{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-segment-any-text--sat-3l-sm","slug":"segment-any-text--sat-3l-sm","name":"sat-3l-sm","type":"model","url":"https://huggingface.co/segment-any-text/sat-3l-sm","page_url":"https://unfragile.ai/segment-any-text--sat-3l-sm","categories":["model-training"],"tags":["transformers","onnx","safetensors","xlm-token","token-classification","multilingual","am","ar","az","be","bg","bn","ca","ceb","cs","cy","da","de","el","en"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-segment-any-text--sat-3l-sm__cap_0","uri":"capability://data.processing.analysis.multilingual.token.level.text.segmentation.and.classification","name":"multilingual token-level text segmentation and classification","description":"Performs token-classification on text across 20+ languages using a transformer-based architecture (likely XLM-RoBERTa or similar multilingual encoder). The model tokenizes input text, passes it through stacked transformer layers, and outputs per-token classification labels (e.g., BIO tags for named entities, sentence boundaries, or semantic segments). Supports inference via HuggingFace Transformers library with ONNX and SafeTensors format options for optimized deployment.","intents":["Segment multilingual text into meaningful units (sentences, phrases, entities) without language-specific preprocessing","Extract and classify tokens across diverse languages in a single unified model","Deploy token classification in production with ONNX runtime for low-latency inference","Fine-tune a pretrained multilingual token classifier on domain-specific text segmentation tasks"],"best_for":["NLP teams building multilingual text processing pipelines","Researchers working on cross-lingual NER, chunking, or boundary detection","Developers deploying token classification at scale with ONNX optimization requirements","Organizations needing language-agnostic text segmentation without maintaining separate models per language"],"limitations":["Token-level predictions may struggle with subword tokenization artifacts (##tokens in BERT-style tokenizers) requiring post-processing to map back to word-level boundaries","Performance varies significantly across the 20 supported languages; likely optimized for high-resource languages (en, de, fr) with degradation on low-resource variants (am, az, ceb)","No built-in handling of code-switching or mixed-language text; treats each token independently without cross-lingual context awareness","Requires full text to be tokenized and passed through all transformer layers; no streaming or incremental inference capability","Model size and latency not specified; 3-layer architecture suggests smaller model but exact throughput/memory footprint unknown"],"requires":["Python 3.7+","transformers library (HuggingFace) version 4.0+","PyTorch or TensorFlow backend (depending on model variant)","ONNX Runtime 1.10+ (optional, for ONNX inference path)","Sufficient GPU memory or CPU for inference (model size ~100-300MB estimated for 3-layer transformer)"],"input_types":["raw text (string)","pre-tokenized sequences (list of tokens)","text with language tags (for explicit language specification)"],"output_types":["token-level classification labels (list of strings, e.g., ['B-PER', 'I-PER', 'O'])","token logits/probabilities (raw model outputs for confidence scoring)","aligned token-to-character mappings (for mapping predictions back to original text spans)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-segment-any-text--sat-3l-sm__cap_1","uri":"capability://automation.workflow.onnx.optimized.inference.for.edge.and.production.deployment","name":"onnx-optimized inference for edge and production deployment","description":"Exports the transformer model to ONNX (Open Neural Network Exchange) format, enabling hardware-agnostic inference across CPUs, GPUs, and specialized accelerators (TPUs, NPUs). ONNX Runtime applies graph optimizations (operator fusion, constant folding, quantization-aware transformations) to reduce model size and latency. SafeTensors format provides secure, memory-mapped weight loading without arbitrary code execution risks.","intents":["Deploy token classification models to edge devices (mobile, embedded systems) with minimal latency","Reduce inference latency and memory footprint for high-throughput production serving","Avoid vendor lock-in by using standardized ONNX format across different inference engines","Safely load model weights without pickle deserialization vulnerabilities"],"best_for":["Production ML teams optimizing inference cost and latency","Edge AI developers targeting mobile or IoT deployments","Security-conscious organizations avoiding pickle-based model loading","Multi-cloud or heterogeneous hardware environments requiring portable model formats"],"limitations":["ONNX conversion may lose some dynamic control flow or custom operations not supported by ONNX opset; requires model architecture compatibility verification","Quantization (if applied) can degrade token classification accuracy by 1-5% depending on quantization bit-width and calibration data","ONNX Runtime performance gains vary by hardware; CPU inference may see only 10-20% speedup vs PyTorch, while GPU gains are typically 20-40%","SafeTensors format is read-only; requires re-export from PyTorch if model needs retraining or modification"],"requires":["ONNX Runtime 1.10+","transformers library with ONNX export support (4.20+)","Optional: ONNX conversion tools (onnxruntime-tools, optimum library)","Hardware-specific ONNX Runtime builds (e.g., onnxruntime-gpu for CUDA, onnxruntime-silicon for Apple Silicon)"],"input_types":["ONNX model file (.onnx)","SafeTensors weight