{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-kredor--punctuate-all","slug":"kredor--punctuate-all","name":"punctuate-all","type":"model","url":"https://huggingface.co/kredor/punctuate-all","page_url":"https://unfragile.ai/kredor--punctuate-all","categories":["model-training"],"tags":["transformers","pytorch","xlm-roberta","token-classification","dataset:wmt/europarl","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-kredor--punctuate-all__cap_0","uri":"capability://text.generation.language.multilingual.punctuation.restoration.via.token.classification","name":"multilingual punctuation restoration via token classification","description":"Restores missing punctuation marks (periods, commas, question marks, exclamation points) in unpunctuated text using XLM-RoBERTa token-classification architecture. The model processes input text as a sequence of tokens and assigns each token a classification label indicating whether it should be followed by punctuation and which type. Inference runs locally or via HuggingFace Inference API without requiring external services.","intents":["I need to automatically add punctuation to transcribed speech or OCR output that lacks punctuation marks","I want to clean up user-generated content or chat logs by restoring proper punctuation","I need a lightweight, open-source solution to punctuate text in multiple languages without API costs","I'm building a text preprocessing pipeline and need punctuation restoration as an intermediate step"],"best_for":["NLP engineers building text preprocessing pipelines for speech-to-text or OCR workflows","Teams working with multilingual datasets requiring punctuation normalization","Developers needing cost-effective, on-premise punctuation restoration without API dependencies","Researchers studying token-level sequence labeling and punctuation prediction"],"limitations":["Token-classification approach processes text sequentially, adding ~50-200ms latency per 512-token chunk depending on hardware","Model trained on Europarl dataset (parliamentary speech) — may underperform on informal text, technical jargon, or domain-specific language","No context awareness beyond local token neighborhoods — struggles with ambiguous punctuation decisions requiring broader discourse understanding","Fixed vocabulary from XLM-RoBERTa pretraining — cannot handle out-of-vocabulary tokens or specialized terminology without subword tokenization fallback","Outputs only standard punctuation marks (period, comma, question mark, exclamation point) — does not handle dashes, parentheses, quotes, or other special punctuation"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.0+","HuggingFace account (optional, for Inference API access)","4GB+ RAM for local inference"],"input_types":["raw text (unpunctuated strings)","transcribed speech (ASR output)","OCR output","chat messages or user-generated content"],"output_types":["punctuated text (same text with punctuation marks inserted)","token-level classification labels (BIO or IOB format for each token)","confidence scores per punctuation prediction"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kredor--punctuate-all__cap_1","uri":"capability://tool.use.integration.batch.text.punctuation.processing.with.huggingface.inference.api.integration","name":"batch text punctuation processing with huggingface inference api integration","description":"Enables serverless batch processing of unpunctuated text through HuggingFace's Inference API endpoints, supporting both synchronous single-request and asynchronous batch job submission. The model is registered as an Inference API endpoint compatible with standard transformers pipeline interface, allowing developers to submit requests without managing GPU infrastructure or model weights locally.","intents":["I want to punctuate large volumes of text without provisioning GPU servers or managing model deployment","I need to integrate punctuation restoration into a cloud-native application without operational overhead","I'm processing batches of documents and want to leverage HuggingFace's distributed inference infrastructure","I need API-based punctuation restoration with automatic scaling and pay-per-use pricing"],"best_for":["Startups and small teams without ML infrastructure expertise","Applications with variable or bursty punctuation workloads","Developers building SaaS products requiring punctuation as a microservice","Teams in regions with limited GPU availability (HuggingFace provides US-based endpoints)"],"limitations":["Network latency adds 100-500ms per request depending on geographic location and API load","Requires active internet connection — cannot operate in offline or air-gapped environments","API rate limits and quota management needed for high-throughput applications (>100 requests/second)","Model inference runs on HuggingFace infrastructure — no guarantee of response time SLA for free tier","Batch job processing may queue for hours during peak usage periods"],"requires":["HuggingFace API token (free or paid account)","Python 3.7+ with requests or httpx library","Network connectivity to huggingface.co","Optional: transformers library for local pipeline fallback"],"input_types":["JSON payload with text field","Batch JSONL files (one JSON object per line)","Raw text strings via REST API"],"output_types":["JSON response with punctuated text and token-level scores","Batch job status and