{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-prosusai--finbert","slug":"prosusai--finbert","name":"finbert","type":"model","url":"https://huggingface.co/ProsusAI/finbert","page_url":"https://unfragile.ai/prosusai--finbert","categories":["data-analysis"],"tags":["transformers","pytorch","tf","jax","bert","text-classification","financial-sentiment-analysis","sentiment-analysis","en","arxiv:1908.10063","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-prosusai--finbert__cap_0","uri":"capability://data.processing.analysis.financial.domain.sentiment.classification","name":"financial-domain sentiment classification","description":"Classifies text into sentiment categories (positive, negative, neutral) using a BERT-based transformer fine-tuned on financial corpora and domain-specific language patterns. The model leverages masked language modeling pre-training followed by supervised fine-tuning on labeled financial news, earnings calls, and analyst reports, enabling it to understand financial terminology and context-dependent sentiment expressions that differ from general-purpose sentiment models.","intents":["Analyze sentiment in financial news articles and earnings call transcripts to gauge market sentiment","Extract sentiment signals from analyst reports and financial commentary for investment decision support","Classify customer feedback and earnings call Q&A sections by sentiment to identify concerns or positive developments","Build financial sentiment indices from news feeds or social media for quantitative trading signals"],"best_for":["Quantitative analysts and fintech teams building sentiment-driven trading systems","Financial institutions analyzing earnings calls, news, and regulatory filings","Researchers studying financial market sentiment and its correlation with price movements","Risk management teams monitoring sentiment in financial communications"],"limitations":["Trained on English financial text only — performance degrades significantly on non-English or mixed-language inputs","Context window limited to 512 tokens (BERT standard) — long documents require chunking, risking sentiment fragmentation across boundaries","Fine-tuned on historical financial data — may not capture emerging financial terminology or novel market contexts (e.g., crypto, novel financial instruments)","Binary/ternary classification only — cannot express nuanced sentiment gradations or mixed sentiment within single documents","No temporal awareness — treats 2008 financial crisis language same as 2024 market conditions despite different semantic drift"],"requires":["Python 3.6+","PyTorch 1.9+ or TensorFlow 2.4+ or JAX (model supports all three frameworks)","Hugging Face Transformers library 4.0+","Minimum 2GB RAM for inference, 8GB+ recommended for batch processing","Internet connection for initial model download (~440MB)"],"input_types":["raw text (news articles, earnings transcripts, analyst reports, financial commentary)","pre-tokenized text sequences up to 512 tokens","batch inputs via Hugging Face pipeline API"],"output_types":["sentiment labels (positive, negative, neutral)","confidence scores per class (logits/probabilities)","structured JSON with label and score for each input"],"categories":["data-processing-analysis","financial-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-prosusai--finbert__cap_1","uri":"capability://tool.use.integration.multi.framework.model.inference.with.automatic.backend.selection","name":"multi-framework model inference with automatic backend selection","description":"Provides unified inference interface across PyTorch, TensorFlow, and JAX backends through Hugging Face Transformers abstraction layer, automatically selecting the optimal framework based on system availability and user preference. The model weights are framework-agnostic (stored in safetensors format), enabling seamless conversion and loading into any supported backend without retraining or weight manipulation.","intents":["Deploy the same model in PyTorch-based production systems and TensorFlow-based serving infrastructure without maintaining separate model versions","Integrate FinBERT into existing ML pipelines regardless of framework choice (PyTorch for research, TensorFlow for production, JAX for high-performance computing)","Reduce deployment friction by choosing the framework that matches existing infrastructure rather than refactoring code"],"best_for":["Teams with heterogeneous ML infrastructure spanning multiple frameworks","Organizations migrating from one framework to another while maintaining model consistency","Researchers prototyping in PyTorch but deploying via TensorFlow Serving or TFLite"],"limitations":["Framework conversion adds ~50-100ms latency on first load due to weight format