file (.safetensors)","tokenized input tensors (int64 token IDs, attention masks)"],"output_types":["logits tensor (float32, shape [batch_size, sequence_length, num_classes])","token classification predictions (int64 class indices)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-segment-any-text--sat-3l-sm__cap_2","uri":"capability://memory.knowledge.cross.lingual.transfer.learning.via.pretrained.multilingual.embeddings","name":"cross-lingual transfer learning via pretrained multilingual embeddings","description":"Leverages a pretrained multilingual transformer (likely XLM-RoBERTa or mBERT) that has learned shared semantic representations across 20+ languages during pretraining on massive multilingual corpora. Token classification predictions are grounded in these cross-lingual embeddings, enabling zero-shot or few-shot transfer to unseen languages and domains. The 3-layer architecture balances parameter efficiency with sufficient capacity to capture language-specific and universal linguistic patterns.","intents":["Apply token classification to languages not explicitly seen during fine-tuning (zero-shot cross-lingual transfer)","Fine-tune on high-resource language data and transfer to low-resource languages with minimal additional training","Detect and classify multilingual code-mixed text by leveraging shared embedding space","Reduce training data requirements for new languages by leveraging pretrained multilingual knowledge"],"best_for":["Multilingual NLP teams with limited labeled data for low-resource languages","Researchers studying cross-lingual transfer and zero-shot generalization","Organizations supporting diverse language markets without maintaining separate models","Projects requiring rapid deployment to new languages without extensive retraining"],"limitations":["Zero-shot transfer accuracy degrades significantly for linguistically distant language pairs (e.g., English to Amharic) or morphologically complex languages","Pretrained multilingual embeddings may encode language-specific biases or underrepresent low-resource languages due to imbalanced pretraining data","Cross-lingual transfer assumes task similarity across languages; domain shift (e.g., medical text in one language, news in another) can hurt performance","No explicit mechanism for language-specific fine-tuning; all 20+ languages share the same token classification head, limiting per-language optimization"],"requires":["Pretrained multilingual transformer checkpoint (XLM-RoBERTa, mBERT, or equivalent)","Labeled token classification data in at least one language for fine-tuning","transformers library with multilingual model support","GPU memory for fine-tuning (typically 8GB+ for batch size 16-32)"],"input_types":["raw text in any of the 20+ supported languages","code-mixed text combining multiple languages","text with language identifiers (optional, for explicit language specification)"],"output_types":["token-level classification labels (language-agnostic)","confidence scores per token and language","cross-lingual alignment information (tokens in language A mapped to semantically equivalent tokens in language B)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-segment-any-text--sat-3l-sm__cap_3","uri":"capability://data.processing.analysis.batch.token.classification.with.configurable.output.formats","name":"batch token classification with configurable output formats","description":"Processes multiple text sequences in parallel through the transformer model, returning per-token predictions in configurable formats (BIO tags, BIOES, flat labels, or raw logits). Supports batching to amortize model loading and leverage GPU parallelism. Output can be aligned back to character-level spans in the original text for downstream consumption (e.g., entity extraction, sentence splitting).","intents":["Classify tokens in multiple documents simultaneously for throughput optimization","Convert model predictions to standard NER/chunking formats (BIO, BIOES) for compatibility with downstream tools","Map token-level predictions back to character offsets in the original text for span-based extraction","Export raw logits for confidence-based filtering or ensemble methods"],"best_for":["Data processing pipelines requiring high-throughput token classification","Teams integrating token classification into existing NER/chunking workflows","Researchers analyzing model confidence and uncertainty across large corpora","Production systems needing flexible output formats for different downstream consumers"],"limitations":["Batch processing requires padding sequences to the same length, increasing computation for variable-length inputs; dynamic batching not explicitly supported","Token-to-character alignment requires careful handling of subword tokenization (##tokens); misalignment can occur with special characters or whitespace","Output format conversion (e.g., BIO to BIOES) is post-hoc and may not handle edge cases (consecutive entities, nested spans) correctly without custom logic","No built-in confidence thresholding; raw logits require manual softmax and threshold application"],"requires":["transformers library with batch inference support","PyTorch or TensorFlow for tensor operations","Optional: tokenizers library for precise token-to-character mapping","GPU memory proportional to batch size × max sequence length (typically 8GB+ for batch size 32, sequence length 512)"],"input_types":["list of text strings (variable length)","pre-tokenized sequences with token IDs","text with language tags for multilingual batches"],"output_types":["BIO/BIOES tag sequences (list of lists)","flat label sequences (list of lists)","raw logits (float32 