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Verified signature# FinAI-BERT FinAI-BERT is a domain-specific BERT-based model fine-tuned for detecting AI-related disclosures in financial texts, particularly from banking annual reports. It was trained using a balanced dataset of 1,586 sentences (793 AI, 793 Non-AI) using `bert-base-uncased` as the base model. ## Intended Use FinAI-BERT is designed to assist researchers, analysts, and regulators in identifying AI narratives in financial disclosures at the sentence level. ## Performance - Accuracy: 99.7% - F1-score: 0.997 - ROC AUC: 1.000 - Brier Score: 0.0000 ## Training Data The model was trained on a manually annotated dataset from U.S. bank reports (2015–2023), covering AI and non-AI sentences. ## Files Included - `config.json`, `tokenizer.json`, `vocab.txt`, `model.safetensors`: Model files - `tokenizer_config.json`, `special_tokens_map.json`: Tokenizer configuration ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_…88e95e5 -
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Verified signature# FinAI-BERT **FinAI-BERT** is a domain-specific BERT-based model fine-tuned for detecting AI-related disclosures in financial texts, particularly banking annual reports. It was trained using a balanced dataset of 1,586 sentences (793 AI, 793 Non-AI) based on `bert-base-uncased`. ## Intended Use Designed for use by researchers, analysts, and regulators to identify AI-related discourse at the sentence level in financial documents. ## Performance - **Accuracy**: 99.7% - **F1-score**: 0.997 - **ROC AUC**: 1.000 - **Brier Score**: 0.0000 ## Training Details Trained on a manually labeled dataset from US banking reports (2015–2023). The model is robust in identifying AI narratives and offers explainability via SHAP. ## Files Included - `model.safetensors`, `config.json`, `tokenizer.json`, `vocab.txt`: Model files - `tokenizer_config.json`, `special_tokens_map.json`: Tokenizer settings ## Citation > Zafar, B. et al. (2025). *Detecting Artificial Intelligence Disclosures in Financial Tex…0b7d493 -
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