generators/iterators of texts"],"output_types":["lists of Encoding objects","batched token ID arrays","batched offset mappings"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_12","uri":"capability://data.processing.analysis.encoding.object.with.rich.metadata.and.token.level.information","name":"encoding object with rich metadata and token-level information","description":"Returns Encoding objects that encapsulate complete tokenization results: token IDs, token strings, character offsets, attention masks, token type IDs (for sequence pairs), and special token positions. The Encoding structure provides convenient accessors for common operations (e.g., getting tokens for a span, padding to length) and supports serialization to/from dictionaries for integration with ML frameworks.","intents":["I need to access token IDs, tokens, and offsets from a single tokenization result","I want to automatically generate attention masks and token type IDs for transformer models","I need to pad/truncate sequences to a fixed length with proper mask handling"],"best_for":["ML engineers building transformer model pipelines","Teams integrating tokenization with PyTorch/TensorFlow data loaders","Researchers analyzing tokenization artifacts and token-level information"],"limitations":["Encoding objects are immutable; cannot modify tokens or offsets after creation","No built-in support for custom metadata fields; requires external storage for task-specific annotations","Padding/truncation operations create new Encoding objects; no in-place modifications","Serialization to dictionaries loses type information; requires manual type casting when deserializing"],"requires":["Python 3.7+ or Node.js 12+","Understanding of transformer model input formats (token IDs, attention masks, token type IDs)"],"input_types":["tokenization results (internal)","dictionaries (for deserialization)"],"output_types":["Encoding objects with token IDs, tokens, offsets, masks","dictionaries (for ML framework integration)","numpy/torch tensors (via conversion)"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_13","uri":"capability://data.processing.analysis.decoder.for.reconstructing.text.from.tokens","name":"decoder for reconstructing text from tokens","description":"Implements decoders that reconstruct original text from token sequences, reversing the tokenization process. Different decoders handle different tokenization schemes: BPE decoder removes ## markers and merges subword tokens, WordPiece decoder handles ## continuation markers, Unigram decoder reconstructs from byte-level tokens. Decoders support optional space insertion and special character handling.","intents":["I need to reconstruct text from model predictions for text generation or machine translation","I want to verify tokenization correctness by round-tripping text through tokenizer and decoder","I need to handle special tokens and control characters when reconstructing text"],"best_for":["ML engineers building text generation pipelines (machine translation, summarization, etc.)","Teams debugging tokenization issues via round-trip verification","Researchers analyzing tokenization artifacts and reconstruction errors"],"limitations":["Decoding is lossy for [UNK] tokens; cannot reconstruct original text if unknowns are present","Space insertion heuristics may not work correctly for all languages (CJK, Arabic, etc.)","Special tokens (e.g., [CLS], [SEP]) are typically removed during decoding; no built-in handling for task-specific tokens","Decoding assumes single-pass tokenization; no support for iterative refinement or confidence scores"],"requires":["Python 3.7+ or Node.js 12+","Token sequences (lists of token IDs or token strings)","Tokenizer with decoder configuration"],"input_types":["token ID sequences","token string sequences","Encoding objects"],"output_types":["reconstructed text strings","text with optional space markers"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_2","uri":"capability://data.processing.analysis.unigram.language.model.tokenization.with.probability.based.selection","name":"unigram language model tokenization with probability-based selection","description":"Implements Unigram tokenization (used by SentencePiece) that models tokenization as a probabilistic process where each token has an associated loss value. During encoding, the algorithm finds the most likely tokenization sequence that minimizes loss, and during training, iteratively removes low-loss tokens from the vocabulary. This approach naturally handles variable-length tokens and rare characters without explicit [UNK] fallback.","intents":["I need to tokenize multilingual text (CJK, Arabic, etc.) without language-specific preprocessing","I want a tokenizer that gracefully handles unknown characters instead of [UNK] tokens","I need to optimize tokenization for compression and vocabulary size efficiency"],"best_for":["Multilingual NLP teams building models for 50+ languages","Researchers optimizing tokenization efficiency for low-resource languages","Teams building production systems that must handle arbitrary Unicode input"],"limitations":["Unigram training is computationally expensive; requires multiple EM iterations over corpus","Probability-based selection adds ~10-20% latency vs greedy BPE matching","Loss values are corpus-dependent; vocabulary from one domain may not transfer well to another"],"requires":["Python 3.7+ or Node.js 12+","Training corpus (minimum 1M tokens recommended for stable probability estimates)","Sufficient memory for EM iterations (typically 2-4x corpus size)"],"input_types":["raw text strings","multilingual text with mixed scripts"],"output_types":["token IDs","token strings with byte-level fallback characters","Encoding objects with loss metadata"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_3","uri":"capability://data.processing.analysis.wordlevel.tokenization.with.simple.vocabulary.lookup","name":"wordlevel tokenization with simple vocabulary lookup","description":"Implements the simplest tokenization strategy: direct vocabulary lookup where each whitespace-separated word maps to a token ID, with [UNK] for out-of-vocabulary words. This approach requires explicit pre-tokenization and is primarily used for legacy models or as a baseline, but provides maximum interpretability and minimal computational overhead.","intents":["I need to tokenize text using a fixed vocabulary for legacy NLP models","I want the simplest possible tokenization strategy for interpretability and debugging","I need to maintain backward compatibility with older word-based models"],"best_for":["Legacy NLP systems migrating from word-based to subword tokenization","Educational projects teaching tokenization fundamentals","Debugging and interpretability-focused workflows"],"limitations":["Out-of-vocabulary rate is high for open-domain text; typically 5-15% of tokens become [UNK]","Vocabulary size must be large (100K+) to cover reasonable coverage, increasing model size","Cannot handle morphological variants or rare words efficiently","Requires explicit pre-tokenization; no built-in handling of punctuation or special characters"],"requires":["Pre-tokenized word list or vocabulary file","Explicit pre-tokenizer (whitespace, punctuation-aware, or language-specific)","Python 3.7+ or Node.js 12+"],"input_types":["pre-tokenized word lists","raw text with pre-tokenizer"],"output_types":["token IDs (one per word)","Encoding objects with word-level offsets"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_4","uri":"capability://data.processing.analysis.composable.pipeline.architecture.with.normalizers.pre.tokenizers.and.post.processors","name":"composable pipeline architecture with normalizers, pre-tokenizers, and post-processors","description":"Provides a modular pipeline where text flows through configurable stages: Normalizer (Unicode normalization, lowercasing, accent removal), PreTokenizer (whitespace/punctuation splitting, language-specific segmentation), Model (BPE/WordPiece/Unigram/WordLevel), PostProcessor (adding special tokens like [CLS]/[SEP], handling sequence pairs), and Decoder (reconstructing text from tokens). Each stage is independently composable, allowing users to build custom tokenizers by chaining components.","intents":["I need to build a custom tokenizer combining BERT normalization with SentencePiece-style subword splitting","I want to add domain-specific preprocessing (e.g., code tokenization, medical abbreviation handling) to standard tokenizers","I need to handle sequence pairs (e.g., question-answer) with automatic special token insertion"],"best_for":["ML engineers building custom tokenizers for specialized domains (code, biomedical, legal)","Teams integrating tokenization into larger NLP pipelines with custom preprocessing","Researchers experimenting with tokenization component combinations"],"limitations":["Pipeline composition is sequential; no built-in parallelization across stages","Custom components require Python/Rust implementation; no declarative DSL for simple transformations","Pipeline state is immutable after creation; cannot dynamically add/remove stages at inference time","Debugging multi-stage pipelines requires tracing through each component's output"],"requires":["Python 3.7+ (for custom Python components via PyO3)","Rust knowledge (for custom Rust components)","Understanding of tokenization pipeline concepts (normalization, pre-tokenization, etc.)"],"input_types":["raw text strings","configuration dictionaries (for loading pre-built pipelines)"],"output_types":["Encoding objects with token IDs, tokens, offsets, and special token masks","JSON configuration files (for serialization)"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_5","uri":"capability://data.processing.analysis.bpe.training.from.raw.corpus.with.configurable.merge.frequency","name":"bpe training from raw corpus with configurable merge frequency","description":"Implements BPE training algorithm that iteratively merges the most frequent byte/character pairs in a corpus to build a vocabulary. The algorithm starts with character-level tokens, counts pair frequencies, merges the top-frequency pair, and repeats until reaching the target vocabulary size. Training supports byte-level BPE (for any Unicode text) and character-level BPE, with configurable minimum frequency thresholds and special token handling.","intents":["I need to train a custom BPE tokenizer on my domain corpus (code, medical text, etc.)","I want to build a multilingual tokenizer by training BPE on concatenated corpora","I need to control vocabulary size and merge frequency for memory/latency trade-offs"],"best_for":["ML engineers building custom language models for specialized domains","Teams training multilingual models with domain-specific vocabularies","Researchers experimenting with vocabulary size impact on model performance"],"limitations":["Training