{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-davlan--xlm-roberta-large-ner-hrl","slug":"davlan--xlm-roberta-large-ner-hrl","name":"xlm-roberta-large-ner-hrl","type":"model","url":"https://huggingface.co/Davlan/xlm-roberta-large-ner-hrl","page_url":"https://unfragile.ai/davlan--xlm-roberta-large-ner-hrl","categories":["model-training"],"tags":["transformers","pytorch","tf","safetensors","xlm-roberta","token-classification","license:afl-3.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-davlan--xlm-roberta-large-ner-hrl__cap_0","uri":"capability://data.processing.analysis.multilingual.named.entity.recognition.with.token.level.classification","name":"multilingual named entity recognition with token-level classification","description":"Performs token-level sequence labeling across 10+ languages using XLM-RoBERTa-large's transformer architecture, which applies cross-lingual transfer learning through masked language modeling on 100+ languages. The model classifies each token in input text into entity categories (person, location, organization, etc.) by computing contextual embeddings via 24 transformer layers and applying a linear classification head on top of each token's hidden state. Supports both PyTorch and TensorFlow inference with safetensors serialization for deterministic model loading.","intents":["Extract named entities from multilingual documents without language-specific preprocessing","Build information extraction pipelines that work across diverse language pairs without retraining","Identify person, location, and organization mentions in non-English text for knowledge graph construction","Perform entity linking and coreference resolution as a preprocessing step for downstream NLP tasks"],"best_for":["NLP teams building multilingual information extraction systems","Researchers prototyping cross-lingual entity recognition without language-specific annotation","Production systems requiring entity extraction across African languages (Hausa, Yoruba, Igbo) and other underrepresented languages"],"limitations":["Token-level predictions require post-processing to reconstruct entity spans from BIO/BIOES tags; no built-in span merging","Performance degrades on code-mixed text (e.g., Hinglish) due to training data composition","Model size (560M parameters) requires 2GB+ GPU memory; CPU inference is ~10-50x slower depending on sequence length","No confidence scores or uncertainty quantification per token; only hard class predictions","Training data is primarily news/Wikipedia; performance on domain-specific text (medical, legal) is not characterized"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","2GB+ GPU VRAM for batch inference, or CPU with 8GB+ RAM for single-sample inference"],"input_types":["raw text strings","pre-tokenized sequences (token lists)","text with existing whitespace/punctuation"],"output_types":["token-level class labels (BIO/BIOES tags)","entity spans with character offsets","logits for each token-class pair (via model.forward())"],"categories":["data-processing-analysis","nlp-information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--xlm-roberta-large-ner-hrl__cap_1","uri":"capability://data.processing.analysis.cross.lingual.transfer.learning.via.transformer.embeddings","name":"cross-lingual transfer learning via transformer embeddings","description":"Leverages XLM-RoBERTa's pre-trained cross-lingual embeddings (trained on 100+ languages via masked language modeling) to enable entity recognition in languages not explicitly present in the NER fine-tuning data. The model maps input tokens to a shared 1024-dimensional embedding space where semantic and syntactic patterns are language-agnostic, allowing a classifier trained on English/Hausa/Yoruba to generalize to unseen languages like Swahili or Amharic. This is achieved through the transformer's self-attention mechanism, which learns language-invariant representations during pre-training.","intents":["Recognize entities in languages with no labeled NER training data by leveraging cross-lingual transfer","Reduce annotation burden for new languages by reusing a single multilingual model","Build NER systems that scale to 50+ languages without language-specific engineering"],"best_for":["Organizations supporting global products across many languages with limited per-language annotation budgets","Research teams studying cross-lingual transfer and zero-shot generalization","Startups building multilingual content moderation or information extraction without language-specific ML expertise"],"limitations":["Transfer quality varies significantly by language pair; typologically distant languages (e.g., English→Chinese) show 5-15% F1 degradation vs in-language performance","Requires careful handling of script differences (Latin, Cyrillic, Arabic, CJK); tokenization may break for some writing systems","No explicit mechanism to handle language-specific entity types (e.g., Japanese honorifics, Arabic diacritics); may require post-processing","Cross-lingual embeddings are frozen after pre-training; fine-tuning on one language can degrade performance on others"],"requires":["transformers library 4.0+","understanding of BPE tokenization and subword handling","validation set in target language to measure transfer quality"],"input_types":["text in any of 100+ languages supported by XLM-RoBERTa","code-mixed text (with caveats on performance)"],"output_types":["entity class predictions for each token","embedding vectors (1024-dim) for downstream