{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-covalenthq--cryptoner","slug":"covalenthq--cryptoner","name":"cryptoNER","type":"model","url":"https://huggingface.co/covalenthq/cryptoNER","page_url":"https://unfragile.ai/covalenthq--cryptoner","categories":["data-analysis"],"tags":["transformers","pytorch","xlm-roberta","token-classification","generated_from_trainer","NER","crypto","base_model:FacebookAI/xlm-roberta-base","base_model:finetune:FacebookAI/xlm-roberta-base","license:mit","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-covalenthq--cryptoner__cap_0","uri":"capability://data.processing.analysis.multilingual.cryptocurrency.entity.recognition","name":"multilingual-cryptocurrency-entity-recognition","description":"Identifies and classifies cryptocurrency-specific named entities (wallet addresses, token names, exchange names, contract addresses) across 100+ languages using XLM-RoBERTa's multilingual transformer backbone. The model performs token-level classification by fine-tuning FacebookAI/xlm-roberta-base on cryptocurrency domain data, enabling it to recognize crypto entities even in non-English text through shared cross-lingual embeddings learned during pre-training.","intents":["Extract cryptocurrency mentions and entities from multilingual blockchain documentation or social media","Identify wallet addresses, token symbols, and exchange names in unstructured text across different languages","Build a pipeline to automatically tag crypto-related entities in compliance or risk monitoring systems","Parse cryptocurrency transaction descriptions or chat logs to extract relevant entity references"],"best_for":["blockchain analytics teams building compliance and monitoring systems","cryptocurrency research platforms needing entity extraction across global sources","developers building multilingual crypto news aggregators or sentiment analysis tools","teams processing international blockchain documentation or community discussions"],"limitations":["Token-level classification means it cannot handle entity relationships or coreference resolution — only identifies individual tokens as entity types","Performance may degrade on rare or newly-created cryptocurrency tokens not well-represented in training data","Requires pre-tokenization compatible with XLM-RoBERTa's WordPiece tokenizer; custom or emerging crypto terminology may be split into subword tokens","No built-in handling of context-dependent entity disambiguation — same token may be classified identically regardless of surrounding context nuance","Multilingual capability comes with trade-off: model size and inference latency are higher than single-language alternatives"],"requires":["PyTorch 1.9+","Transformers library 4.0+","HuggingFace Datasets library for batch processing","GPU with 2GB+ VRAM for efficient inference (CPU inference supported but slower)","Input text must be pre-tokenized or compatible with AutoTokenizer from transformers"],"input_types":["raw text (English or 100+ other languages)","pre-tokenized sequences","text with variable length (model handles padding/truncation)"],"output_types":["token-level classification labels (BIO or BIOES tagging scheme)","confidence scores per token","structured entity spans with start/end positions and entity type"],"categories":["data-processing-analysis","ner","domain-specific-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-covalenthq--cryptoner__cap_1","uri":"capability://data.processing.analysis.cross.lingual.token.classification.with.shared.embeddings","name":"cross-lingual-token-classification-with-shared-embeddings","description":"Performs token-level sequence labeling by leveraging XLM-RoBERTa's shared multilingual embedding space, where tokens from different languages map to semantically similar positions in a 768-dimensional vector space. The model classifies each token independently using a linear classification head on top of contextualized embeddings, enabling zero-shot transfer to unseen languages through the shared embedding geometry learned during XLM-RoBERTa's pre-training on 100+ languages.","intents":["Apply a trained cryptocurrency NER model to text in languages not explicitly seen during fine-tuning","Process mixed-language or code-switched text containing both English and other languages","Reduce annotation effort by training on English crypto data and automatically generalizing to other languages","Build a single model that handles global cryptocurrency discussions without language-specific variants"],"best_for":["international blockchain platforms processing user-generated content in multiple languages","research teams studying cryptocurrency adoption across non-English speaking regions","compliance systems monitoring global crypto exchanges and communities","developers building language-agnostic crypto data extraction pipelines"],"limitations":["Zero-shot transfer quality degrades for languages with very different linguistic structures or scripts not well-represented in XLM-RoBERTa's pre-training","Shared embedding space means the model cannot learn language-specific entity patterns — all languages must fit the same classification boundaries","Performance on low-resource languages (e.g., minority languages) is significantly lower than on high-resource languages like English, Spanish, or Mandarin","No explicit language identification — model processes all input uniformly regardless of actual language, potentially causing confusion on code-switched text"],"requires":["XLM-RoBERTa tokenizer (AutoTokenizer.from_pretrained('xlm-roberta-base'))","Input text in any