tensors, shape [batch_size, sequence_length, num_classes])","character-level span annotations (list of dicts with start, end, label, confidence)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-segment-any-text--sat-3l-sm__cap_4","uri":"capability://data.processing.analysis.language.agnostic.token.boundary.detection.and.segmentation","name":"language-agnostic token boundary detection and segmentation","description":"Identifies token boundaries and semantic segments (e.g., sentence boundaries, phrase boundaries, entity spans) across languages without language-specific rules or preprocessing. The model learns universal linguistic patterns (punctuation, whitespace, morphological boundaries) during multilingual pretraining, enabling consistent segmentation across typologically diverse languages (e.g., English, Arabic, Chinese-adjacent scripts).","intents":["Segment text into sentences or phrases without language-specific sentence splitters","Detect word/token boundaries in languages without explicit whitespace (e.g., Chinese, Japanese, Thai) or with complex morphology (e.g., German, Finnish)","Identify semantic boundaries (clause boundaries, entity spans) in a language-agnostic manner","Preprocess text for downstream NLP tasks (parsing, translation, summarization) with consistent tokenization"],"best_for":["Multilingual text processing pipelines avoiding language-specific preprocessing","Organizations processing diverse scripts and writing systems (Latin, Arabic, Devanagari, CJK)","Researchers studying universal linguistic patterns across languages","Systems requiring consistent tokenization across 20+ languages without maintaining language-specific rules"],"limitations":["Boundary detection may fail on non-standard text (social media, code-mixed, transliterated text) due to distribution shift from pretraining data","Languages with complex morphology (Turkish, Finnish, Hungarian) may require post-processing to split compound words or agglutinated forms","No explicit handling of punctuation ambiguity (e.g., periods in abbreviations, ellipsis); requires context-aware post-processing","Segmentation granularity is fixed at the token level; no configurable segment sizes (e.g., subword vs. word vs. phrase)"],"requires":["Pretrained multilingual transformer with token classification head","transformers library for inference","Text in one of the 20+ supported languages","Optional: language identification model for explicit language specification"],"input_types":["raw text (any of 20+ languages)","text with language tags","pre-tokenized text (if using custom tokenizer)"],"output_types":["token-level boundary labels (e.g., 'B-SEGMENT', 'I-SEGMENT', 'O')","segmented text (list of segments/spans)","character-level boundary offsets"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library (HuggingFace) version 4.0+","PyTorch or TensorFlow backend (depending on model variant)","ONNX Runtime 1.10+ (optional, for ONNX inference path)","Sufficient GPU memory or CPU for inference (model size ~100-300MB estimated for 3-layer transformer)","ONNX Runtime 1.10+","transformers library with ONNX export support (4.20+)","Optional: ONNX conversion tools (onnxruntime-tools, optimum library)","Hardware-specific ONNX Runtime builds (e.g., onnxruntime-gpu for CUDA, onnxruntime-silicon for Apple Silicon)","Pretrained multilingual transformer checkpoint (XLM-RoBERTa, mBERT, or equivalent)"],"failure_modes":["Token-level predictions may struggle with subword tokenization artifacts (##tokens in BERT-style tokenizers) requiring post-processing to map back to word-level boundaries","Performance varies significantly across the 20 supported languages; likely optimized for high-resource languages (en, de, fr) with degradation on low-resource variants (am, az, ceb)","No built-in handling of code-switching or mixed-language text; treats each token independently without cross-lingual context awareness","Requires full text to be tokenized and passed through all transformer layers; no streaming or incremental inference capability","Model size and latency not specified; 3-layer architecture suggests smaller model but exact throughput/memory footprint unknown","ONNX conversion may lose some dynamic control flow or custom operations not supported by ONNX opset; requires model architecture compatibility verification","Quantization (if applied) can degrade token classification accuracy by 1-5% depending on quantization bit-width and calibration data","ONNX Runtime performance gains vary by hardware; CPU inference may see only 10-20% speedup vs PyTorch, while GPU gains are typically 20-40%","SafeTensors format is read-only; requires re-export from PyTorch if model needs retraining or modification","Zero-shot transfer accuracy degrades significantly for linguistically distant language pairs (e.g., English to Amharic) or morphologically complex languages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5691776017867048,"quality":0.2,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:23:01.785Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":290595,"model_likes":10}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=segment-any-text--sat-3l-sm","compare_url":"https://unfragile.ai/compare?artifact=segment-any-text--sat-3l-sm"}},"signature":"okLNC6ooGbwnq5cZPlJd2TDpR3nR09l0vy4LdotUBJ2pJBX9qUvPNX63m1Qu9l7o0xyfAg1SuHvDi/3/YP6ECQ==","signedAt":"2026-06-20T01:40:16.400Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/segment-any-text--sat-3l-sm","artifact":"https://unfragile.ai/segment-any-text--sat-3l-sm","verify":"https://unfragile.ai/api/v1/verify?slug=segment-any-text--sat-3l-sm","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"}}