tasks"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--xlm-roberta-large-ner-hrl__cap_2","uri":"capability://automation.workflow.efficient.batch.inference.with.safetensors.serialization","name":"efficient batch inference with safetensors serialization","description":"Supports loading model weights from safetensors format (a memory-safe, deterministic serialization standard) and executing batch token classification on GPU or CPU. The model can process multiple sequences in parallel by padding them to a common length and computing attention masks, then classifying all tokens in a single forward pass. Safetensors format eliminates pickle deserialization vulnerabilities and enables faster model loading via memory-mapped I/O, reducing initialization latency from ~5s (pickle) to ~1s (safetensors) on typical hardware.","intents":["Deploy NER in production with sub-second model loading and deterministic behavior","Process large document batches (100s-1000s of sequences) efficiently on limited GPU memory","Integrate NER into microservices without security risks from untrusted model weights"],"best_for":["Production ML systems requiring fast model initialization and deterministic loading","Teams processing high-volume document streams (news aggregation, content moderation)","Security-conscious organizations avoiding pickle-based model deserialization"],"limitations":["Batch size is limited by GPU VRAM; typical max batch size is 8-32 for sequences of 128-512 tokens on 8GB GPUs","Padding to max sequence length in a batch wastes computation on short sequences; no dynamic batching or bucketing built-in","Safetensors loading requires transformers 4.26+; older codebases must upgrade","No streaming/chunked inference for very long documents; sequences >512 tokens must be split manually"],"requires":["transformers 4.26+","PyTorch 1.9+ or TensorFlow 2.4+","GPU with 2GB+ VRAM for batch inference, or CPU with 8GB+ RAM"],"input_types":["list of text strings","pre-tokenized sequences (token IDs)"],"output_types":["batch of token-level class predictions","logits tensors (batch_size, seq_length, num_classes)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--xlm-roberta-large-ner-hrl__cap_3","uri":"capability://tool.use.integration.framework.agnostic.inference.via.pytorch.and.tensorflow.backends","name":"framework-agnostic inference via pytorch and tensorflow backends","description":"Provides dual inference paths: native PyTorch (using torch.nn.Module) and TensorFlow (using tf.keras.Model), allowing deployment in either framework without retraining or conversion. The model weights are stored in a framework-agnostic format (safetensors) and automatically converted to the target framework's tensor types (torch.Tensor or tf.Tensor) on load. This enables teams to use their preferred inference stack (PyTorch for research, TensorFlow for production serving via TF Lite or TF Serving) without maintaining separate models.","intents":["Deploy the same NER model in PyTorch research pipelines and TensorFlow production services","Migrate from PyTorch to TensorFlow (or vice versa) without retraining or model conversion","Integrate NER into TensorFlow Lite for mobile/edge inference without framework-specific engineering"],"best_for":["Organizations with mixed PyTorch/TensorFlow stacks (e.g., research in PyTorch, production in TensorFlow)","Teams deploying to TensorFlow Serving, TF Lite, or TensorFlow.js without PyTorch dependencies","Cross-platform ML systems requiring framework flexibility"],"limitations":["Inference performance may differ slightly between frameworks due to operator implementations (typically <5% variance)","TensorFlow version requires TF 2.4+; older TensorFlow installations must upgrade","No automatic quantization or pruning; both frameworks load the full 560M parameter model","TensorFlow Lite conversion requires manual optimization (quantization, pruning) not provided by the model"],"requires":["PyTorch 1.9+ OR TensorFlow 2.4+ (not both required, but one is mandatory)","transformers 4.0+","safetensors library for loading"],"input_types":["text strings","pre-tokenized token IDs"],"output_types":["torch.Tensor (PyTorch) or tf.Tensor (TensorFlow) with shape (batch_size, seq_length, num_classes)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--xlm-roberta-large-ner-hrl__cap_4","uri":"capability://tool.use.integration.huggingface.inference.api.endpoint.deployment","name":"huggingface inference api endpoint deployment","description":"Model is compatible with HuggingFace's managed Inference API, which provides serverless token classification endpoints without requiring users to manage infrastructure. The API automatically handles model loading, batching, and GPU allocation, exposing a REST endpoint that accepts JSON payloads with text and returns entity predictions. This is enabled by the model's registration in HuggingFace's model hub with proper task metadata (token-classification) and safetensors weights.","intents":["Deploy NER without managing servers or GPUs via HuggingFace's managed API","Prototype multilingual NER systems with zero infrastructure setup","Integrate NER into applications via simple HTTP requests without ML infrastructure expertise"],"best_for":["Startups and solo developers prototyping NER without DevOps resources","Teams needing quick proof-of-concepts before committing to self-hosted infrastructure","Low-to-medium volume applications (< 1000 requests/day) where managed pricing is cost-effective"],"limitations":["Inference latency is 1-5 seconds per request due to