of 100+ supported languages or language mixtures","Transformers library 4.0+ with ONNX export support for production deployment"],"input_types":["monolingual text in any XLM-RoBERTa supported language","code-switched text mixing multiple languages","text with non-Latin scripts (Arabic, Chinese, Cyrillic, etc.)"],"output_types":["per-token classification labels with entity type","confidence scores indicating model uncertainty per token","token-to-entity mapping preserving original text positions"],"categories":["data-processing-analysis","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-covalenthq--cryptoner__cap_2","uri":"capability://data.processing.analysis.fine.tuned.transformer.sequence.labeling.with.contextualized.embeddings","name":"fine-tuned-transformer-sequence-labeling-with-contextualized-embeddings","description":"Applies domain-specific fine-tuning to XLM-RoBERTa's pre-trained transformer backbone using supervised learning on cryptocurrency-annotated text. The model generates contextualized token embeddings (where each token's representation depends on surrounding context) and passes them through a linear classification layer to predict entity labels. Fine-tuning updates all transformer weights via backpropagation on the cryptocurrency NER task, adapting the general-purpose language model to recognize crypto-specific patterns.","intents":["Use a pre-trained, ready-to-deploy model without needing to train from scratch on cryptocurrency data","Leverage transfer learning to achieve high accuracy on crypto NER with minimal additional training data","Integrate a production-ready model into inference pipelines without custom training infrastructure","Access a model optimized for the specific cryptocurrency domain rather than generic text"],"best_for":["teams deploying NER systems without in-house ML infrastructure or annotation expertise","startups building crypto analytics products and needing fast time-to-market","researchers studying cryptocurrency discourse without access to large labeled datasets","developers integrating NER into existing applications via HuggingFace Model Hub"],"limitations":["Fine-tuning is fixed — the model cannot adapt to new cryptocurrency entities or domain shifts without retraining","Inference latency is ~100-300ms per document on CPU (depending on text length), making real-time processing of high-volume streams challenging without GPU acceleration","Model size is 558MB (XLM-RoBERTa-base), requiring significant storage and memory for edge deployment or resource-constrained environments","Fine-tuning data distribution bias: if training data overrepresents certain cryptocurrencies or languages, model performance will be skewed toward those domains"],"requires":["HuggingFace Transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Minimum 2GB RAM for inference, 8GB+ for batch processing","Optional: GPU (CUDA 11.0+) for production inference at scale"],"input_types":["raw text strings of variable length","pre-tokenized sequences with token IDs","batched input (multiple documents processed simultaneously)"],"output_types":["entity type labels per token (e.g., B-TOKEN, I-WALLET, O)","logits/probabilities for each entity class per token","decoded entity spans with confidence scores"],"categories":["data-processing-analysis","transfer-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-covalenthq--cryptoner__cap_3","uri":"capability://automation.workflow.batch.inference.with.automatic.tokenization.and.padding","name":"batch-inference-with-automatic-tokenization-and-padding","description":"Processes multiple documents simultaneously through the model using HuggingFace's pipeline abstraction, which handles tokenization, padding, batching, and output decoding automatically. The pipeline manages variable-length inputs by padding shorter sequences and truncating longer ones to a maximum length, then aggregates predictions across the batch for efficient GPU utilization. Output is automatically decoded from token-level labels back to human-readable entity spans with character offsets.","intents":["Process large collections of cryptocurrency documents (news, social media, blockchain data) without manual tokenization","Extract entities from variable-length texts without worrying about padding or truncation logic","Achieve efficient inference on GPUs by batching multiple documents together","Get entity spans with original text positions without manual post-processing"],"best_for":["data engineering teams building ETL pipelines for cryptocurrency data extraction","researchers processing large corpora of blockchain-related text","production systems needing efficient batch processing of incoming documents","developers without deep NLP expertise who need simple, high-level APIs"],"limitations":["Automatic padding and truncation may lose information for very long documents (>512 tokens) — truncation is lossy and may cut entity spans mid-token","Batch size is limited by GPU memory; typical batch sizes are 8-32 documents depending on document length and hardware","Pipeline abstraction adds overhead (~10-20ms per batch) compared to raw model inference, making it suboptimal for latency-critical applications","No built-in support for sliding window inference on long documents — documents longer than 512 tokens are truncated rather than processed in overlapping chunks"],"requires":["HuggingFace Transformers library 4.0+","PyTorch or TensorFlow backend","Input: list of text strings (any length)","Optional: GPU for batch processing (CPU works but is slower)"],"input_types":["list of text