{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebook--esm2_t33_650m_ur50d","slug":"facebook--esm2_t33_650m_ur50d","name":"esm2_t33_650M_UR50D","type":"model","url":"https://huggingface.co/facebook/esm2_t33_650M_UR50D","page_url":"https://unfragile.ai/facebook--esm2_t33_650m_ur50d","categories":["model-training"],"tags":["transformers","pytorch","tf","safetensors","esm","fill-mask","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-facebook--esm2_t33_650m_ur50d__cap_0","uri":"capability://data.processing.analysis.protein.sequence.masked.token.prediction","name":"protein-sequence-masked-token-prediction","description":"Predicts masked amino acid tokens in protein sequences using a 33-layer transformer encoder trained on 250M unlabeled protein sequences from UniRef50. The model uses bidirectional attention to infer missing residues by learning contextual patterns from evolutionary and structural relationships encoded in the training corpus. Outputs probability distributions over the 20 standard amino acids plus special tokens for each masked position.","intents":["I need to predict missing or uncertain amino acids in a protein sequence for structural validation","I want to identify likely amino acid substitutions at specific positions for protein engineering","I need to generate embeddings for downstream protein property prediction tasks","I want to validate protein sequence quality by checking if masked positions can be accurately reconstructed"],"best_for":["computational biologists performing protein sequence analysis and validation","protein engineering teams designing variants with predicted functional properties","researchers building protein language models and fine-tuning on domain-specific tasks","bioinformaticians integrating protein understanding into ML pipelines"],"limitations":["Trained exclusively on natural protein sequences — may not generalize well to highly engineered or synthetic proteins with non-standard amino acids","Requires input sequences in standard FASTA format with single-letter amino acid codes; cannot process post-translational modifications or non-canonical residues","Context window limited to sequence length — long proteins (>1024 residues) may lose long-range structural context in predictions","No built-in uncertainty quantification — outputs probabilities but not confidence intervals or epistemic uncertainty estimates","Inference latency scales quadratically with sequence length due to transformer self-attention complexity"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+ for model loading","transformers library 4.20+ for tokenization and inference","Python 3.7+","GPU with 2GB+ VRAM for batch inference (CPU inference supported but slow)","Protein sequences in standard single-letter amino acid notation (A-Z, with X for unknown)"],"input_types":["protein-sequence-string","fasta-format-text","tokenized-amino-acid-indices"],"output_types":["probability-distribution-per-position","top-k-predictions-with-scores","hidden-state-embeddings","attention-weights"],"categories":["data-processing-analysis","protein-language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--esm2_t33_650m_ur50d__cap_1","uri":"capability://data.processing.analysis.protein.sequence.embedding.generation","name":"protein-sequence-embedding-generation","description":"Extracts dense vector representations (embeddings) from protein sequences by passing them through the 33-layer transformer encoder and extracting hidden states at specified layers. These embeddings capture semantic and functional properties of proteins and can be used as input features for downstream ML tasks like classification, clustering, or similarity search. Supports per-token embeddings (one vector per amino acid) or sequence-level pooling (single vector per protein).","intents":["I need fixed-size vector representations of proteins for machine learning models","I want to cluster proteins by functional similarity using their learned representations","I need to measure semantic similarity between protein sequences for homology detection","I want to use protein embeddings as features for predicting properties like solubility or binding affinity"],"best_for":["ML engineers building protein property prediction pipelines","researchers performing protein clustering and functional annotation","teams implementing protein similarity search and recommendation systems","bioinformaticians integrating protein understanding into multi-modal models"],"limitations":["Embeddings are task-agnostic and not optimized for specific downstream tasks — may require fine-tuning for best performance","No built-in pooling strategy — users must manually implement mean/max/CLS token pooling or use per-token embeddings","Embedding dimensionality fixed at 1280 (model hidden size) — cannot be reduced without additional projection layers","Sequence length variation requires padding or truncation — no adaptive handling of variable-length inputs","Embeddings are not normalized by default — cosine similarity requires manual L2 normalization"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+","transformers library 4.20+","Python 3.7+","GPU with 2GB+ VRAM for efficient batch processing","NumPy or PyTorch for embedding manipulation and similarity