translation","JAX backend requires explicit jit compilation for production performance — raw inference is slower than PyTorch/TensorFlow","Memory footprint varies by framework — TensorFlow typically uses 15-20% more RAM than PyTorch due to graph construction overhead","No automatic quantization or optimization across frameworks — must be applied per-backend separately"],"requires":["Hugging Face Transformers 4.0+","At least one of: PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.20+","safetensors library for efficient weight loading"],"input_types":["text strings or pre-tokenized token IDs","batch inputs as lists or numpy arrays"],"output_types":["framework-native tensors (torch.Tensor, tf.Tensor, jax.Array)","numpy arrays via .numpy() conversion"],"categories":["tool-use-integration","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-prosusai--finbert__cap_2","uri":"capability://data.processing.analysis.batch.inference.with.configurable.tokenization.and.padding","name":"batch inference with configurable tokenization and padding","description":"Processes multiple text inputs simultaneously through the Hugging Face pipeline API with automatic tokenization, padding, and batching strategies. The implementation handles variable-length sequences by applying dynamic padding (pad to longest in batch) or fixed-length padding, manages attention masks automatically, and supports both eager execution and batched processing for throughput optimization.","intents":["Process hundreds or thousands of financial documents in a single batch operation for daily sentiment analysis runs","Optimize inference throughput by batching inputs and leveraging GPU parallelization","Handle variable-length financial texts (short tweets vs. long earnings transcripts) without manual preprocessing"],"best_for":["Data engineers building batch sentiment analysis pipelines for financial data lakes","Teams processing daily feeds of financial news or earnings calls","Researchers analyzing large corpora of financial documents"],"limitations":["Batch size limited by GPU/CPU memory — typical batch sizes 8-64 on consumer GPUs, 128-512 on enterprise hardware","Dynamic padding adds overhead for heterogeneous batch lengths — batches with uniform lengths process 10-15% faster","No built-in result streaming — entire batch must complete before results are available, limiting real-time use cases","Tokenization preprocessing is single-threaded in default pipeline — CPU becomes bottleneck for very large batches (>10k documents)"],"requires":["Hugging Face Transformers 4.0+","PyTorch/TensorFlow/JAX installed","Sufficient GPU memory for batch size (minimum 4GB for batch_size=32)"],"input_types":["list of text strings","pandas DataFrame with text column","generator/iterator of text documents"],"output_types":["list of sentiment labels and confidence scores","pandas DataFrame with predictions","numpy array of logits"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-prosusai--finbert__cap_3","uri":"capability://tool.use.integration.hugging.face.hub.integration.with.model.versioning.and.caching","name":"hugging face hub integration with model versioning and caching","description":"Integrates with Hugging Face Model Hub for automatic model discovery, download, and local caching with version control. The implementation uses git-based versioning (via huggingface_hub library) to track model revisions, automatically downloads model weights on first use, caches them locally to avoid redundant downloads, and supports pinning specific model versions or branches for reproducibility.","intents":["Automatically download and cache the latest FinBERT model without manual weight management","Pin specific model versions in production to ensure reproducible sentiment analysis results across deployments","Update to improved model versions without code changes by simply updating the model identifier"],"best_for":["Teams deploying FinBERT in cloud environments with ephemeral storage","Production systems requiring model versioning and reproducibility","Researchers comparing results across different FinBERT versions"],"limitations":["Initial download requires internet connectivity and can take 2-5 minutes depending on bandwidth (440MB model)","Cache location must be writable — fails silently in read-only filesystems without explicit error messaging","No built-in cache invalidation strategy — stale cached weights persist until manually cleared","Hub API rate limits apply — bulk downloads of multiple model versions may trigger temporary throttling"],"requires":["huggingface_hub library 0.10+","Internet connectivity for initial model download","Writable filesystem with ~500MB free space for model cache","Optional: Hugging Face API token for private model access (not