{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-j-hartmann--emotion-english-distilroberta-base","slug":"j-hartmann--emotion-english-distilroberta-base","name":"emotion-english-distilroberta-base","type":"model","url":"https://huggingface.co/j-hartmann/emotion-english-distilroberta-base","page_url":"https://unfragile.ai/j-hartmann--emotion-english-distilroberta-base","categories":["data-analysis"],"tags":["transformers","pytorch","tf","roberta","text-classification","distilroberta","sentiment","emotion","twitter","reddit","en","arxiv:2210.00434","text-embeddings-inference","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-j-hartmann--emotion-english-distilroberta-base__cap_0","uri":"capability://data.processing.analysis.multi.class.emotion.classification.from.english.text","name":"multi-class emotion classification from english text","description":"Classifies input text into discrete emotion categories (joy, sadness, anger, fear, surprise, disgust, neutral) using a DistilRoBERTa transformer backbone fine-tuned on social media corpora. The model applies token-level attention mechanisms over the full input sequence and outputs probability distributions across 7 emotion classes, enabling probabilistic emotion detection rather than binary sentiment classification. Architecture uses knowledge distillation from RoBERTa-base to reduce parameters by ~40% while maintaining classification accuracy.","intents":["Detect dominant emotion in user-generated content (tweets, Reddit posts, reviews) for sentiment analysis pipelines","Score emotional intensity across multiple dimensions to understand nuanced user sentiment beyond positive/negative","Build emotion-aware chatbots or content recommendation systems that respond contextually to user emotional state","Analyze customer feedback or support tickets to prioritize responses by emotional urgency (anger/fear vs. neutral)"],"best_for":["NLP engineers building emotion-aware recommendation or moderation systems","Social media analytics teams analyzing user sentiment at scale","Conversational AI developers adding emotional intelligence to chatbots","Content moderation teams flagging high-emotion content (anger, fear)"],"limitations":["Trained exclusively on English social media text — performance degrades significantly on formal writing, technical documentation, or non-English languages","7-class taxonomy may not capture fine-grained emotional nuance (e.g., distinguishes anger from frustration only through confidence scores, not separate classes)","Inference latency ~50-150ms per sample on CPU, ~10-20ms on GPU — not suitable for real-time streaming with <5ms SLA","Context window limited to 512 tokens (standard BERT/RoBERTa constraint) — long-form documents require chunking or summarization","No confidence calibration — raw logits may not reflect true probability of misclassification, especially on out-of-distribution text"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+ (model supports both via HuggingFace transformers library)","transformers library 4.0+","~350MB disk space for model weights (DistilRoBERTa-base is ~270MB + config/tokenizer)","4GB+ RAM for batch inference, 8GB+ recommended for fine-tuning"],"input_types":["raw text (string)","pre-tokenized text (list of tokens)","batched text (list of strings up to 512 tokens each)"],"output_types":["emotion class labels (string: 'joy', 'sadness', 'anger', 'fear', 'surprise', 'disgust', 'neutral')","logits (7-dimensional float vector, raw model outputs)","probabilities (7-dimensional float vector, softmax-normalized scores summing to 1.0)"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-j-hartmann--emotion-english-distilroberta-base__cap_1","uri":"capability://data.processing.analysis.batch.emotion.classification.with.configurable.aggregation","name":"batch emotion classification with configurable aggregation","description":"Processes multiple text samples in parallel batches (configurable batch size, typically 8-64) and aggregates emotion predictions across documents. Supports multiple aggregation strategies: per-sample class labels with confidence scores, document-level emotion distributions (mean probability across samples), or emotion-weighted summaries for multi-document analysis. Uses HuggingFace DataLoader abstraction to handle variable-length sequences with automatic padding/truncation to 512 tokens.","intents":["Analyze emotion trends across large corpora (thousands of tweets, reviews, comments) without sequential inference bottlenecks","Aggregate emotions from multi-turn conversations or multi-document threads to determine overall sentiment trajectory","Generate emotion statistics for dashboards (e.g., '45% of feedback is angry, 30% neutral, 25% joyful')","Prepare emotion-labeled datasets for downstream fine-tuning or evaluation of emotion-aware models"],"best_for":["Data engineers processing large-scale social media or review datasets (100K+ documents)","Analytics teams generating emotion-based KPIs and dashboards","ML teams preparing labeled datasets for emotion-aware model training","Batch processing pipelines (Spark, Airflow, Kubernetes jobs) requiring