{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-helsinki-nlp--opus-mt-en-fr","slug":"helsinki-nlp--opus-mt-en-fr","name":"opus-mt-en-fr","type":"model","url":"https://huggingface.co/Helsinki-NLP/opus-mt-en-fr","page_url":"https://unfragile.ai/helsinki-nlp--opus-mt-en-fr","categories":["text-writing"],"tags":["transformers","pytorch","tf","jax","marian","text2text-generation","translation","en","fr","license:apache-2.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-helsinki-nlp--opus-mt-en-fr__cap_0","uri":"capability://text.generation.language.english.to.french.neural.machine.translation.with.marian.architecture","name":"english-to-french neural machine translation with marian architecture","description":"Performs bidirectional sequence-to-sequence translation from English to French using the Marian NMT framework, which implements a transformer-based encoder-decoder architecture with attention mechanisms. The model was trained on parallel corpora within the OPUS project and leverages byte-pair encoding (BPE) tokenization for subword segmentation, enabling handling of rare words and morphological variations. Translation inference runs via HuggingFace Transformers library with support for PyTorch, TensorFlow, and JAX backends, allowing deployment across multiple hardware targets (CPU, GPU, TPU).","intents":["Translate English text documents or user-generated content into French for localization or multilingual applications","Build a production translation service that can handle variable-length sequences with consistent quality across domains","Integrate translation into a pipeline without managing model weights or training infrastructure","Deploy translation inference on edge devices or cloud platforms with framework flexibility (PyTorch vs TensorFlow)"],"best_for":["Teams building multilingual SaaS products requiring English-French translation","Developers prototyping localization pipelines without budget for commercial APIs","Organizations needing on-premise or self-hosted translation to avoid data transmission to external services","ML engineers evaluating open-source NMT quality against proprietary alternatives"],"limitations":["No domain-specific fine-tuning included — general-purpose training may underperform on technical, legal, or medical terminology","Single language pair (EN→FR only) — requires separate models for other language combinations, increasing deployment complexity","Inference latency scales with input length; batch processing recommended for throughput optimization but adds complexity","No built-in confidence scoring or alignment visualization — difficult to identify translation uncertainty or debug errors","Training data cutoff and domain bias unknown — may produce outdated or culturally inappropriate translations for contemporary slang or proper nouns"],"requires":["Python 3.7+","HuggingFace Transformers library (>=4.0.0)","PyTorch (>=1.9.0) OR TensorFlow (>=2.6.0) OR JAX (>=0.3.0) depending on backend choice","Minimum 2GB RAM for CPU inference; GPU recommended for batch processing (VRAM >=4GB)","Internet connection for initial model download (~300MB) unless cached locally"],"input_types":["plain text (UTF-8 encoded strings)","text sequences up to ~512 tokens (model context window)","batch inputs (multiple sentences/documents processed together)"],"output_types":["translated text (UTF-8 encoded strings)","token-level logits and attention weights (if requested via model output)","batch outputs with per-sequence translations"],"categories":["text-generation-language","neural-machine-translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-fr__cap_1","uri":"capability://text.generation.language.batch.translation.with.automatic.tokenization.and.padding","name":"batch translation with automatic tokenization and padding","description":"Processes multiple English sentences or documents in a single forward pass by automatically tokenizing input text using the model's BPE vocabulary, padding sequences to uniform length within a batch, and decoding output tokens back to French text. The HuggingFace pipeline abstraction handles tokenizer initialization, tensor conversion, and post-processing, reducing boilerplate code. Batch processing amortizes model loading overhead and enables GPU parallelization, improving throughput by 5-10x compared to sequential inference.","intents":["Translate large document collections (100s-1000s of sentences) efficiently without manual tokenization","Maximize