{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-cardiffnlp--twitter-roberta-base-sentiment-latest","slug":"cardiffnlp--twitter-roberta-base-sentiment-latest","name":"twitter-roberta-base-sentiment-latest","type":"model","url":"https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest","page_url":"https://unfragile.ai/cardiffnlp--twitter-roberta-base-sentiment-latest","categories":["data-analysis"],"tags":["transformers","pytorch","tf","roberta","text-classification","en","dataset:tweet_eval","arxiv:2202.03829","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-cardiffnlp--twitter-roberta-base-sentiment-latest__cap_0","uri":"capability://data.processing.analysis.twitter.domain.sentiment.classification.with.roberta.embeddings","name":"twitter-domain sentiment classification with roberta embeddings","description":"Classifies text into negative, neutral, or positive sentiment using a RoBERTa base model fine-tuned on 124K tweets from the TweetEval dataset (arxiv:2202.03829). The model leverages RoBERTa's masked language modeling pretraining and domain-specific fine-tuning to capture sentiment patterns in informal, short-form social media text with special handling for hashtags, mentions, and emoji-adjacent language. Outputs probability scores across three sentiment classes with token-level attention weights available for interpretability.","intents":["Analyze sentiment of social media posts, customer feedback, or user-generated content at scale","Build sentiment-aware content moderation or recommendation systems","Monitor brand perception or campaign sentiment in real-time Twitter/X streams","Fine-tune downstream NLP tasks that require sentiment as a feature or auxiliary signal"],"best_for":["Data scientists and ML engineers building social media analytics pipelines","Teams deploying sentiment analysis in production with <500ms latency requirements","Researchers studying sentiment in informal/colloquial language domains","Developers integrating pre-trained models via Hugging Face Transformers without custom training"],"limitations":["Trained exclusively on English tweets — performance degrades significantly on non-English text, formal writing, or out-of-domain genres (medical, legal, technical)","Fixed vocabulary of ~50K BPE tokens — rare words or neologisms may be split into subword tokens, reducing semantic precision","No multi-label support — assigns single sentiment class per input, cannot express mixed sentiments (e.g., 'love the product but hate the price')","Context window limited to 512 tokens — longer documents require truncation or sliding-window approaches","Inference latency ~50-100ms per sample on CPU, requires GPU for batch processing >32 samples efficiently"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0)","4GB+ RAM for model loading (base model ~355MB)","Optional: CUDA 11.0+ for GPU acceleration"],"input_types":["raw text (string)","tokenized sequences (token IDs)","batched text arrays"],"output_types":["logits (raw model outputs, shape [batch_size, 3])","probability scores via softmax (shape [batch_size, 3], sums to 1.0)","predicted class labels (0=negative, 1=neutral, 2=positive)","attention weights (optional, for interpretability)"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cardiffnlp--twitter-roberta-base-sentiment-latest__cap_1","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.batching.and.mixed.precision.quantization","name":"batch inference with dynamic batching and mixed-precision quantization","description":"Supports efficient batch processing of multiple texts through Hugging Face Transformers' pipeline API with automatic padding/truncation, optional mixed-precision (fp16) inference for 2x speedup on compatible hardware, and dynamic batching to maximize GPU utilization. Integrates with ONNX Runtime for CPU inference optimization and supports model quantization (int8) for edge deployment, reducing model size from 355MB to ~90MB with <2% accuracy loss.","intents":["Process thousands of tweets/reviews per minute for real-time sentiment dashboards","Deploy sentiment analysis on resource-constrained devices (mobile, edge servers, Raspberry Pi)","Reduce inference costs in cloud environments by optimizing compute utilization","Build scalable batch jobs that process historical data or large document collections"],"best_for":["Production teams running high-throughput sentiment analysis (>1K samples/sec)","Edge computing scenarios with limited GPU/CPU resources","Cost-sensitive deployments on cloud platforms (AWS SageMaker, Azure ML, GCP Vertex)","Data engineers building ETL pipelines that classify millions of records"],"limitations":["Mixed-precision (fp16) requires NVIDIA GPU with compute capability >=7.0 (V100+) — older GPUs fall back to fp32, negating speedup","Quantization (int8) introduces 1-3% accuracy degradation on edge cases (borderline neutral/positive samples)","Dynamic batching adds ~10-20ms overhead per batch due to padding computation — not beneficial for single-sample inference","No built-in distributed inference — requires manual sharding across multiple GPUs/TPUs via data parallelism","Batch size tuning is hardware-dependent — optimal batch size varies 8-256 depending on GPU memory and input length"],"requires":["transformers library with pipeline support (>=4.0.0)","Optional: ONNX Runtime (>=1.10.0) for CPU optimization","Optional: torch-quantization or TensorRT for int8 quantization","CUDA 11.0+ for fp16 inference (optional but recommended)"],"input_types":["list of strings (variable length)","pandas DataFrame column","generator/iterator for streaming data"],"output_types":["batched logits (shape [batch_size, 3])","batched probability scores","batched class labels with confidence scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cardiffnlp--twitter-roberta-base-sentiment-latest__cap_2","uri":"capability://tool.use.integration.multi.framework.model.export.and.deployment.compatibility","name":"multi-framework model export and deployment compatibility","description":"Model is available in both PyTorch and TensorFlow formats with automatic conversion via Hugging Face Hub, enabling deployment across diverse inference engines (ONNX Runtime, TensorFlow Lite, TensorRT, Core ML). Supports HuggingFace Inference Endpoints for serverless deployment with auto-scaling, and is compatible with Azure ML, AWS SageMaker, and Google Vertex AI managed services via standard model registry integrations.","intents":["Deploy the same sentiment model across heterogeneous infrastructure (cloud, on-prem, edge)","Migrate from PyTorch development to TensorFlow production without retraining","Integrate sentiment analysis into existing ML pipelines built on different frameworks","Leverage managed inference services for auto-scaling without infrastructure management"],"best_for":["Teams with mixed ML stacks (PyTorch research, TensorFlow production)","Organizations using managed cloud ML platforms (Azure ML, SageMaker, Vertex AI)","Developers deploying to mobile/edge via TensorFlow Lite or Core ML","Enterprises requiring framework-agnostic model governance and versioning"],"limitations":["PyTorch↔TensorFlow conversion can introduce numerical precision differences (±0.1-0.5% accuracy variance) due to floating-point rounding in different backends","ONNX export requires manual opset version selection — incompatible opsets may cause silent inference errors or performance degradation","TensorFlow Lite conversion requires post-training quantization — dynamic shapes not fully supported, limiting flexibility for variable-length inputs","Managed inference endpoints (HF Endpoints, SageMaker) add 50-200ms latency vs self-hosted due to network overhead and cold-start penalties","Model versioning across frameworks can diverge — PyTorch and TensorFlow versions may have different update schedules on Hub"],"requires":["PyTorch (>=1.9.0) OR TensorFlow (>=2.4.0)","transformers library (>=4.0.0)","Optional: onnx (>=1.10.0) and onnxruntime (>=1.10.0) for ONNX export","Optional: tensorflow-lite (>=2.8.0) for mobile deployment","Cloud credentials (Azure, AWS, GCP) for managed service deployment"],"input_types":["PyTorch model state dict (.pt, .pth)","TensorFlow SavedModel format","ONNX model format (.onnx)","Hugging Face Hub model identifier (string)"],"output_types":["Framework-specific model artifacts","Inference API responses (JSON from managed endpoints)","Batch prediction outputs in framework-native format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cardiffnlp--twitter-roberta-base-sentiment-latest__cap_3","uri":"capability://data.processing.analysis.interpretable.sentiment.predictions.with.attention.visualization","name":"interpretable sentiment predictions with attention visualization","description":"Exposes token-level attention weights from RoBERTa's transformer layers, enabling visualization of which words/phrases most influenced the sentiment prediction. Integrates with Hugging Face's `output_attentions=True` flag to return attention matrices (shape [num_layers, num_heads, seq_length, seq_length]), allowing developers to build attention heatmaps, saliency maps, or LIME-style feature importance explanations without additional model inference.","intents":["Debug model predictions by identifying which words drove the sentiment classification","Build explainability dashboards that show users why a tweet was classified as positive/negative","Validate model behavior on edge cases (sarcasm, negation, mixed sentiment)","Generate human-interpretable explanations for regulatory compliance (GDPR, CCPA, AI Act)"],"best_for":["Data scientists validating model behavior and identifying failure modes","Product teams building user-facing explainability