needed for FinBERT)"],"input_types":["model identifier string (e.g., 'ProsusAI/finbert')","optional revision parameter (branch, tag, or commit hash)"],"output_types":["loaded model object ready for inference","local filesystem path to cached model weights"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-prosusai--finbert__cap_4","uri":"capability://data.processing.analysis.tokenization.with.financial.vocabulary.and.subword.handling","name":"tokenization with financial vocabulary and subword handling","description":"Applies BERT's WordPiece tokenization algorithm with a vocabulary trained on financial corpora, breaking text into subword tokens that preserve financial terminology (e.g., 'EBITDA' stays intact rather than splitting into 'EB', '##IT', '##DA'). The tokenizer handles special tokens ([CLS], [SEP], [PAD], [UNK]) and maintains token-to-character mappings for interpretability, enabling sentiment attribution to specific financial terms.","intents":["Tokenize financial text while preserving domain-specific terms like ticker symbols, financial metrics, and acronyms","Map model predictions back to original text spans to identify which financial terms drove sentiment classification","Handle out-of-vocabulary financial terminology gracefully through subword fallback without losing semantic information"],"best_for":["Teams building interpretable financial sentiment systems that need to explain which terms influenced predictions","Financial NLP researchers analyzing which vocabulary drives sentiment in financial discourse","Compliance teams needing to audit model decisions by tracing predictions to source text"],"limitations":["Vocabulary is fixed at model training time — new financial terminology (e.g., novel crypto terms) falls back to [UNK] token, losing semantic information","Subword tokenization can fragment financial terms across multiple tokens — 'cryptocurrency' becomes 'crypt', '##o', '##currency', complicating interpretability","No built-in handling for financial symbols or special characters — '$', '%', commas in numbers require preprocessing","Token-to-character mapping is approximate for whitespace-heavy text — exact span recovery requires careful offset tracking"],"requires":["Hugging Face Transformers 4.0+","Pre-trained tokenizer from 'ProsusAI/finbert' model (automatically loaded)"],"input_types":["raw text strings","pre-tokenized word sequences"],"output_types":["token IDs (integers)","attention masks (binary array indicating padding)","token strings (for debugging)","token-to-character offset mappings"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-prosusai--finbert__cap_5","uri":"capability://planning.reasoning.attention.based.sentiment.attribution.and.model.interpretability","name":"attention-based sentiment attribution and model interpretability","description":"Exposes BERT's multi-head attention weights to enable attribution of sentiment predictions to specific input tokens and phrases. The implementation extracts attention matrices from all 12 transformer layers and 12 attention heads, aggregates them across layers, and computes token importance scores that indicate which words most influenced the final sentiment classification. This enables visualization of attention patterns and extraction of key financial terms driving predictions.","intents":["Understand which financial terms and phrases drove a specific sentiment prediction for model debugging and validation","Generate attention visualizations showing which parts of earnings calls or news articles most influenced sentiment classification","Extract key financial terms and concepts that the model considers most important for sentiment determination","Build trust in model predictions by showing interpretable evidence for each classification"],"best_for":["Financial analysts and compliance teams needing to audit and explain model predictions","Researchers studying which financial terminology and concepts drive market sentiment","Teams building user-facing applications requiring explainable sentiment analysis","Model validation and debugging workflows"],"limitations":["Attention weights are not true feature importance — high attention doesn't guarantee causal influence on prediction, only correlation","Attention aggregation across 144 heads (12 layers × 12 heads) can obscure individual head contributions — no single 'correct' aggregation method","Interpretability is post-hoc only — cannot modify attention patterns to change predictions without retraining","Attention visualization is most useful for short texts (<100 tokens) — long documents produce dense, hard-to-interpret attention matrices","No built-in statistical significance testing — cannot determine if attention to a token is meaningful or noise"],"requires":["Hugging