results (async processing)","Streaming response for real-time applications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kredor--punctuate-all__cap_2","uri":"capability://text.generation.language.cross.lingual.punctuation.prediction.with.xlm.roberta.embeddings","name":"cross-lingual punctuation prediction with xlm-roberta embeddings","description":"Uses XLM-RoBERTa's multilingual contextual embeddings to predict punctuation across 100+ languages without language-specific fine-tuning. The model encodes input tokens into dense vector representations capturing semantic and syntactic context, then applies a classification head to predict punctuation labels. Shared embedding space enables zero-shot or few-shot transfer to languages not explicitly in training data.","intents":["I need to punctuate text in languages beyond English without training separate models","I want to handle code-switched or multilingual documents with a single model","I'm building a system that processes user input in unknown languages and needs punctuation restoration","I need to evaluate punctuation prediction performance across diverse language families"],"best_for":["Multilingual NLP teams working across diverse language pairs","Applications serving global audiences with unpredictable language distributions","Researchers studying cross-lingual transfer learning and zero-shot punctuation","Organizations with limited resources for language-specific model training"],"limitations":["Performance varies significantly across languages — trained on Europarl (parliamentary speech), performs best on European languages and formal registers","Low-resource and non-Latin-script languages (e.g., Amharic, Thai, Vietnamese) may have degraded accuracy due to underrepresentation in training data","XLM-RoBERTa's 100-language coverage does not guarantee equal performance across all languages — some languages may see 10-20% accuracy drops vs. English","Shared embedding space may conflate punctuation conventions across languages with different rules (e.g., French spacing before colons vs. English)"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","8GB+ RAM for XLM-RoBERTa base model (12GB+ for large variant)","Optional: language detection library (langdetect, fasttext) for preprocessing"],"input_types":["raw text in any of 100+ supported languages","code-switched text (mixing multiple languages)","language-tagged text with explicit language hints"],"output_types":["punctuated text in original language","per-token punctuation confidence scores","language-specific punctuation statistics"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kredor--punctuate-all__cap_3","uri":"capability://data.processing.analysis.token.level.punctuation.classification.with.bio.sequence.labeling","name":"token-level punctuation classification with bio sequence labeling","description":"Implements BIO (Begin-Inside-Outside) sequence labeling scheme where each token is classified as Outside (no punctuation), Begin (punctuation follows), or Inside (continuation of punctuation span). The model outputs per-token classification probabilities, enabling downstream applications to make confidence-based decisions about punctuation insertion. Supports both greedy decoding (highest probability label) and Viterbi decoding (globally optimal label sequence).","intents":["I need fine-grained token-level punctuation predictions with confidence scores for downstream filtering","I want to implement custom punctuation insertion logic based on model confidence thresholds","I'm building a system that needs to explain which tokens triggered punctuation decisions","I need to handle edge cases by adjusting punctuation based on token-level probabilities"],"best_for":["NLP engineers building interpretable text processing pipelines","Teams requiring confidence-based filtering or uncertainty quantification","Developers implementing custom punctuation rules on top of model predictions","Researchers analyzing token-level linguistic patterns in punctuation"],"limitations":["BIO scheme assumes punctuation follows tokens — cannot handle leading punctuation or complex punctuation patterns (e.g., nested parentheses)","Token-level predictions are independent of global sentence structure — may produce grammatically incorrect punctuation sequences","Confidence scores reflect model uncertainty but not linguistic correctness — high confidence does not guarantee accurate punctuation","Viterbi decoding adds ~10-20% inference latency compared to greedy decoding"],"requires":["Python 3.7+","transformers library 4.0+ with token-classification pipeline","PyTorch 1.9+ or TensorFlow 2.4+","Optional: seqeval library for evaluation metrics"],"input_types":["tokenized text (list of tokens)","raw text (auto-tokenized by model)","pre-tokenized sequences with token boundaries"],"output_types":["BIO labels per token (Outside, Begin-Period, Begin-Comma, etc.)","per-token classification probabilities (0-1 confidence scores)","globally optimal label sequence (Viterbi decoding)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kredor--punctuate-all__cap_4","uri":"capability://tool.use.integration.local.model.inference.with.transformers.pipeline.abstraction","name":"local model inference with transformers pipeline abstraction","description":"Provides direct integration with HuggingFace transformers library's pipeline API, enabling zero-configuration local