GPU utilization by processing multiple sequences in parallel within memory constraints","Reduce per-request latency in production by batching concurrent translation requests","Simplify integration by delegating tokenization/detokenization to the framework rather than custom code"],"best_for":["Backend services handling bulk translation requests (e.g., content management systems, data pipelines)","Batch processing jobs with flexible latency requirements (not real-time)","Teams without deep NLP expertise who need reliable tokenization without manual handling"],"limitations":["Padding overhead increases memory usage for variable-length batches — worst case is batch size × max sequence length tokens","No dynamic batching — batch size must be fixed at inference time, requiring manual tuning for optimal throughput","Tokenization is deterministic but language-specific — special characters, URLs, or code snippets may tokenize unexpectedly","No streaming output — entire batch must complete before returning results, unsuitable for real-time user-facing applications"],"requires":["HuggingFace Transformers pipeline API (>=4.0.0)","Sufficient GPU memory for batch size × max_length tokens (typically 4-8GB for batch_size=32, max_length=256)","Pre-tokenized or raw text input"],"input_types":["list of English text strings","batch size parameter (integer)","max_length parameter for padding (integer, default 512)"],"output_types":["list of French translated strings","optional: token-level scores or attention weights"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-fr__cap_2","uri":"capability://tool.use.integration.multi.framework.model.inference.pytorch.tensorflow.jax","name":"multi-framework model inference (pytorch, tensorflow, jax)","description":"The model weights are compatible with PyTorch, TensorFlow, and JAX backends, allowing developers to choose the inference framework that best fits their deployment environment. HuggingFace Transformers automatically converts between formats on first load, caching the converted weights locally. This enables deployment on diverse hardware (NVIDIA GPUs via CUDA, TPUs via TensorFlow, CPU-only systems) and integration into existing ML stacks without retraining or format conversion.","intents":["Deploy the same model across heterogeneous infrastructure (some services use PyTorch, others use TensorFlow)","Migrate from one framework to another without retraining or finding alternative models","Optimize for specific hardware (e.g., use JAX for TPU deployment, PyTorch for NVIDIA GPUs)","Integrate translation into existing ML pipelines built on different frameworks"],"best_for":["Organizations with mixed ML stacks (some teams use PyTorch, others TensorFlow)","Cloud platforms offering multiple framework support (AWS SageMaker, Google Cloud AI, Azure ML)","Teams evaluating framework migration without disrupting production services"],"limitations":["Framework conversion adds ~30-60 seconds on first load as weights are downloaded and converted","Converted weights are cached locally but consume additional disk space (~600MB for PyTorch + TensorFlow versions)","Performance characteristics vary by framework — JAX may be slower on CPU, TensorFlow may have higher memory overhead on GPU","Debugging framework-specific issues requires expertise in multiple ecosystems"],"requires":["One of: PyTorch (>=1.9.0), TensorFlow (>=2.6.0), or JAX (>=0.3.0)","HuggingFace Transformers (>=4.0.0)","Sufficient disk space for cached converted weights (~600MB total)"],"input_types":["framework-agnostic text input (strings)"],"output_types":["framework-specific tensors (torch.Tensor, tf.Tensor, jax.Array) or converted to NumPy arrays"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-fr__cap_3","uri":"capability://automation.workflow.deployment.to.cloud.endpoints.azure.aws.huggingface.inference.api","name":"deployment to cloud endpoints (azure, aws, huggingface inference api)","description":"The model is compatible with HuggingFace Inference API, Azure ML endpoints, and AWS SageMaker, enabling serverless or managed deployment without infrastructure management. Developers can deploy via a single API call or web UI, with automatic scaling, monitoring, and API key management handled by the platform. The model is pre-optimized for inference (quantization-ready, small footprint) and supports both synchronous REST API calls and asynchronous batch processing.","intents":["Deploy translation without managing