computation"],"input_types":["protein-sequence-string","fasta-format-text","tokenized-amino-acid-indices","batch-sequences-list"],"output_types":["dense-vector-1280-dim","per-token-embeddings-matrix","sequence-level-pooled-embedding","numpy-array","torch-tensor"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--esm2_t33_650m_ur50d__cap_2","uri":"capability://automation.workflow.batch.protein.sequence.inference","name":"batch-protein-sequence-inference","description":"Processes multiple protein sequences in parallel through the transformer encoder using batching and dynamic padding to maximize GPU utilization. Automatically handles variable-length sequences by padding to the longest sequence in the batch and masking padded positions during attention computation. Supports both CPU and GPU inference with automatic device selection and memory-efficient gradient checkpointing for large batches.","intents":["I need to process thousands of protein sequences efficiently for large-scale analysis","I want to parallelize inference across multiple sequences to reduce total runtime","I need to balance memory usage and speed when processing very long proteins","I want to integrate protein sequence analysis into a production pipeline with throughput requirements"],"best_for":["bioinformaticians processing large protein databases or metagenomics datasets","production systems requiring high-throughput protein sequence analysis","researchers performing genome-wide protein property prediction","teams building scalable protein engineering platforms"],"limitations":["Batch size is memory-constrained — typical GPUs (8GB VRAM) support ~32-64 sequences of 512 residues; larger batches require gradient checkpointing or sequence truncation","Dynamic padding adds overhead for highly variable-length batches — performance degrades if batch contains both short (50 residues) and long (1000+ residues) sequences","No built-in distributed inference — scaling to multiple GPUs requires manual data parallelism or external frameworks like Hugging Face Accelerate","Inference latency has quadratic complexity with sequence length — doubling sequence length increases compute ~4x","No caching of intermediate representations — repeated inference on identical sequences recomputes embeddings from scratch"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+","transformers library 4.20+","Python 3.7+","GPU with 4GB+ VRAM for batch inference (8GB+ recommended for batches >16 sequences)","Optional: Hugging Face Accelerate for distributed inference"],"input_types":["batch-sequences-list","fasta-file","csv-with-sequence-column","dataloader-iterator"],"output_types":["batch-embeddings-tensor","batch-predictions-dict","numpy-array-batch","pandas-dataframe-with-results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--esm2_t33_650m_ur50d__cap_3","uri":"capability://data.processing.analysis.protein.sequence.tokenization.and.encoding","name":"protein-sequence-tokenization-and-encoding","description":"Converts raw protein sequences (strings of amino acid letters) into numerical token IDs compatible with the transformer model using a learned vocabulary of 33 tokens (20 standard amino acids + special tokens for padding, masking, unknown, and start/end markers). Handles edge cases like lowercase letters, non-standard amino acids (X, U, O), and sequence length constraints by truncating or padding to a configurable maximum length (default 1024 tokens).","intents":["I need to prepare protein sequences for input to the ESM2 model","I want to handle variable-length sequences with consistent preprocessing","I need to mask specific positions in a protein sequence for masked language modeling tasks","I want to convert model token outputs back to human-readable amino acid sequences"],"best_for":["developers integrating ESM2 into protein analysis pipelines","researchers fine-tuning ESM2 on domain-specific protein datasets","bioinformaticians building data preprocessing workflows","teams implementing protein sequence augmentation or data generation"],"limitations":["Fixed vocabulary of 33 tokens — cannot represent post-translational modifications, non-canonical amino acids, or chemical variants without preprocessing","Maximum sequence length of 1024 tokens — longer proteins must be truncated, losing C-terminal context","No built-in handling of sequence alignment or multiple sequence alignments (MSAs) — requires external tools like MSA-Transformer for MSA-aware embeddings","Tokenization is deterministic but not reversible for special tokens — cannot perfectly reconstruct original sequences from token IDs alone","No support for quality scores or confidence annotations from sequencing data"],"requires":["transformers library 4.20+ with ESM2 tokenizer","Python 3.7+","PyTorch or TensorFlow for tensor conversion"],"input_types":["protein-sequence-string","fasta-format-text","list-of-sequences","amino-acid-letters-a-z"],"output_types":["token-id-list","torch-tensor-token-ids","attention-mask-tensor","token-type-ids"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--esm2_t33_650m_ur50d__cap_4","uri":"capability://data.processing.analysis.masked.position.prediction.with.context","name":"masked-position-prediction-with-context","description":"Predicts amino acid identities at masked positions by computing logits over the 20 standard amino acids using the transformer's contextual understanding of surrounding residues. The model learns to infer missing positions by leveraging evolutionary patterns, structural constraints, and functional requirements