{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-lucas-leme--finbert-pt-br","slug":"lucas-leme--finbert-pt-br","name":"FinBERT-PT-BR","type":"model","url":"https://huggingface.co/lucas-leme/FinBERT-PT-BR","page_url":"https://unfragile.ai/lucas-leme--finbert-pt-br","categories":["data-analysis"],"tags":["transformers","pytorch","bert","text-classification","pt","license:apache-2.0","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-lucas-leme--finbert-pt-br__cap_0","uri":"capability://data.processing.analysis.portuguese.financial.sentiment.classification","name":"portuguese financial sentiment classification","description":"Classifies Portuguese-language financial text into sentiment categories (positive, negative, neutral) using a BERT-based transformer fine-tuned on financial domain corpora. The model leverages masked language modeling pre-training followed by supervised fine-tuning on labeled financial documents, enabling it to capture domain-specific terminology and sentiment patterns in Portuguese financial discourse without requiring manual feature engineering.","intents":["Analyze sentiment of Portuguese financial news articles to track market sentiment","Classify earnings call transcripts in Portuguese for investor sentiment analysis","Categorize customer feedback from Brazilian financial services for product insights","Batch process financial documents in Portuguese to identify bullish vs bearish signals"],"best_for":["Brazilian fintech companies analyzing local market sentiment","Financial analysts processing Portuguese-language earnings reports and news","NLP teams building Portuguese-specific financial intelligence systems","Researchers studying sentiment dynamics in Portuguese-speaking financial markets"],"limitations":["Fine-tuned exclusively on Portuguese financial text — performance degrades significantly on non-financial Portuguese or other Romance languages","Requires text preprocessing and tokenization compatible with BERT's WordPiece vocabulary — special financial terms may be subword-tokenized, reducing semantic precision","Context window limited to 512 tokens — longer financial documents require chunking or summarization before classification","No confidence calibration or uncertainty quantification — outputs raw logits without probability calibration for risk-sensitive applications","Inference latency ~200-400ms per document on CPU; GPU acceleration recommended for production batch processing"],"requires":["Python 3.7+","transformers library (HuggingFace) version 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Minimum 2GB RAM for model loading; 4GB+ recommended for batch inference","Internet connection for initial model download (~440MB for full model weights)"],"input_types":["raw text (Portuguese)","pre-tokenized sequences","text files (UTF-8 encoded)"],"output_types":["classification logits (3-dimensional vector for sentiment classes)","predicted class labels (positive/negative/neutral)","attention weights (optional, for interpretability)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lucas-leme--finbert-pt-br__cap_1","uri":"capability://data.processing.analysis.batch.financial.text.embedding.generation","name":"batch financial text embedding generation","description":"Generates fixed-dimensional dense vector embeddings (768-dimensional) for Portuguese financial text by extracting the [CLS] token representation from the final transformer layer. These embeddings capture semantic meaning in a continuous vector space, enabling downstream tasks like similarity search, clustering, and retrieval without requiring additional fine-tuning. The model uses the standard BERT pooling strategy where the [CLS] token aggregates contextual information across the entire input sequence.","intents":["Build semantic search over financial documents to find similar news articles or earnings calls","Cluster financial documents by topic or sentiment theme for portfolio analysis","Create vector database indexes for retrieval-augmented generation (RAG) over financial corpora","Compute document similarity matrices to identify correlated financial events across Portuguese sources"],"best_for":["Teams building vector databases (Pinecone, Weaviate, Milvus) for financial document retrieval","Researchers conducting large-scale analysis of Portuguese financial text corpora","Production systems requiring semantic search over financial documents with sub-100ms latency","ML engineers implementing similarity-based recommendation systems for financial content"],"limitations":["Fixed 768-dimensional embeddings may not capture all nuances of complex financial concepts — dimensionality reduction (PCA, UMAP) may lose information","Embeddings are not normalized by default — cosine similarity requires L2 normalization before comparison","No fine-tuning capability exposed through HuggingFace model card — embeddings reflect pre-training + financial fine-tuning only, not task-specific optimization","Batch processing requires loading entire model into memory — not suitable for resource-constrained edge devices without