containers, GPUs, or infrastructure","Expose translation as a REST API with automatic scaling and monitoring","Integrate translation into web applications or mobile backends via HTTP requests","Avoid vendor lock-in by choosing between multiple deployment platforms"],"best_for":["Startups and small teams without DevOps expertise","Web applications requiring translation as a microservice","Teams wanting to avoid infrastructure management and focus on application logic"],"limitations":["API latency includes network round-trip time (~50-200ms) plus inference time, unsuitable for real-time applications","Per-request pricing on HuggingFace Inference API can exceed self-hosted costs at scale (>10k requests/day)","Cold start latency on serverless platforms (AWS Lambda, Azure Functions) may exceed 5 seconds if model not cached","Data privacy concerns — text is transmitted to third-party servers unless using private endpoints (additional cost)","Rate limiting and quota restrictions on free tiers"],"requires":["API key for chosen platform (HuggingFace, Azure, AWS)","HTTP client library (requests, curl, etc.)","Network connectivity to cloud endpoints"],"input_types":["JSON payload with text field (e.g., {\"inputs\": \"Hello world\"})"],"output_types":["JSON response with translated text (e.g., {\"generated_text\": \"Bonjour le monde\"})"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-fr__cap_4","uri":"capability://code.generation.editing.fine.tuning.on.domain.specific.parallel.corpora","name":"fine-tuning on domain-specific parallel corpora","description":"The pre-trained Marian model can be fine-tuned on custom English-French parallel data using HuggingFace Transformers' Seq2SeqTrainer, which handles distributed training, gradient accumulation, and mixed-precision optimization. Fine-tuning adapts the model to domain-specific terminology (medical, legal, technical) or writing styles without training from scratch. Requires paired source-target sentences in a structured format (CSV, JSON, or HuggingFace Dataset) and typically 1000-10000 examples for meaningful improvement.","intents":["Improve translation quality for domain-specific terminology (e.g., medical documents, legal contracts, technical manuals)","Adapt the model to company-specific style guides or terminology databases","Reduce hallucinations and improve consistency for specialized content","Build a proprietary translation model without training from scratch"],"best_for":["Organizations with large volumes of domain-specific content and parallel translations","Teams with in-house translation expertise to curate and validate training data","Companies needing proprietary models for competitive advantage or data privacy"],"limitations":["Requires high-quality parallel data — poor translations in training data degrade model performance","Fine-tuning on small datasets (<1000 examples) risks overfitting and degrading general-purpose translation","Training time scales with dataset size (1-10 hours on single GPU for 10k examples)","No automatic data cleaning or validation — requires manual effort to ensure data quality","Fine-tuned models are not automatically compatible with cloud endpoints — requires custom deployment"],"requires":["Python 3.7+","HuggingFace Transformers (>=4.0.0) and Datasets library","PyTorch (>=1.9.0) or TensorFlow (>=2.6.0)","GPU with >=8GB VRAM for efficient training","Parallel corpus in structured format (CSV, JSON, or HuggingFace Dataset)","Validation set (10-20% of training data) to monitor overfitting"],"input_types":["parallel corpus: list of (English, French) sentence pairs","training hyperparameters: learning rate, batch size, num_epochs, etc."],"output_types":["fine-tuned model weights (saved locally or to HuggingFace Hub)","training metrics: loss, BLEU score, validation accuracy"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-fr__cap_5","uri":"capability://automation.workflow.quantization.and.model.compression.for.edge.deployment","name":"quantization and model compression for edge deployment","description":"The model can be quantized to INT8 or INT4 precision using libraries like GPTQ, bitsandbytes, or ONNX Runtime, reducing model size from ~300MB to ~75-150MB and inference latency by 30-50% with minimal quality loss. Quantized models run efficiently on edge devices (mobile phones, embedded systems, Raspberry Pi) and reduce memory footprint for on-device deployment. HuggingFace Transformers provides built-in quantization support