network round-trip and shared GPU resources; not suitable for real-time applications","Pricing is per-request (typically $0.0001-0.001 per request); high-volume use cases are more expensive than self-hosted","No SLA or guaranteed uptime; subject to HuggingFace's service availability","Request payload size is limited (typically 1MB); very long documents must be chunked client-side","No custom batching or request prioritization; all requests share the same queue"],"requires":["HuggingFace API key (free tier available with rate limits)","HTTP client library (curl, requests, fetch, etc.)","internet connectivity"],"input_types":["JSON payload with 'inputs' field containing text string"],"output_types":["JSON array of entity predictions with token offsets and class labels"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--xlm-roberta-large-ner-hrl__cap_5","uri":"capability://data.processing.analysis.entity.span.reconstruction.from.token.level.predictions","name":"entity span reconstruction from token-level predictions","description":"Outputs token-level BIO (Begin-Inside-Outside) or BIOES (Begin-Inside-Outside-End-Single) tags that must be post-processed to reconstruct entity spans with character offsets. The model predicts a class label for each token (e.g., B-PER, I-PER, O), and downstream code must merge consecutive I-tags into spans and map token positions back to character offsets in the original text. This is a standard NLP pattern but requires careful handling of subword tokenization (BPE), where a single word may be split into multiple tokens.","intents":["Extract entity spans (start/end character positions) from token-level predictions for downstream processing","Handle subword tokenization correctly when reconstructing entities from BPE tokens","Build entity linking or knowledge graph systems that require precise character offsets"],"best_for":["NLP pipelines that need entity spans with character offsets for document annotation or linking","Teams building information extraction systems that feed into entity databases","Researchers studying entity recognition evaluation metrics (precision, recall, F1)"],"limitations":["Subword tokenization (BPE) complicates span reconstruction; a single word like 'unbelievable' may be split into ['un', 'be', 'lievable'], requiring careful offset calculation","No built-in span merging logic; users must implement BIO tag decoding and offset mapping themselves","Entity boundaries may not align with token boundaries; the model predicts per-token, not per-span, leading to potential boundary errors","No confidence scores per span; only hard predictions per token, making it difficult to rank or filter low-confidence entities"],"requires":["understanding of BIO/BIOES tagging schemes","tokenizer from transformers library to map token positions to character offsets","post-processing code to merge token-level predictions into spans"],"input_types":["token-level class predictions (BIO tags)"],"output_types":["entity spans with (start_char, end_char, entity_type) tuples","annotated text with entity boundaries marked"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","2GB+ GPU VRAM for batch inference, or CPU with 8GB+ RAM for single-sample inference","understanding of BPE tokenization and subword handling","validation set in target language to measure transfer quality","transformers 4.26+","GPU with 2GB+ VRAM for batch inference, or CPU with 8GB+ RAM","PyTorch 1.9+ OR TensorFlow 2.4+ (not both required, but one is mandatory)","transformers 4.0+"],"failure_modes":["Token-level predictions require post-processing to reconstruct entity spans from BIO/BIOES tags; no built-in span merging","Performance degrades on code-mixed text (e.g., Hinglish) due to training data composition","Model size (560M parameters) requires 2GB+ GPU memory; CPU inference is ~10-50x slower depending on sequence length","No confidence scores or uncertainty quantification per token; only hard class predictions","Training data is primarily news/Wikipedia; performance on domain-specific text (medical, legal) is not characterized","Transfer quality varies significantly by language pair; typologically distant languages (e.g., English→Chinese) show 5-15% F1 degradation vs in-language performance","Requires careful handling of script differences (Latin, Cyrillic, Arabic, CJK); tokenization may break for some writing systems","No explicit mechanism to handle language-specific entity types (e.g., Japanese honorifics, Arabic diacritics); may require post-processing","Cross-lingual embeddings are frozen after pre-training; fine-tuning on one language can degrade performance on others","Batch size is limited by GPU VRAM; typical max batch size is 8-32 for sequences of 128-512 tokens on 8GB GPUs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.60684790290648,"quality":0.37,"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":460384,"model_likes":13}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=davlan--xlm-roberta-large-ner-hrl","compare_url":"https://unfragile.ai/compare?artifact=davlan--xlm-roberta-large-ner-hrl"}},"signature":"vX1lQHAZ3AK+p3KbUWrNkk2sB/Bu4HaoWJH2Hh2FB1QkvhJE2BxCWGlUTf9Iiw1loFYnenBKBo0AozzzRMoPDQ==","signedAt":"2026-06-20T09:38:36.381Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/davlan--xlm-roberta-large-ner-hrl","artifact":"https://unfragile.ai/davlan--xlm-roberta-large-ner-hrl","verify":"https://unfragile.ai/api/v1/verify?slug=davlan--xlm-roberta-large-ner-hrl","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"}}