{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-helsinki-nlp--opus-mt-en-de","slug":"helsinki-nlp--opus-mt-en-de","name":"opus-mt-en-de","type":"model","url":"https://huggingface.co/Helsinki-NLP/opus-mt-en-de","page_url":"https://unfragile.ai/helsinki-nlp--opus-mt-en-de","categories":["text-writing"],"tags":["transformers","pytorch","tf","jax","rust","marian","text2text-generation","translation","en","de","license:cc-by-4.0","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-de__cap_0","uri":"capability://text.generation.language.english.to.german.neural.machine.translation.with.marian.encoder.decoder.architecture","name":"english-to-german neural machine translation with marian encoder-decoder architecture","description":"Translates English text to German using the Marian NMT framework, a specialized encoder-decoder Transformer architecture optimized for translation tasks. The model employs byte-pair encoding (BPE) tokenization with shared vocabulary across language pairs, enabling efficient handling of rare words and morphological variations. Inference can be executed via HuggingFace Transformers library with support for multiple backends (PyTorch, TensorFlow, JAX, Rust), allowing deployment flexibility across CPU and GPU environments.","intents":["Translate English documents, web content, or user-generated text to German programmatically","Build multilingual applications that require English-German translation without training custom models","Integrate translation into production pipelines with framework flexibility (PyTorch, TF, or Rust backends)","Deploy translation inference at scale with optimized model weights and tokenizer configuration"],"best_for":["Teams building multilingual SaaS products targeting German-speaking markets","Developers integrating translation into existing HuggingFace-based NLP pipelines","Organizations needing open-source translation without licensing restrictions (CC-BY-4.0)","ML engineers deploying translation on resource-constrained hardware via Rust or quantized inference"],"limitations":["Domain-specific terminology may be mistranslated without fine-tuning on domain corpora; model trained on general web text","No built-in handling of code, mathematical notation, or specialized formatting — treats all input as natural language","Inference latency ~500-1500ms per sentence on CPU depending on length; GPU required for sub-100ms latency at scale","No confidence scores or alignment information returned — only final translation text","Bidirectional translation requires separate model (opus-mt-de-en); this model is unidirectional English→German only","Context window limited to sentence-level; no cross-sentence coherence optimization for multi-paragraph documents"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) OR TensorFlow (>=2.4.0) OR JAX (>=0.2.0) depending on backend choice","~630MB disk space for model weights and tokenizer files","For production: GPU with >=2GB VRAM recommended for batch inference, or CPU with 4GB+ RAM for single-instance translation"],"input_types":["plain text (UTF-8 encoded strings)","text sequences up to model's max token length (~512 tokens typical for Marian)","batch inputs via HuggingFace pipeline or manual batching"],"output_types":["translated text (UTF-8 string)","batch outputs as list of strings","optional: token-level logits and attention weights via model.generate() with output_scores=True"],"categories":["text-generation-language","neural-machine-translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-de__cap_1","uri":"capability://text.generation.language.batch.translation.with.dynamic.padding.and.sequence.bucketing","name":"batch translation with dynamic padding and sequence bucketing","description":"Processes multiple English sentences or documents simultaneously using HuggingFace pipeline's batching mechanism with dynamic padding and sequence bucketing to minimize computational waste. The model groups sequences of similar length into buckets, pads them to the longest sequence in each bucket, and processes them in parallel on GPU/CPU. This approach reduces the overhead of padding short sequences to the global max length, improving throughput by 2-5x compared to processing sequences individually.","intents":["Translate large document collections (100s-1000s of sentences) efficiently in a single batch job","Maximize GPU utilization when translating variable-length inputs without manual bucketing logic","Reduce per-token inference cost in production by amortizing model loading overhead across multiple inputs"],"best_for":["Data engineers processing bulk translation jobs (e.g., localizing documentation, translating datasets)","Backend