inference without API calls. The model is registered in HuggingFace Model Hub with config.json and model weights, allowing developers to instantiate a pipeline with a single line of code: `pipeline('token-classification', model='kredor/punctuate-all')`. Supports CPU and GPU inference with automatic device detection and mixed-precision (fp16) optimization.","intents":["I want to run punctuation restoration locally without external API dependencies or network calls","I need to integrate punctuation into an offline application or air-gapped environment","I'm building a production system and want to avoid API latency and rate limits","I need to fine-tune or adapt the punctuation model to my specific domain"],"best_for":["Developers building offline-first or privacy-sensitive applications","Teams with on-premise infrastructure or air-gapped deployments","Production systems requiring sub-100ms latency and high throughput","Researchers fine-tuning or extending the model for domain adaptation"],"limitations":["Requires local GPU (8GB+ VRAM) or CPU (slow inference, 1-5 seconds per 512 tokens) for reasonable performance","Model weights (~560MB for base variant) must be downloaded and cached locally, adding storage overhead","No automatic updates — developers must manually pull new model versions from HuggingFace Hub","Requires Python environment setup and dependency management (PyTorch, transformers) — higher barrier to entry than API-based solutions","Single-machine inference — no built-in distributed inference or load balancing"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","4GB+ RAM (CPU inference) or 8GB+ VRAM (GPU inference)","~600MB disk space for model weights","Optional: CUDA 11.0+ for GPU acceleration"],"input_types":["raw text strings","pre-tokenized token lists","batch of texts (list of strings)"],"output_types":["list of token-level predictions with entity labels","aggregated punctuated text (with post-processing)","raw model logits and attention weights (for analysis)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-kredor--punctuate-all__cap_5","uri":"capability://code.generation.editing.fine.tuning.and.domain.adaptation.on.custom.punctuation.datasets","name":"fine-tuning and domain adaptation on custom punctuation datasets","description":"Model architecture and weights are fully compatible with HuggingFace transformers Trainer API, enabling developers to fine-tune on domain-specific punctuation data. Supports standard supervised fine-tuning workflows: load pretrained weights, prepare labeled dataset in BIO format, configure training hyperparameters, and optimize on custom data. Includes support for mixed-precision training (fp16), gradient accumulation, and distributed training across multiple GPUs.","intents":["I need to adapt the punctuation model to my domain (medical, legal, technical) where Europarl training data doesn't apply","I want to improve punctuation accuracy on informal text, chat, or domain-specific language","I'm building a specialized system and need to fine-tune the model on proprietary data","I need to optimize the model for specific punctuation patterns in my use case"],"best_for":["Teams with domain-specific punctuation requirements and labeled training data","Organizations with proprietary datasets and privacy constraints preventing API-based solutions","Researchers studying domain adaptation and transfer learning in punctuation","Companies building vertical-specific NLP products (legal tech, medical transcription, etc.)"],"limitations":["Requires labeled training data in BIO format — annotation effort scales with domain complexity and data volume","Fine-tuning on small datasets (<1000 examples) risks overfitting — requires careful hyperparameter tuning and validation","Training infrastructure needed: GPU with 8GB+ VRAM, 4-8 hours for full fine-tuning on 10K examples","No built-in active learning or data augmentation — developers must handle data preparation and quality control","Fine-tuned models may lose generalization to other domains — requires careful evaluation on held-out test sets"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","GPU with 8GB+ VRAM (or CPU for small datasets, very slow)","Labeled dataset in BIO format (minimum 500-1000 examples for meaningful fine-tuning)","Optional: datasets library for data loading, wandb for experiment tracking"],"input_types":["BIO-formatted training data (text + token-level labels)","CoNLL-2003 format files","Custom JSON/CSV with text and punctuation annotations"],"output_types":["fine-tuned model weights (PyTorch .bin files)","training metrics (loss, F1, precision, recall per punctuation type)","evaluation results on validation/test sets"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.0+","HuggingFace account (optional, for Inference API access)","4GB+ RAM for local inference","HuggingFace API token (free or paid account)","Python 3.7+ with requests or httpx library","Network connectivity to huggingface.co","Optional: transformers library for local pipeline fallback","8GB+ RAM for XLM-RoBERTa base model (12GB+ for large variant)"],"failure_modes":["Token-classification approach processes text sequentially, adding ~50-200ms latency per 512-token chunk depending on hardware","Model trained on Europarl dataset (parliamentary speech) — may underperform on informal text, technical jargon, 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