encoded in the 250M-sequence training corpus. Outputs ranked predictions with confidence scores (softmax probabilities) for each masked position.","intents":["I want to predict what amino acid should be at a specific position in a protein sequence","I need to validate protein sequences by checking if masked positions can be accurately reconstructed","I want to identify likely amino acid substitutions for protein engineering experiments","I need to fill in gaps or uncertain regions in protein sequences from sequencing data"],"best_for":["protein engineers designing variants with predicted functional properties","bioinformaticians validating sequence quality and detecting sequencing errors","researchers studying protein evolution and conservation patterns","teams building protein design tools with AI-guided suggestions"],"limitations":["Predictions are context-dependent — the same position may have different predictions in different sequence contexts, limiting generalization to novel proteins","No uncertainty quantification beyond softmax probabilities — cannot distinguish between high-confidence predictions and ambiguous positions","Trained on natural proteins — may not generalize to highly engineered proteins with non-standard amino acids or unusual compositions","Predictions reflect training data biases — common amino acids are over-predicted, rare ones under-predicted","No structural constraints — predictions are based on sequence context alone, not 3D structure or folding energy"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+","transformers library 4.20+","Python 3.7+","GPU with 2GB+ VRAM (CPU inference supported but slow)","Protein sequence with [MASK] tokens at positions to predict"],"input_types":["protein-sequence-with-mask-tokens","fasta-with-masked-positions","tokenized-sequence-with-mask-ids"],"output_types":["logits-over-20-amino-acids","softmax-probabilities","top-k-predictions-with-scores","ranked-amino-acid-list"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--esm2_t33_650m_ur50d__cap_5","uri":"capability://planning.reasoning.transfer.learning.fine.tuning.on.custom.datasets","name":"transfer-learning-fine-tuning-on-custom-datasets","description":"Enables fine-tuning of the pre-trained ESM2 model on custom protein datasets for domain-specific tasks (e.g., predicting protein properties, classifying protein families, or optimizing sequences for specific functions). The model's 33-layer transformer encoder can be partially or fully fine-tuned using standard PyTorch/TensorFlow training loops, with support for gradient accumulation, mixed precision training, and learning rate scheduling to optimize convergence on limited labeled data.","intents":["I want to adapt ESM2 to predict properties specific to my protein domain (e.g., thermostability, binding affinity)","I need to fine-tune on a small labeled dataset without overfitting","I want to add task-specific classification heads on top of ESM2 embeddings","I need to optimize ESM2 for inference speed and model size on edge devices"],"best_for":["researchers building domain-specific protein property predictors","teams with labeled protein datasets for custom ML tasks","protein engineering groups optimizing for specific functional properties","companies deploying protein models in production with custom requirements"],"limitations":["Fine-tuning requires labeled data — no built-in semi-supervised or self-supervised fine-tuning strategies","Full fine-tuning of 650M parameters requires significant GPU memory (16GB+ VRAM) and computational resources","No built-in hyperparameter optimization — requires manual tuning of learning rate, batch size, and regularization","Transfer learning effectiveness depends on domain similarity — fine-tuning on very different protein types may not improve performance","No built-in model compression or quantization — fine-tuned models remain 650M parameters and require similar inference resources"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+","transformers library 4.20+","Python 3.7+","GPU with 16GB+ VRAM for full fine-tuning (8GB+ for parameter-efficient methods like LoRA)","Labeled protein dataset with 100+ examples for meaningful fine-tuning","Optional: Hugging Face Trainer API or custom training loop"],"input_types":["protein-sequences-with-labels","fasta-with-property-annotations","csv-dataset-format","huggingface-dataset-object"],"output_types":["fine-tuned-model-weights","task-specific-predictions","training-metrics-logs","saved-checkpoint-files"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.6+ for model loading","transformers library 4.20+ for tokenization and inference","Python 3.7+","GPU with 2GB+ VRAM for batch inference (CPU inference supported but slow)","Protein sequences in standard single-letter amino acid notation (A-Z, with X for unknown)","PyTorch 1.9+ or TensorFlow 2.6+","transformers library 4.20+","GPU with 2GB+ VRAM for efficient batch processing","NumPy or PyTorch for embedding manipulation and similarity computation","GPU with 4GB+ VRAM for batch inference (8GB+ recommended for batches >16 sequences)"],"failure_modes":["Trained exclusively on natural protein sequences — may not generalize well to highly engineered or synthetic proteins with non-standard amino acids","Requires input sequences in standard FASTA format with single-letter amino acid codes; 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