services handling concurrent translation requests with batch aggregation","Researchers evaluating translation quality across large corpora"],"limitations":["Batch size must fit in GPU/CPU memory; typical max batch size 32-128 depending on sequence length and hardware","Dynamic padding adds ~50-100ms overhead per batch for bucketing logic; not beneficial for single-sequence inference","No built-in streaming or incremental output — entire batch must complete before results are available","Bucketing heuristics may not be optimal for highly variable-length inputs (e.g., mix of 10-token and 500-token sequences)"],"requires":["HuggingFace transformers library with pipeline API","Sufficient GPU memory for batch_size × max_sequence_length × model_hidden_dim (typically 2-8GB for batch_size=32)","Input as list of strings or pre-tokenized sequences"],"input_types":["list of English text strings (variable length)","pre-tokenized input_ids tensors with attention masks"],"output_types":["list of translated German strings (same order as input)","optional: batch-level metrics (total tokens processed, throughput in tokens/sec)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-de__cap_2","uri":"capability://tool.use.integration.multi.backend.inference.execution.pytorch.tensorflow.jax.rust","name":"multi-backend inference execution (pytorch, tensorflow, jax, rust)","description":"Executes the same trained Marian model weights across four distinct inference backends (PyTorch, TensorFlow, JAX, Rust) by leveraging HuggingFace's unified model format and conversion tooling. Each backend has distinct performance characteristics: PyTorch offers maximum flexibility and debugging, TensorFlow enables TFLite mobile deployment, JAX provides JIT compilation and automatic differentiation, and Rust enables zero-copy inference with minimal memory overhead. The model weights are stored in a backend-agnostic format and converted on-the-fly or pre-converted for each target environment.","intents":["Deploy the same translation model across heterogeneous infrastructure (cloud GPU, edge devices, mobile, serverless)","Choose inference backend based on deployment constraints (latency, memory, power consumption) without retraining","Integrate translation into existing ML stacks using preferred framework (PyTorch for research, TF for production, Rust for systems)"],"best_for":["ML teams managing multi-platform deployments (web, mobile, edge, cloud)","Organizations with existing TensorFlow or JAX infrastructure seeking to add translation","Systems engineers building low-latency inference servers in Rust or C++"],"limitations":["Backend-specific optimizations (quantization, pruning) must be applied per-backend; no universal optimization path","JAX backend requires functional programming style; less intuitive for imperative PyTorch users","Rust backend has limited ecosystem for preprocessing/postprocessing; typically requires Python wrapper for tokenization","TensorFlow Lite conversion may lose some model features; not all Marian operations are supported in TFLite","Performance varies significantly by backend: PyTorch ~100ms, TF ~120ms, JAX ~90ms, Rust ~80ms per sentence (GPU, typical)"],"requires":["PyTorch backend: torch>=1.9.0","TensorFlow backend: tensorflow>=2.4.0","JAX backend: jax>=0.2.0, jaxlib","Rust backend: rust toolchain, tokenizers crate for BPE decoding","HuggingFace transformers library (>=4.0.0) for model loading and conversion"],"input_types":["text strings (all backends)","pre-tokenized tensors (PyTorch: torch.Tensor, TF: tf.Tensor, JAX: jnp.ndarray, Rust: Vec<u32>)"],"output_types":["translated text strings (all backends)","backend-specific tensor outputs (PyTorch: torch.Tensor, TF: tf.Tensor, JAX: jnp.ndarray, Rust: Vec<u32>)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-de__cap_3","uri":"capability://data.processing.analysis.tokenization.with.byte.pair.encoding.bpe.and.shared.vocabulary","name":"tokenization with byte-pair encoding (bpe) and shared vocabulary","description":"Tokenizes English input and German output using byte-pair encoding (BPE) with a shared vocabulary learned across both languages during model training. The tokenizer merges frequent character sequences into subword units, enabling the model to handle rare words and morphological variations without an unbounded vocabulary. Shared vocabulary (typically 32K-64K tokens) reduces model parameters compared to separate vocabularies and improves translation of cognates and shared terminology between English and German.","intents":["Automatically handle out-of-vocabulary words and rare morphological forms without explicit fallback logic","Tokenize input text