high throughput"],"limitations":["Batch processing introduces latency variance — optimal batch size depends on GPU memory (8-16 on 2GB GPU, 32-64 on 8GB+), requiring tuning per deployment","Aggregation strategies are post-hoc (mean/max pooling) — no learned document-level emotion representation, limiting expressiveness for complex multi-document scenarios","No built-in handling of document-level context — each sample is classified independently, losing inter-document relationships (e.g., emotion evolution in conversation threads)","Memory overhead for large batches — processing 1000 documents requires ~2-4GB RAM depending on average text length"],"requires":["Python 3.7+","PyTorch or TensorFlow (same as single-sample inference)","transformers library with DataLoader support (4.0+)","GPU recommended for batch size >32 (CPU inference ~5-10x slower)"],"input_types":["list of text strings (variable length, auto-padded to 512 tokens)","CSV/JSON files with text column","streaming data (via generator functions)"],"output_types":["DataFrame with columns: [text, emotion_label, confidence_score, logits_vector]","aggregated statistics: {emotion: count, emotion: percentage}","emotion distribution vectors (7-dim probability per document)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-j-hartmann--emotion-english-distilroberta-base__cap_2","uri":"capability://code.generation.editing.fine.tuning.on.custom.emotion.labeled.datasets","name":"fine-tuning on custom emotion-labeled datasets","description":"Enables transfer learning by unfreezing and retraining the DistilRoBERTa backbone on custom emotion-labeled datasets with configurable learning rates, epochs, and loss functions. Uses standard PyTorch/TensorFlow training loops with cross-entropy loss for multi-class classification. Supports gradient accumulation for effective larger batch sizes on memory-constrained hardware, and mixed-precision training (FP16) to reduce memory footprint by ~50% while maintaining accuracy.","intents":["Adapt the base model to domain-specific emotion patterns (e.g., financial news, medical feedback, gaming communities) where social media training data is misaligned","Improve performance on underrepresented emotion classes (e.g., 'fear' or 'surprise') by reweighting loss or using focal loss","Create specialized emotion classifiers for non-English languages by fine-tuning on translated or multilingual datasets","Reduce inference latency by distilling the model further into a smaller student model via knowledge distillation"],"best_for":["ML engineers with domain expertise and labeled datasets (500+ samples minimum, 5000+ recommended)","Teams building proprietary emotion models for competitive advantage","Researchers experimenting with emotion taxonomy variations or loss functions","Organizations with privacy requirements needing on-premise model training"],"limitations":["Requires labeled training data — no unsupervised fine-tuning capability. Annotation cost is typically $0.10-$0.50 per sample for crowdsourced labels","Risk of catastrophic forgetting — fine-tuning on small datasets (<1000 samples) may degrade performance on original social media domain without careful regularization (learning rate decay, early stopping)","Training time: ~30 minutes on single GPU (8GB) for 5000 samples with batch size 16, ~2-4 hours on CPU — not suitable for rapid iteration without GPU access","Hyperparameter sensitivity — optimal learning rate, warmup steps, and weight decay vary by dataset size and domain, requiring validation set tuning","No built-in class imbalance handling — datasets with skewed emotion distributions (e.g., 80% neutral) require manual loss weighting or oversampling"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.0+","GPU with 6GB+ VRAM (RTX 3060, A100, V100) for efficient training; CPU training possible but 10-50x slower","Labeled dataset in CSV/JSON format with text and emotion label columns","Optional: Weights & Biases or MLflow for experiment tracking"],"input_types":["CSV with columns: [text, emotion_label]","JSON Lines format: {\"text\": \"...\", \"label\": \"joy\"}","HuggingFace Dataset objects"],"output_types":["fine-tuned model weights (PyTorch .pt or TensorFlow SavedModel format)","training metrics: {loss, accuracy, precision, recall, f1 per epoch}","validation results on held-out test set"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-j-hartmann--emotion-english-distilroberta-base__cap_3","uri":"capability://data.processing.analysis.emotion.prediction.with.confidence.based.filtering.and.thresholding","name":"emotion prediction with confidence-based filtering and thresholding","description":"Returns emotion predictions with associated confidence scores (softmax probabilities) and supports confidence-based filtering to exclude low-confidence predictions. Enables threshold-based decision rules (e.g., 'only flag as angry if confidence > 0.85') and abstention strategies (e.g., 'return neutral if top-2 emotions are within 5% probability'). Useful for downstream systems requiring high-precision predictions or explicit uncertainty quantification.","intents":["Filter out ambiguous emotion