quantization","Embedding drift over time as financial language evolves — model does not adapt to new terminology without retraining"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","4GB+ RAM for batch processing","Vector database client library (optional, for storage/retrieval)"],"input_types":["raw Portuguese text","tokenized sequences (input_ids, attention_mask)"],"output_types":["numpy arrays (768-dimensional float32 vectors)","torch tensors","normalized embeddings (after L2 normalization)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lucas-leme--finbert-pt-br__cap_2","uri":"capability://automation.workflow.multi.provider.model.serving.and.inference.optimization","name":"multi-provider model serving and inference optimization","description":"Supports deployment across multiple inference backends including HuggingFace Inference Endpoints, Azure ML, and text-embeddings-inference (TEI) via standardized model artifact exports. The model can be served through REST APIs, containerized inference servers, or integrated into ML pipelines without code changes by leveraging the transformers library's unified model loading interface and ONNX export capabilities for hardware-accelerated inference.","intents":["Deploy the model to production via Azure ML for enterprise compliance and governance","Set up auto-scaling inference endpoints on HuggingFace for variable traffic patterns","Containerize the model for Kubernetes-based inference with GPU acceleration","Integrate the model into existing ML pipelines (SageMaker, Vertex AI) without retraining"],"best_for":["DevOps teams deploying NLP models to cloud infrastructure (Azure, AWS, GCP)","Organizations requiring multi-cloud or hybrid deployment flexibility","Teams building inference microservices with containerization (Docker, Kubernetes)","Production systems needing inference optimization (quantization, ONNX compilation)"],"limitations":["No built-in model versioning or A/B testing framework — requires external orchestration for canary deployments","Inference latency varies significantly by backend: HuggingFace Endpoints (~500ms), Azure ML (~300ms), TEI with GPU (~50ms) — no unified SLA","ONNX export requires manual conversion and validation — not all transformer operations are ONNX-compatible, may require fallback to PyTorch","No built-in monitoring, logging, or observability — requires integration with external APM tools (DataDog, New Relic)","Cold start latency on serverless platforms (AWS Lambda) can exceed 30 seconds due to model size (~440MB)"],"requires":["Cloud account (Azure, AWS, GCP, or HuggingFace Pro)","Docker and container registry (for containerized deployment)","transformers library 4.0+","ONNX Runtime (optional, for inference optimization)","Kubernetes cluster (optional, for orchestrated deployment)"],"input_types":["REST API requests (JSON with text payload)","batch inference jobs (CSV, Parquet)","streaming data (Kafka topics, message queues)"],"output_types":["JSON responses (classification logits, predicted labels)","batch prediction files (CSV, Parquet)","streaming predictions (Kafka, message queues)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lucas-leme--finbert-pt-br__cap_3","uri":"capability://code.generation.editing.fine.tuning.and.transfer.learning.for.domain.specific.financial.tasks","name":"fine-tuning and transfer learning for domain-specific financial tasks","description":"Provides a pre-trained checkpoint optimized for financial text that can be further fine-tuned on downstream tasks (e.g., entity extraction, aspect-based sentiment, risk classification) using standard HuggingFace Trainer API or custom training loops. The model's weights encode financial domain knowledge from pre-training, reducing the amount of labeled data required for task-specific fine-tuning compared to generic BERT — typically 10-50% less labeled data needed for convergence on financial tasks.","intents":["Fine-tune the model on proprietary financial datasets for company-specific sentiment classification","Adapt the model to classify financial risk categories (high/medium/low) with limited labeled examples","Transfer the model to related tasks like financial entity recognition or aspect extraction","Create ensemble models by fine-tuning multiple copies on different financial subdomains (equity, fixed income, derivatives)"],"best_for":["Data scientists with 100-1000 labeled financial documents wanting to build custom classifiers","Financial institutions building proprietary NLP models on internal corpora","Teams with domain expertise but limited ML engineering resources","Researchers studying transfer learning effectiveness in financial NLP"],"limitations":["Fine-tuning requires GPU (NVIDIA A100/V100 recommended) — CPU training is prohibitively slow (>24 hours for moderate datasets)","Hyperparameter sensitivity — financial domain fine-tuning is sensitive to learning rate and warmup steps; requires validation set tuning","Catastrophic forgetting risk — aggressive fine-tuning can degrade performance on general financial tasks while improving on specific subtasks","No built-in data