consistently with the model's training procedure, ensuring correct inference","Reduce model size and memory footprint via shared vocabulary instead of separate source/target vocabularies"],"best_for":["Developers integrating translation without understanding tokenization internals (HuggingFace handles it automatically)","Systems with memory constraints where shared vocabulary reduces model size by 20-30%","Applications handling technical text with shared English-German terminology (e.g., software documentation)"],"limitations":["BPE tokenization is lossy; rare characters or unusual Unicode sequences may be split into many subword tokens, increasing latency","Shared vocabulary may not be optimal for language pairs with very different morphology; separate vocabularies could improve quality","Tokenizer is fixed at model release; cannot be updated or fine-tuned without retraining the entire model","Special tokens (e.g., <unk>, <pad>) are hardcoded; custom special tokens require tokenizer retraining","BPE decoding can produce artifacts (e.g., extra spaces) if subword boundaries don't align with word boundaries"],"requires":["HuggingFace tokenizers library (>=0.10.0) for fast BPE decoding","Pre-trained vocabulary file (vocab.json) and merge file (merges.txt) from HuggingFace model hub","Input text must be valid UTF-8; non-UTF-8 input will be decoded with error handling (replace or ignore)"],"input_types":["raw English text strings (UTF-8)","pre-tokenized token IDs (for advanced use cases)"],"output_types":["token IDs (list of integers)","attention masks (binary mask indicating padding)","token strings (for debugging/inspection)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-de__cap_4","uri":"capability://text.generation.language.beam.search.decoding.with.configurable.beam.width.and.length.penalties","name":"beam search decoding with configurable beam width and length penalties","description":"Generates translations using beam search, a greedy-with-lookahead decoding algorithm that maintains multiple hypotheses (beams) during generation and selects the highest-probability translation. The implementation supports configurable beam width (typically 4-8), length penalty to prevent bias toward short translations, and early stopping when all beams have generated end-of-sequence tokens. Beam search trades off inference latency (linear with beam width) for translation quality, typically improving BLEU scores by 1-3 points compared to greedy decoding.","intents":["Generate higher-quality translations by exploring multiple decoding paths instead of greedily selecting top-1 token at each step","Balance translation quality and latency by tuning beam width (higher = better quality, slower)","Prevent degenerate translations (e.g., very short or repetitive) via length penalties"],"best_for":["Production systems where translation quality is critical and 2-5x latency increase is acceptable","Batch processing where latency is amortized across many sequences","Research and evaluation where maximum translation quality is prioritized over speed"],"limitations":["Beam search latency scales linearly with beam width; beam_width=8 is ~8x slower than greedy decoding (beam_width=1)","Memory usage scales with beam width; each beam maintains a copy of decoder state, requiring 2-4x more GPU memory than greedy decoding","Beam search is not guaranteed to find the globally optimal translation; it's a heuristic that can miss better translations outside the beam","Length penalty hyperparameter must be tuned per use case; default values may produce suboptimal results for domain-specific text","No diversity penalty; all beams may converge to similar hypotheses, reducing exploration"],"requires":["HuggingFace transformers library with generate() method supporting beam_search decoding","GPU with sufficient memory for beam_width × sequence_length × hidden_dim (typically 4-8GB for beam_width=8)","Hyperparameter tuning: beam_width (default 4), length_penalty (default 1.0), early_stopping (default True)"],"input_types":["input_ids tensor (tokenized English text)","attention_mask tensor (binary mask for padding)"],"output_types":["output_ids tensor (token IDs of translated sequence)","optional: beam_scores (log-probability of each beam's final hypothesis)","optional: sequences (all beam hypotheses, not just top-1)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-helsinki-nlp--opus-mt-en-de__cap_5","uri":"capability://automation.workflow.deployment.via.huggingface.inference.endpoints.and.cloud.platforms.azure.aws.gcp","name":"deployment via huggingface inference endpoints and cloud platforms (azure, aws, gcp)","description":"Model is compatible with HuggingFace