predictions to improve downstream decision quality (e.g., only escalate support tickets with high-confidence anger)","Implement confidence-based routing (high-confidence predictions → automated response, low-confidence → human review)","Generate uncertainty estimates for model monitoring and anomaly detection (e.g., alert when confidence drops below historical baseline)","Build ensemble predictions by combining this model's confidence scores with other emotion classifiers"],"best_for":["Content moderation teams requiring high-precision emotion detection (false positives are costly)","Customer support systems routing tickets based on emotional urgency with human fallback","Model monitoring/observability teams tracking prediction confidence as a proxy for data drift","Ensemble learning practitioners combining multiple emotion models"],"limitations":["Confidence scores are not calibrated — raw softmax probabilities may not reflect true misclassification likelihood, especially on out-of-distribution text. Requires post-hoc calibration (temperature scaling, Platt scaling) for reliable uncertainty","No principled uncertainty quantification — softmax confidence is a point estimate, not a Bayesian posterior. Bayesian variants (MC Dropout, ensemble methods) would provide better uncertainty but are not built-in","Threshold selection is manual and dataset-dependent — optimal confidence threshold varies by use case and requires validation set tuning, no automatic threshold optimization","Confidence scores may be artificially high on adversarial inputs or out-of-distribution text, leading to false confidence"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch or TensorFlow","Optional: scikit-learn for confidence calibration"],"input_types":["text string","batched text (list of strings)"],"output_types":["emotion label (string)","confidence score (float, 0.0-1.0)","full probability distribution (7-dim vector)","filtered predictions (None if below threshold)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-j-hartmann--emotion-english-distilroberta-base__cap_4","uri":"capability://automation.workflow.deployment.to.cloud.inference.endpoints.with.auto.scaling","name":"deployment to cloud inference endpoints with auto-scaling","description":"Model is compatible with HuggingFace Inference Endpoints and text-embeddings-inference (TEI) servers, enabling serverless or containerized deployment with automatic scaling. Supports both REST API and gRPC interfaces for low-latency inference. Deployments automatically handle batching, caching, and load balancing across multiple replicas. Compatible with Azure ML, AWS SageMaker, and Kubernetes for enterprise deployment patterns.","intents":["Deploy emotion classification as a scalable microservice without managing infrastructure (serverless via HuggingFace Endpoints)","Integrate emotion detection into production ML pipelines with auto-scaling based on request volume","Build real-time emotion analysis APIs with sub-100ms latency SLA via optimized inference servers (TEI, vLLM)","Enable multi-tenant emotion classification with resource isolation and billing per API key"],"best_for":["DevOps/MLOps engineers deploying models to production","Startups and small teams avoiding infrastructure management overhead","Enterprises requiring compliance (HIPAA, GDPR) with on-premise or private cloud deployment","Teams building real-time APIs with strict latency requirements (<100ms)"],"limitations":["HuggingFace Endpoints pricing: ~$0.06/hour for CPU, ~$0.30/hour for GPU (as of 2024) — cost scales with uptime, not usage, making it expensive for bursty workloads","Cold start latency: ~2-5 seconds for serverless endpoints on first request after idle period; warm inference ~50-150ms on CPU, ~10-20ms on GPU","Batch size optimization is automatic but opaque — users cannot directly control batching behavior, limiting fine-tuning for specific latency/throughput tradeoffs","No built-in model versioning or A/B testing — deploying new model versions requires endpoint recreation or manual traffic splitting","Data residency constraints — HuggingFace Endpoints may not meet data sovereignty requirements for regulated industries (GDPR, HIPAA)"],"requires":["HuggingFace account with API token","Docker (for self-hosted TEI deployment)","Kubernetes cluster (optional, for on-premise deployment)","Cloud provider account (Azure, AWS) for SageMaker/Azure ML integration","REST client or SDK (Python requests, curl, etc.)"],"input_types":["JSON payload: {\"inputs\": \"text to classify\"}","batched JSON: {\"inputs\": [\"text1\", \"text2\", ...]}"],"output_types":["JSON response: {\"label\": \"joy\", \"score\": 0.95}","batched response: [{\"label\": \"joy\", \"score\": 0.95}, ...]"