augmentation or active learning strategies — requires manual dataset curation and balancing","Fine-tuned models are not compatible with quantization or ONNX export without additional conversion steps"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ with CUDA 11.0+ (for GPU acceleration)","Labeled dataset (minimum 100-500 examples for meaningful fine-tuning)","GPU with 8GB+ VRAM (16GB+ recommended for batch size >16)"],"input_types":["CSV/JSON files with text and labels","HuggingFace Dataset objects","custom PyTorch DataLoader instances"],"output_types":["fine-tuned model checkpoint (PyTorch .bin files)","training metrics (loss, accuracy, F1 scores)","validation predictions and confusion matrices"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lucas-leme--finbert-pt-br__cap_4","uri":"capability://safety.moderation.interpretability.and.attention.visualization.for.financial.text.analysis","name":"interpretability and attention visualization for financial text analysis","description":"Exposes transformer attention weights from all 12 layers and 12 attention heads, enabling visualization and analysis of which input tokens the model attends to when making sentiment predictions. Attention patterns can be extracted and visualized using tools like BertViz or custom analysis scripts to understand which financial terms, entities, or phrases drive the model's classification decisions — useful for validating model behavior and building trust in production systems.","intents":["Visualize attention patterns to verify the model focuses on relevant financial terms (e.g., 'profit', 'loss', 'risk')","Debug misclassifications by examining which tokens received high attention in incorrect predictions","Generate explanations for stakeholders by showing which phrases influenced sentiment classification","Conduct error analysis to identify systematic biases or failure modes in financial sentiment detection"],"best_for":["Compliance and risk teams requiring explainability for regulatory reporting","Researchers studying attention mechanisms in financial NLP","ML engineers debugging model failures on edge cases","Product teams building user-facing explanations for model predictions"],"limitations":["Attention weights do not directly correspond to feature importance — high attention does not guarantee causal influence on predictions","Attention visualization is computationally expensive for long documents — requires storing 12×12 attention matrices per token, consuming significant memory","No built-in saliency or gradient-based explanation methods — requires external libraries (LIME, SHAP) for alternative interpretability approaches","Attention patterns are model-specific and may not generalize to other architectures — insights from FinBERT may not apply to GPT-based models","Visualization tools (BertViz) require additional dependencies and manual integration into analysis pipelines"],"requires":["Python 3.7+","transformers library 4.0+ (with output_attentions=True)","BertViz library (optional, for visualization)","matplotlib or plotly (for custom visualizations)","Jupyter notebook or similar interactive environment (recommended)"],"input_types":["raw Portuguese text","tokenized sequences with attention output enabled"],"output_types":["attention weight matrices (shape: [layers, heads, seq_len, seq_len])","attention visualizations (HTML, PNG)","token-level importance scores"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library (HuggingFace) version 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Minimum 2GB RAM for model loading; 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GPU acceleration recommended for production batch processing","Fixed 768-dimensional embeddings may not capture all nuances of complex financial concepts — dimensionality reduction (PCA, UMAP) may lose information","Embeddings are not normalized by default — cosine similarity requires L2 normalization before comparison","No fine-tuning capability exposed through HuggingFace model card — embeddings reflect pre-training + financial fine-tuning only, not task-specific optimization","Batch processing requires loading entire model into memory — not suitable for resource-constrained edge devices without quantization","Embedding drift over time as financial language evolves — model does not adapt to new terminology without retraining","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6564658280742616,"quality":0.2,"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":731712,"model_likes":29}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lucas-leme--finbert-pt-br","compare_url":"https://unfragile.ai/compare?artifact=lucas-leme--finbert-pt-br"}},"signature":"Es8x4awSlpedAnOhixVRaqiP3z1y7KAhAsLb8e2UHm/kEBThz7EqEI/Eu++28oGSTPI1orj26h00c9cLCTBVBg==","signedAt":"2026-06-21T22:28:37.835Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lucas-leme--finbert-pt-br","artifact":"https://unfragile.ai/lucas-leme--finbert-pt-br","verify":"https://unfragile.ai/api/v1/verify?slug=lucas-leme--finbert-pt-br","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"}}