features","Compliance/audit teams requiring model transparency documentation","Researchers studying attention mechanisms in sentiment models"],"limitations":["Attention weights are correlative, not causal — high attention to a word doesn't prove it caused the prediction; requires additional probing (gradient-based attribution) for true feature importance","Attention visualization is most interpretable for short texts (<50 tokens) — longer sequences produce dense heatmaps that are difficult to parse visually","Extracting attention adds ~15-20% inference latency and 2-3x memory overhead per sample due to storing full attention matrices","Multi-head attention (12 heads in base model) requires aggregation strategy (mean, max, or learned weighting) — no consensus on best approach","Attention patterns don't directly map to linguistic phenomena (e.g., high attention to 'not' doesn't guarantee negation handling)"],"requires":["transformers library (>=4.0.0)","PyTorch or TensorFlow with attention output support","Optional: matplotlib, seaborn, or plotly for visualization","Optional: captum or integrated-gradients for gradient-based attribution"],"input_types":["raw text (string)","tokenized sequences with attention mask"],"output_types":["attention weight tensors (shape [num_layers, num_heads, seq_length, seq_length])","aggregated attention heatmaps","token importance scores (derived from attention)","visualization-ready JSON (token-attention pairs)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cardiffnlp--twitter-roberta-base-sentiment-latest__cap_4","uri":"capability://code.generation.editing.fine.tuning.and.transfer.learning.with.task.specific.adaptation","name":"fine-tuning and transfer learning with task-specific adaptation","description":"Model weights are fully trainable and can be fine-tuned on custom sentiment datasets or adapted for related tasks (emotion classification, stance detection, toxicity scoring) via standard supervised learning. Supports parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) to reduce trainable parameters from 125M to ~1M while maintaining 99%+ accuracy, enabling rapid iteration on limited compute budgets. Integrates with Hugging Face Trainer API for distributed training, mixed-precision, gradient accumulation, and automatic hyperparameter tuning.","intents":["Adapt the model to domain-specific sentiment (e.g., financial sentiment, medical patient feedback, product reviews)","Fine-tune on proprietary data to improve accuracy on internal use cases without sharing data externally","Transfer learning to related tasks (emotion, stance, toxicity) with minimal labeled data (100-1K examples)","Reduce fine-tuning costs via LoRA for rapid experimentation on resource-constrained teams"],"best_for":["ML teams with domain-specific sentiment data (finance, healthcare, e-commerce)","Startups with limited compute budgets wanting to customize pre-trained models","Researchers exploring transfer learning from Twitter sentiment to other domains","Organizations with proprietary data that cannot be shared with external APIs"],"limitations":["Fine-tuning requires labeled data — typically 500-5K examples for good performance; <100 examples risk severe overfitting","Full fine-tuning requires 8GB+ GPU memory (A100 recommended for batch size >16); LoRA reduces to ~4GB but adds 10-15% inference latency","Catastrophic forgetting risk — fine-tuning on narrow domains may degrade performance on general Twitter sentiment","Hyperparameter sensitivity — learning rate, warmup steps, and batch size require tuning; poor choices lead to 2-5% accuracy drops","LoRA introduces rank selection complexity — optimal rank (8-64) is task-dependent and requires experimentation"],"requires":["transformers library (>=4.0.0)","PyTorch (>=1.9.0) with training support","GPU with 8GB+ VRAM (A100/V100 recommended)","Optional: peft library (>=0.2.0) for LoRA fine-tuning","Optional: wandb or tensorboard for training monitoring","Labeled dataset in CSV/JSON format with text and sentiment labels"],"input_types":["labeled text-sentiment pairs (CSV, JSON, Hugging Face Dataset)","pre-tokenized sequences with labels","streaming datasets for large corpora"],"output_types":["fine-tuned model weights (.pt, .safetensors)","training metrics (loss, accuracy, F1, validation curves)","LoRA adapters (low-rank weight matrices, ~5-10MB)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cardiffnlp--twitter-roberta-base-sentiment-latest__cap_5","uri":"capability://automation.workflow.real.time.streaming.sentiment.analysis.with.stateless.inference","name":"real-time streaming sentiment analysis with stateless inference","description":"Model is stateless (no recurrent connections or memory) and can process individual tweets/messages independently without context accumulation, enabling true real-time streaming via