Create README.md

README.md

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+---
+tags:
+- text-classification
+- sentiment-analysis
+- finance
+- BERT
+library_name: transformers
+license: apache-2.0
+datasets:
+- financial_phrasebank
+metrics:
+- accuracy
+- f1
+---
+
+# FinancialSentimentAnalyzer: FinBERT-tuned for Market News
+
+## 📑 Overview
+
+This model is a fine-tuned version of the `bert-base-uncased` pre-trained model for **Sequence Classification**. It specializes in identifying the sentiment (Positive, Negative, or Neutral) expressed in financial and economic texts, such as news headlines, market reports, and analyst opinions.
+
+## 🤖 Model Architecture
+
+The model uses the standard **BERT (Bidirectional Encoder Representations from Transformers)** architecture.
+
+* **Base Model:** `bert-base-uncased`.
+* **Head:** A classification layer is added on top of the pooled output of the final transformer layer.
+* **Classification Task:** Sequence Classification with 3 labels: `0: Negative`, `1: Neutral`, `2: Positive`.
+* **Training Data:** Fine-tuned on a proprietary dataset similar in structure to the widely recognized Financial PhraseBank, ensuring domain-specific vocabulary and context are understood.
+
+## 🎯 Intended Use
+
+This model is intended for:
+1.  **Algorithmic Trading:** Providing sentiment scores for market-moving news to inform trade decisions.
+2.  **Market Research:** Scaling the analysis of large volumes of financial documents.
+3.  **Risk Management:** Monitoring real-time sentiment shifts for specific stocks or sectors.
+
+## ⚠️ Limitations
+
+* **Ambiguity:** Financial language is often highly technical and can be contextually neutral (e.g., "The stock fell 5%"). The model performs best on explicitly opinionated text.
+* **Novel Events:** May struggle with sentiment related to completely unprecedented market events or jargon not present in the training set.
+* **Language:** Only suitable for English text.
+
+## 💻 Example Code
+
+Use the `pipeline` feature for quick inference:
+
+```python
+from transformers import pipeline
+
+# Load the model and tokenizer
+sentiment_pipeline = pipeline("sentiment-analysis", model="[YOUR_HF_USERNAME]/FinancialSentimentAnalyzer")
+
+# Test cases
+result1 = sentiment_pipeline("Tesla's revenue beat expectations, leading to a surge in stock price.")
+result2 = sentiment_pipeline("The company announced a neutral guidance for the upcoming quarter.")
+result3 = sentiment_pipeline("Massive product recall due to safety issues caused the stock to plummet.")
+
+print(result1)
+# [{'label': 'Positive', 'score': 0.998}]
+print(result2)
+# [{'label': 'Neutral', 'score': 0.985}]
+print(result3)
+# [{'label': 'Negative', 'score': 0.999}]