requires loading entire corpus into memory; no streaming mode for >100GB datasets","Merge frequency computation is O(n) per iteration; training 50K vocabulary takes 30min-2hrs on 1B token corpus","Vocabulary is corpus-specific; trained BPE may not generalize well to out-of-domain text","No built-in support for subword regularization or other training-time augmentations"],"requires":["Raw text corpus (minimum 10M tokens recommended for stable vocabulary)","Python 3.7+ or Node.js 12+","Sufficient RAM (typically 2-4x corpus size for intermediate data structures)","Target vocabulary size parameter (typically 10K-50K tokens)"],"input_types":["raw text files or file paths","text iterators/generators","pre-tokenized word lists"],"output_types":["trained Tokenizer object with BPE model","vocabulary file (JSON format with merge rules)","merge statistics (pair frequencies, iteration logs)"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_6","uri":"capability://data.processing.analysis.unigram.vocabulary.training.with.em.based.loss.optimization","name":"unigram vocabulary training with em-based loss optimization","description":"Implements Unigram language model training using Expectation-Maximization (EM) to optimize token loss values. The algorithm initializes vocabulary with frequent substrings, computes token loss via forward-backward algorithm, and iteratively removes low-loss tokens until reaching target vocabulary size. This approach naturally balances vocabulary coverage and compression efficiency.","intents":["I need to train a Unigram tokenizer optimized for compression and multilingual coverage","I want to build a tokenizer that gracefully handles unknown characters without [UNK] tokens","I need to optimize vocabulary size for a specific corpus while maintaining character-level fallback"],"best_for":["Teams building multilingual models requiring robust unknown character handling","Researchers optimizing tokenization efficiency for low-resource languages","ML engineers training models where vocabulary size is a critical constraint"],"limitations":["EM training is computationally expensive; 2-4 hours for 1B token corpus vs 30min for BPE","Loss values are corpus-specific and may not transfer across domains","Requires careful tuning of EM iterations and convergence thresholds","No built-in support for controlling vocabulary composition (e.g., forcing inclusion of specific tokens)"],"requires":["Raw text corpus (minimum 10M tokens for stable loss estimates)","Python 3.7+ or Node.js 12+","Sufficient RAM and CPU (EM iterations are single-threaded in current implementation)","Target vocabulary size and EM iteration count parameters"],"input_types":["raw text files","text iterators","pre-tokenized word lists"],"output_types":["trained Tokenizer object with Unigram model","vocabulary file with loss values (JSON format)","EM iteration logs and convergence metrics"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_7","uri":"capability://data.processing.analysis.wordpiece.and.wordlevel.training.from.vocabulary.and.corpus","name":"wordpiece and wordlevel training from vocabulary and corpus","description":"Implements training for WordPiece and WordLevel tokenizers by computing subword statistics from a pre-tokenized corpus. For WordPiece, the algorithm identifies frequent subword pairs and builds a vocabulary with ## continuation markers; for WordLevel, it simply counts word frequencies and selects the top-K words. Both approaches support minimum frequency thresholds and special token handling.","intents":["I need to train a BERT-compatible WordPiece tokenizer on my domain corpus","I want to build a custom word-level tokenizer with domain-specific vocabulary","I need to create a tokenizer that preserves specific tokens (e.g., domain terms, code identifiers)"],"best_for":["Teams fine-tuning BERT models on domain-specific corpora","ML engineers building custom vocabularies for specialized NLP tasks","Researchers comparing tokenization strategies for specific domains"],"limitations":["WordPiece training requires pre-tokenized input; no built-in word segmentation for CJK languages","WordLevel training produces high OOV rates for open-domain text; requires very large vocabularies (100K+)","No support for dynamic vocabulary expansion; requires full retraining for new domains","Minimum frequency thresholds may remove important rare tokens (e.g., domain-specific terms)"],"requires":["Pre-tokenized corpus (word-separated text)","Python 3.7+ or Node.js 12+","Target vocabulary size and minimum frequency parameters","Optional: special tokens list (e.g., [CLS], [SEP], [UNK])"],"input_types":["pre-tokenized text files","word frequency lists","text iterators with pre-tokenization"],"output_types":["trained Tokenizer object with WordPiece/WordLevel model","vocabulary file (JSON format with token frequencies)","training statistics (coverage, OOV rate, vocabulary composition)"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_8","uri":"capability://data.processing.analysis.tokenizer.serialization.and.deserialization.with.json.configuration","name":"tokenizer serialization and deserialization with json configuration","description":"Implements save/load functionality for tokenizers via JSON configuration files that capture the complete pipeline state: normalizer settings, pre-tokenizer rules, model parameters (vocabulary, merge rules, loss values), post-processor