message queues (Kafka, RabbitMQ) or event-driven architectures (AWS Lambda, Google Cloud Functions). Inference is deterministic and reproducible — same input always produces identical output regardless of processing order, making it suitable for distributed, fault-tolerant pipelines without state synchronization overhead.","intents":["Monitor Twitter/X streams in real-time for brand sentiment or crisis detection","Build event-driven sentiment pipelines triggered by incoming messages (Kafka topics, SNS events)","Deploy sentiment analysis as serverless functions (Lambda, Cloud Functions) without persistent state","Aggregate sentiment across distributed systems without requiring centralized state management"],"best_for":["Teams building real-time social media monitoring dashboards","Event-driven architectures using Kafka, RabbitMQ, or cloud message queues","Serverless deployments (AWS Lambda, Google Cloud Functions, Azure Functions)","Distributed systems requiring stateless, horizontally-scalable sentiment analysis"],"limitations":["Stateless design means no context awareness — cannot leverage conversation history or user sentiment trends to improve predictions","No temporal modeling — cannot detect sentiment shifts within a conversation thread or user's sentiment evolution over time","Serverless cold-start latency (1-5 seconds for first invocation) can delay real-time processing; requires warm-up strategies or provisioned concurrency","Message queue throughput bottleneck — model inference (50-100ms/sample) may lag behind high-volume streams (>100 msgs/sec) without horizontal scaling","No built-in deduplication — identical messages processed multiple times waste compute; requires external deduplication logic"],"requires":["transformers library (>=4.0.0)","PyTorch or TensorFlow runtime","Message queue client (kafka-python, pika for RabbitMQ, boto3 for AWS SQS)","Optional: AWS Lambda, Google Cloud Functions, or Azure Functions runtime","Optional: Redis or DynamoDB for caching/deduplication"],"input_types":["individual text strings (tweets, messages)","JSON events from message queues","streaming data from APIs (Twitter API v2, etc.)"],"output_types":["sentiment predictions (class + confidence)","JSON events for downstream processing","metrics (throughput, latency, error rate)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0)","4GB+ RAM for model loading (base model ~355MB)","Optional: CUDA 11.0+ for GPU acceleration","transformers library with pipeline support (>=4.0.0)","Optional: ONNX Runtime (>=1.10.0) for CPU optimization","Optional: torch-quantization or TensorRT for int8 quantization","CUDA 11.0+ for fp16 inference (optional but recommended)","Optional: onnx (>=1.10.0) and onnxruntime (>=1.10.0) for ONNX export"],"failure_modes":["Trained exclusively on English tweets — performance degrades significantly on non-English text, formal writing, or out-of-domain genres (medical, legal, technical)","Fixed vocabulary of ~50K BPE tokens — rare words or neologisms may be split into subword tokens, reducing semantic precision","No multi-label support — assigns single sentiment class per input, cannot express mixed sentiments (e.g., 'love the product but hate the price')","Context window limited to 512 tokens — longer documents require truncation or sliding-window approaches","Inference latency ~50-100ms per sample on CPU, requires GPU for batch processing >32 samples efficiently","Mixed-precision (fp16) requires NVIDIA GPU with compute capability >=7.0 (V100+) — older GPUs fall back to fp32, negating speedup","Quantization (int8) introduces 1-3% accuracy degradation on edge cases (borderline neutral/positive samples)","Dynamic batching adds ~10-20ms overhead per batch due to padding computation — not beneficial for single-sample inference","No built-in distributed inference — requires manual sharding across multiple GPUs/TPUs via data parallelism","Batch size tuning is hardware-dependent — optimal batch size varies 8-256 depending on GPU memory and input length","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8341711959428844,"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":3359835,"model_likes":791}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cardiffnlp--twitter-roberta-base-sentiment-latest","compare_url":"https://unfragile.ai/compare?artifact=cardiffnlp--twitter-roberta-base-sentiment-latest"}},"signature":"hniESi5x1Ss7Xy+q8W05pBCAO6po6A6j2IsqITriLkYpaCMJTE+/OMyMZGCijwFQ3IzGdFaXbU7t7miLto21CA==","signedAt":"2026-06-22T22:29:30.803Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cardiffnlp--twitter-roberta-base-sentiment-latest","artifact":"https://unfragile.ai/cardiffnlp--twitter-roberta-base-sentiment-latest","verify":"https://unfragile.ai/api/v1/verify?slug=cardiffnlp--twitter-roberta-base-sentiment-latest","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"}}