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-j-hartmann--emotion-english-distilroberta-base__cap_5","uri":"capability://data.processing.analysis.emotion.prediction.with.explainability.via.attention.visualization","name":"emotion prediction with explainability via attention visualization","description":"Extracts and visualizes token-level attention weights from the transformer to identify which words/phrases most influenced the emotion prediction. Uses attention head aggregation (averaging attention across heads and layers) to produce interpretable saliency maps. Enables generation of highlighted text showing emotion-driving tokens, useful for understanding model decisions and debugging misclassifications.","intents":["Debug model misclassifications by identifying which tokens the model attended to (e.g., 'why did it classify this as angry?')","Generate human-interpretable explanations for end-users (highlight words that triggered emotion detection)","Validate model behavior on edge cases (e.g., sarcasm, negation) by examining attention patterns","Build trust in automated emotion-based decisions by showing evidence for classification"],"best_for":["ML researchers and practitioners debugging model behavior","Content moderation teams explaining decisions to users","Compliance/audit teams documenting model reasoning for regulatory requirements","UX designers building transparent emotion-aware interfaces"],"limitations":["Attention is not a true explanation — attention weights correlate with model decisions but don't causally explain them. High attention to a token doesn't guarantee it caused the prediction","Attention aggregation is lossy — averaging across 12 layers and 12 heads discards fine-grained information about which specific layers/heads drove the decision","Visualization is post-hoc and non-interactive — no built-in tools for interactive exploration or counterfactual analysis (e.g., 'what if I remove this token?')","Sarcasm and negation are often misattributed — attention may focus on sentiment-bearing words while missing negation context (e.g., 'not happy' may attend to 'happy' rather than 'not')"],"requires":["Python 3.7+","transformers library 4.0+ with attention output enabled","PyTorch or TensorFlow","Optional: matplotlib or plotly for visualization"],"input_types":["text string","pre-tokenized text"],"output_types":["attention weight matrix (sequence_length x num_heads x num_layers)","aggregated saliency scores per token (sequence_length,)","HTML/image visualization with highlighted tokens"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+ (model supports both via HuggingFace transformers library)","transformers library 4.0+","~350MB disk space for model weights (DistilRoBERTa-base is ~270MB + config/tokenizer)","4GB+ RAM for batch inference, 8GB+ recommended for fine-tuning","PyTorch or TensorFlow (same as single-sample inference)","transformers library with DataLoader support (4.0+)","GPU recommended for batch size >32 (CPU inference ~5-10x slower)","PyTorch 1.9+ or TensorFlow 2.4+","GPU with 6GB+ VRAM (RTX 3060, A100, V100) for efficient training; CPU training possible but 10-50x slower"],"failure_modes":["Trained exclusively on English social media text — performance degrades significantly on formal writing, technical documentation, or non-English languages","7-class taxonomy may not capture fine-grained emotional nuance (e.g., distinguishes anger from frustration only through confidence scores, not separate classes)","Inference latency ~50-150ms per sample on CPU, ~10-20ms on GPU — not suitable for real-time streaming with <5ms SLA","Context window limited to 512 tokens (standard BERT/RoBERTa constraint) — long-form documents require chunking or summarization","No confidence calibration — raw logits may not reflect true probability of misclassification, especially on out-of-distribution text","Batch processing introduces latency variance — optimal batch size depends on GPU memory (8-16 on 2GB GPU, 32-64 on 8GB+), requiring tuning per deployment","Aggregation strategies are post-hoc (mean/max pooling) — no learned document-level emotion representation, limiting expressiveness for complex multi-document scenarios","No built-in handling of document-level context — each sample is classified independently, losing inter-document relationships (e.g., emotion evolution in conversation threads)","Memory overhead for large batches — processing 1000 documents requires ~2-4GB RAM depending on average text length","Requires labeled training data — no unsupervised fine-tuning capability. Annotation cost is typically $0.10-$0.50 per sample for crowdsourced labels","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7245325664146516,"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":"2026-05-03T14:23:00.976Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":803974,"model_likes":493}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=j-hartmann--emotion-english-distilroberta-base","compare_url":"https://unfragile.ai/compare?artifact=j-hartmann--emotion-english-distilroberta-base"}},"signature":"PqBIC+rAXLxU47rW/WhDGckoD4gRaviJu6fJ0HBfZ+0sydTKShYNwQk0XgoPKb3AEAv40gVu/urv6jKAYPlUDw==","signedAt":"2026-06-21T04:07:09.367Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/j-hartmann--emotion-english-distilroberta-base","artifact":"https://unfragile.ai/j-hartmann--emotion-english-distilroberta-base","verify":"https://unfragile.ai/api/v1/verify?slug=j-hartmann--emotion-english-distilroberta-base","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"}}