Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- nlp
+- sentiment-analysis
+- finance
+- trading
+- bert
+---
+
+# financial_sentiment_transformer_v2
+
+## Overview
+`financial_sentiment_transformer_v2` is a BERT-based model specifically fine-tuned on financial news, earnings call transcripts, and specialized social media feeds (e.g., StockTwits). It is designed to capture the nuanced language of market volatility and economic forecasting.
+
+## Model Architecture
+The model uses a standard **BERT-Base** (Bidirectional Encoder Representations from Transformers) backbone with a sequence classification head.
+
+
+
+- **Pre-training:** Initially trained on general-purpose text (Wikipedia/BooksCorpus).
+- **Fine-tuning:** Domain-specific fine-tuning on over 500,000 labeled financial snippets.
+- **Nuance Handling:** Specifically trained to distinguish between general negativity and "financial negativity" (e.g., "Yields dropped" can be bullish or bearish depending on context).
+
+## Intended Use
+- **Algorithmic Trading:** Providing sentiment scores as features for quantitative models.
+- **Market Intelligence:** Aggregating sentiment trends across thousands of daily news articles.
+- **Risk Management:** Monitoring sudden shifts in public sentiment regarding specific tickers or sectors.
+
+## Limitations
+- **Temporal Bias:** Financial jargon changes rapidly (e.g., "transitory inflation"); the model may require retraining as economic cycles shift.
+- **Sarcasm:** Like most NLP models, it struggles with highly sarcastic or ironic statements common in retail trading forums.
+- **Short Context:** Limited to 512 tokens, which may be insufficient for long-form macroeconomic reports.