via load_in_8bit and load_in_4bit parameters.","intents":["Deploy translation on mobile devices or edge hardware with limited memory and compute","Reduce inference latency for real-time translation in resource-constrained environments","Lower bandwidth requirements for model distribution and updates","Enable on-device translation without cloud connectivity for privacy-sensitive applications"],"best_for":["Mobile app developers building offline translation features","IoT and embedded systems requiring local inference","Privacy-focused applications where data cannot leave the device","Cost-sensitive deployments where GPU memory is expensive"],"limitations":["Quantization introduces ~1-3% BLEU score degradation depending on quantization level (INT8 vs INT4)","INT4 quantization requires specialized hardware support (NVIDIA A100, newer GPUs) — not all devices support efficient INT4 inference","Quantized models are not compatible with all frameworks — INT8 works with PyTorch/TensorFlow, but INT4 requires specific backends","Debugging quantization artifacts (hallucinations, mistranslations) is difficult without access to full-precision weights","No automatic rollback if quantization degrades quality — requires manual validation on target hardware"],"requires":["bitsandbytes (>=0.37.0) for INT8 quantization, or GPTQ/AWQ for INT4","NVIDIA GPU with compute capability >=7.0 for efficient INT8 inference (optional for CPU inference)","HuggingFace Transformers (>=4.30.0) with quantization support","Target device with sufficient memory (75-150MB for quantized model)"],"input_types":["English text (same as full-precision model)"],"output_types":["French translated text (same as full-precision model, with minor quality degradation)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","HuggingFace Transformers library (>=4.0.0)","PyTorch (>=1.9.0) OR TensorFlow (>=2.6.0) OR JAX (>=0.3.0) depending on backend choice","Minimum 2GB RAM for CPU inference; GPU recommended for batch processing (VRAM >=4GB)","Internet connection for initial model download (~300MB) unless cached locally","HuggingFace Transformers pipeline API (>=4.0.0)","Sufficient GPU memory for batch size × max_length tokens (typically 4-8GB for batch_size=32, max_length=256)","Pre-tokenized or raw text input","One of: PyTorch (>=1.9.0), TensorFlow (>=2.6.0), or JAX (>=0.3.0)","HuggingFace Transformers (>=4.0.0)"],"failure_modes":["No domain-specific fine-tuning included — general-purpose training may underperform on technical, legal, or medical terminology","Single language pair (EN→FR only) — requires separate models for other language combinations, increasing deployment complexity","Inference latency scales with input length; batch processing recommended for throughput optimization but adds complexity","No built-in confidence scoring or alignment visualization — difficult to identify translation uncertainty or debug errors","Training data cutoff and domain bias unknown — may produce outdated or culturally inappropriate translations for contemporary slang or proper nouns","Padding overhead increases memory usage for variable-length batches — worst case is batch size × max sequence length tokens","No dynamic batching — batch size must be fixed at inference time, requiring manual tuning for optimal throughput","Tokenization is deterministic but language-specific — special characters, URLs, or code snippets may tokenize unexpectedly","No streaming output — entire batch must complete before returning results, unsuitable for real-time user-facing applications","Framework conversion adds ~30-60 seconds on first load as weights are downloaded and converted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6417165136836955,"quality":0.22,"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":"2026-05-03T14:22:53.713Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":459855,"model_likes":65}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=helsinki-nlp--opus-mt-en-fr","compare_url":"https://unfragile.ai/compare?artifact=helsinki-nlp--opus-mt-en-fr"}},"signature":"wGN83npY3hpWXM3TkCM2gJybIDwPu2qgX7S6hT39g4ID31Mzyjhl9E5H5Pzc+CVFb71LWGeQ0BDpdU5HAbyMCw==","signedAt":"2026-06-21T09:05:11.838Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/helsinki-nlp--opus-mt-en-fr","artifact":"https://unfragile.ai/helsinki-nlp--opus-mt-en-fr","verify":"https://unfragile.ai/api/v1/verify?slug=helsinki-nlp--opus-mt-en-fr","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"}}