configuration, and decoder settings. Serialization enables reproducible tokenization across environments and version control of tokenizer configurations.","intents":["I need to save a trained tokenizer and load it in production without retraining","I want to version control tokenizer configurations alongside model checkpoints","I need to share tokenizers across teams and ensure identical tokenization behavior"],"best_for":["ML teams managing production NLP pipelines with reproducibility requirements","Researchers publishing models with tokenizer configurations","Teams migrating tokenizers across Python/Node.js/Rust environments"],"limitations":["JSON serialization can be verbose for large vocabularies (50K tokens = 5-10MB JSON file)","Custom Python/Rust components cannot be serialized; only built-in components are supported","No built-in versioning; tokenizer format changes may require manual migration scripts","Vocabulary files are not compressed; no built-in support for vocabulary deduplication"],"requires":["Python 3.7+ or Node.js 12+","Disk space for JSON configuration (typically 1-10MB per tokenizer)","Read/write permissions for file system or cloud storage"],"input_types":["Tokenizer objects (in-memory)","JSON configuration files","file paths to tokenizer.json"],"output_types":["JSON configuration files","Tokenizer objects (deserialized)","vocabulary files (extracted from JSON)"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tokenizers__cap_9","uri":"capability://data.processing.analysis.offset.tracking.and.character.to.token.mapping.for.span.extraction","name":"offset tracking and character-to-token mapping for span extraction","description":"Tracks character-level offsets (start/end positions in original text) for each token, enabling reverse mapping from token positions back to original text spans. The Encoding object stores offset tuples for each token, allowing users to extract original text for specific tokens or identify which tokens correspond to a given character range. This is essential for entity extraction, question answering, and other span-based NLP tasks.","intents":["I need to extract original text spans for predicted entity boundaries in NER tasks","I want to map token predictions back to character positions for span-based QA","I need to align tokenization with external annotations (e.g., POS tags, entity labels) at character level"],"best_for":["NLP engineers building entity extraction and span-based QA systems","Teams aligning tokenization with external linguistic annotations","Researchers analyzing tokenization artifacts and boundary errors"],"limitations":["Offset tracking adds ~5-15% memory overhead to Encoding objects","Offsets are only accurate for lossless tokenization; [UNK] tokens have undefined character spans","No built-in support for overlapping spans or discontinuous entities","Offset computation assumes single-pass tokenization; no support for iterative refinement"],"requires":["Python 3.7+ or Node.js 12+","Tokenizer with offset tracking enabled (default behavior)","Original text string (required for offset validation)"],"input_types":["raw text strings","Encoding objects with offset metadata"],"output_types":["offset tuples (start, end) for each token","character-to-token mapping dictionaries","original text spans extracted via offsets"],"categories":["data-processing-analysis","nlp-tokenization"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ (for Python bindings via PyO3)","Node.js 12+ (for Node.js bindings via napi-rs)","Rust 1.56+ (for native compilation from source)","Pre-trained vocabulary file (typically 30K-100K tokens)","Python 3.7+ or Node.js 12+","Pre-tokenizer to split text into words (whitespace or language-specific)","Python 3.7+ (for Python bindings)","Node.js 12+ (for Node.js bindings)","Rust 1.56+ (for compilation from source)","C compiler (gcc/clang/MSVC) for native extension compilation"],"failure_modes":["BPE training requires loading entire corpus into memory; no streaming training mode for datasets >100GB","Offset tracking adds ~5-15% memory overhead compared to token-only output","Custom BPE merge rules cannot be injected mid-tokenization; requires retraining","WordPiece greedy matching is not optimal for all languages; CJK languages require pre-segmentation","No built-in support for dynamic vocabulary expansion; requires retraining for new domains","[UNK] token loss is irreversible; cannot reconstruct original text from tokens with unknown subwords","PyO3 bindings require Python 3.7+; no support for Python 2.x or PyPy","napi-rs bindings require Node.js 12+; no support for older Node versions","Custom Rust components cannot be exposed to Python/Node.js without additional binding code","Async support in Python requires tokio runtime integration; not compatible with asyncio-only code","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.6,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:15.343Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-tokenizers","compare_url":"https://unfragile.ai/compare?artifact=pypi-tokenizers"}},"signature":"Zf4OI1N+5UzPVj6tkfcP6ZtVUp6hmPNPxIbYnM+PU/Z/EdaMOXBgWsFE82D+zo6hmd8SPjcnXnwA5Cv+w/BaCg==","signedAt":"2026-06-21T18:48:01.691Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-tokenizers","artifact":"https://unfragile.ai/pypi-tokenizers","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-tokenizers","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}