Inference Endpoints, a managed inference service that handles model loading, scaling, and API serving without manual DevOps. Additionally, the model can be deployed on Azure ML, AWS SageMaker, and Google Cloud Vertex AI via their respective model registries and inference frameworks. Deployment abstracts away infrastructure management: users specify desired throughput/latency SLAs, and the platform auto-scales compute resources (GPUs, TPUs) and handles load balancing.","intents":["Deploy translation without managing Docker, Kubernetes, or GPU infrastructure","Scale translation API from 1 to 1000+ requests/second automatically based on demand","Integrate translation into existing cloud ML workflows (Azure ML pipelines, SageMaker training jobs)"],"best_for":["Startups and small teams without DevOps expertise seeking managed inference","Enterprises with existing cloud commitments (Azure, AWS, GCP) wanting to minimize vendor lock-in","Applications with variable traffic patterns where auto-scaling reduces idle compute costs"],"limitations":["HuggingFace Inference Endpoints adds ~50-200ms latency per request due to network round-trip and load balancer overhead","Cloud platform deployments incur per-request or per-hour charges; self-hosted inference may be cheaper for high-volume use cases (>1M requests/month)","Cold start latency (first request after idle period) can be 5-30 seconds as models are loaded into GPU memory","Vendor lock-in: migrating from Azure to AWS requires re-deploying and re-testing; no portable deployment format","Rate limiting and quota management vary by platform; some platforms have strict per-minute request limits"],"requires":["HuggingFace account and API token for Inference Endpoints","Azure subscription (for Azure ML), AWS account (for SageMaker), or GCP project (for Vertex AI)","Model must be public or user must have access permissions on HuggingFace model hub"],"input_types":["HTTP POST request with JSON payload: {\"inputs\": \"English text to translate\"}","optional: batch inputs as list of strings"],"output_types":["HTTP JSON response: {\"translation_text\": \"Translated German text\"}","optional: batch outputs as list of translation objects"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) OR TensorFlow (>=2.4.0) OR JAX (>=0.2.0) depending on backend choice","~630MB disk space for model weights and tokenizer files","For production: GPU with >=2GB VRAM recommended for batch inference, or CPU with 4GB+ RAM for single-instance translation","HuggingFace transformers library with pipeline API","Sufficient GPU memory for batch_size × max_sequence_length × model_hidden_dim (typically 2-8GB for batch_size=32)","Input as list of strings or pre-tokenized sequences","PyTorch backend: torch>=1.9.0","TensorFlow backend: tensorflow>=2.4.0"],"failure_modes":["Domain-specific terminology may be mistranslated without fine-tuning on domain corpora; model trained on general web text","No built-in handling of code, mathematical notation, or specialized formatting — treats all input as natural language","Inference latency ~500-1500ms per sentence on CPU depending on length; GPU required for sub-100ms latency at scale","No confidence scores or alignment information returned — only final translation text","Bidirectional translation requires separate model (opus-mt-de-en); this model is unidirectional English→German only","Context window limited to sentence-level; no cross-sentence coherence optimization for multi-paragraph documents","Batch size must fit in GPU/CPU memory; typical max batch size 32-128 depending on sequence length and hardware","Dynamic padding adds ~50-100ms overhead per batch for bucketing logic; not beneficial for single-sequence inference","No built-in streaming or incremental output — entire batch must complete before results are available","Bucketing heuristics may not be optimal for highly variable-length inputs (e.g., mix of 10-token and 500-token sequences)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6719502250666167,"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":814426,"model_likes":42}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=helsinki-nlp--opus-mt-en-de","compare_url":"https://unfragile.ai/compare?artifact=helsinki-nlp--opus-mt-en-de"}},"signature":"G5WqTFHhdaXGm46q2HwnpRVTwxxyP/SEp0RHhNPRlF+Cs3vmpW+9Bwh9Zc7jFxEaGbjrzZrxwoTXXUM5zY7ADA==","signedAt":"2026-06-22T03:58:08.706Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/helsinki-nlp--opus-mt-en-de","artifact":"https://unfragile.ai/helsinki-nlp--opus-mt-en-de","verify":"https://unfragile.ai/api/v1/verify?slug=helsinki-nlp--opus-mt-en-de","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"}}