Face Transformers 4.0+ with output_attentions=True","PyTorch/TensorFlow/JAX for attention extraction","Optional: matplotlib or plotly for attention visualization"],"input_types":["tokenized text sequences","model predictions with attention weights enabled"],"output_types":["attention weight matrices (shape: [num_layers, num_heads, seq_length, seq_length])","aggregated token importance scores (1D array per token)","attention visualizations (heatmaps or interactive plots)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-prosusai--finbert__cap_6","uri":"capability://automation.workflow.endpoint.deployment.with.azure.and.cloud.platform.support","name":"endpoint deployment with azure and cloud platform support","description":"Supports deployment to Hugging Face Inference Endpoints and Azure ML with automatic containerization, scaling, and API exposure. The model can be deployed via Hugging Face's managed inference service (which handles model serving, auto-scaling, and API management) or exported to Azure ML for integration with enterprise ML pipelines. Both paths abstract away infrastructure management and provide REST/gRPC APIs for remote inference.","intents":["Deploy FinBERT as a scalable REST API without managing containers or Kubernetes clusters","Integrate sentiment analysis into existing Azure ML pipelines and workflows","Expose FinBERT inference to downstream applications via standard HTTP endpoints"],"best_for":["Teams deploying to Hugging Face Inference Endpoints for managed, serverless inference","Enterprises with Azure ML infrastructure requiring sentiment analysis capabilities","Organizations needing production-grade inference APIs without DevOps overhead"],"limitations":["Hugging Face Inference Endpoints have variable latency (100-500ms per request) due to shared infrastructure — not suitable for sub-100ms SLA requirements","Azure ML deployment requires Azure subscription and familiarity with Azure ML SDK — steeper learning curve than Hugging Face Endpoints","Cold start latency on Hugging Face Endpoints can reach 5-10 seconds if endpoint scales down due to inactivity","No built-in request batching across multiple clients — each API call triggers separate inference, losing batch optimization benefits","Pricing scales with inference volume — high-throughput applications may be more cost-effective with self-hosted inference"],"requires":["Hugging Face account for Inference Endpoints deployment","Azure subscription and Azure ML workspace for Azure ML deployment","Hugging Face CLI or SDK for endpoint management","Optional: Docker for local testing before cloud deployment"],"input_types":["JSON payloads with text field (via REST API)","batch requests with multiple text inputs"],"output_types":["JSON responses with sentiment labels and confidence scores","HTTP status codes and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+","PyTorch 1.9+ or TensorFlow 2.4+ or JAX (model supports all three frameworks)","Hugging Face Transformers library 4.0+","Minimum 2GB RAM for inference, 8GB+ recommended for batch processing","Internet connection for initial model download (~440MB)","Hugging Face Transformers 4.0+","At least one of: PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.20+","safetensors library for efficient weight loading","PyTorch/TensorFlow/JAX installed","Sufficient GPU memory for batch size (minimum 4GB for batch_size=32)"],"failure_modes":["Trained on English financial text only — performance degrades significantly on non-English or mixed-language inputs","Context window limited to 512 tokens (BERT standard) — long documents require chunking, risking sentiment fragmentation across boundaries","Fine-tuned on historical financial data — may not capture emerging financial terminology or novel market contexts (e.g., crypto, novel financial instruments)","Binary/ternary classification only — cannot express nuanced sentiment gradations or mixed sentiment within single documents","No temporal awareness — treats 2008 financial crisis language same as 2024 market conditions despite different semantic drift","Framework conversion adds ~50-100ms latency on first load due to weight format translation","JAX backend requires explicit jit compilation for production performance — raw inference is slower than PyTorch/TensorFlow","Memory footprint varies by framework — TensorFlow typically uses 15-20% more RAM than PyTorch due to graph construction overhead","No automatic quantization or optimization across frameworks — must be applied per-backend separately","Batch size limited by GPU/CPU memory — typical batch sizes 8-64 on consumer GPUs, 128-512 on enterprise hardware","builder identity is not verified yet","no observed match outcomes 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