strings","single text string","file paths to text documents"],"output_types":["list of entity predictions per document","entity spans with character offsets and confidence scores","aggregated entity counts or statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-covalenthq--cryptoner__cap_4","uri":"capability://data.processing.analysis.entity.span.extraction.with.character.offset.mapping","name":"entity-span-extraction-with-character-offset-mapping","description":"Converts token-level classification predictions back to entity spans in the original text by tracking character offsets through the tokenization process. The model maintains a mapping between token indices and their positions in the original text, allowing it to reconstruct entity boundaries (start and end character positions) from token-level labels. This enables downstream systems to directly reference entities in the source text without manual span reconstruction.","intents":["Extract cryptocurrency entities with exact positions in source text for highlighting or annotation","Link extracted entities back to original documents for audit trails or compliance reporting","Build entity-aware text processing pipelines that need to preserve source text references","Create training data for downstream tasks by extracting entity spans with their original context"],"best_for":["document processing systems that need to highlight or annotate entities in source text","compliance and audit systems requiring traceability of extracted entities to source documents","data labeling pipelines that use model predictions as weak supervision","information extraction systems building knowledge graphs from text"],"limitations":["Character offset mapping assumes the original text is preserved unchanged — any text normalization or cleaning breaks the mapping","Subword tokenization (WordPiece) can split entities across multiple tokens, requiring heuristics to reconstruct entity boundaries that may be ambiguous","Offset mapping is only accurate for the specific tokenizer used during fine-tuning — using a different tokenizer will produce incorrect offsets","Special tokens (CLS, SEP, PAD) have no meaningful character offsets and must be filtered out before span reconstruction"],"requires":["Original text preserved exactly as input to the model (no preprocessing or normalization)","HuggingFace Transformers library with offset_mapping support (4.0+)","Token-level predictions with entity labels (BIO or BIOES format)"],"input_types":["original text string","token-level classification labels","tokenizer with offset_mapping capability"],"output_types":["entity spans with (start_char, end_char) positions","entity text extracted from original document","entity type and confidence score per span"],"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":["PyTorch 1.9+","Transformers library 4.0+","HuggingFace Datasets library for batch processing","GPU with 2GB+ VRAM for efficient inference (CPU inference supported but slower)","Input text must be pre-tokenized or compatible with AutoTokenizer from transformers","XLM-RoBERTa tokenizer (AutoTokenizer.from_pretrained('xlm-roberta-base'))","Input text in any of 100+ supported languages or language mixtures","Transformers library 4.0+ with ONNX export support for production deployment","HuggingFace Transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+"],"failure_modes":["Token-level classification means it cannot handle entity relationships or coreference resolution — only identifies individual tokens as entity types","Performance may degrade on rare or newly-created cryptocurrency tokens not well-represented in training data","Requires pre-tokenization compatible with XLM-RoBERTa's WordPiece tokenizer; custom or emerging crypto terminology may be split into subword tokens","No built-in handling of context-dependent entity disambiguation — same token may be classified identically regardless of surrounding context nuance","Multilingual capability comes with trade-off: model size and inference latency are higher than single-language alternatives","Zero-shot transfer quality degrades for languages with very different linguistic structures or scripts not well-represented in XLM-RoBERTa's pre-training","Shared embedding space means the model cannot learn language-specific entity patterns — all languages must fit the same classification boundaries","Performance on low-resource languages (e.g., minority languages) is significantly lower than on high-resource languages like English, Spanish, or Mandarin","No explicit language identification — model processes all input uniformly regardless of actual language, potentially causing confusion on code-switched text","Fine-tuning is fixed — the model cannot adapt to new cryptocurrency entities or domain shifts without retraining","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5672113911721213,"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":"2026-04-22T08:08:28.377Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":248869,"model_likes":15}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=covalenthq--cryptoner","compare_url":"https://unfragile.ai/compare?artifact=covalenthq--cryptoner"}},"signature":"VPumc9CMQ5BFWr5J6TKre6lTP5LwcuuJQcdkdR2mX0vesOnsFKFK4EJutc520jvUvpTWBZGopMxk/JzHDAwCAA==","signedAt":"2026-06-22T07:50:46.051Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/covalenthq--cryptoner","artifact":"https://unfragile.ai/covalenthq--cryptoner","verify":"https://unfragile.ai/api/v1/verify?slug=covalenthq--cryptoner","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"}}