Create REDME.md

REDME.md

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+# finance-sentiment-classifier-bert-base
+
+## Model Overview
+
+This model is a fine-tuned BERT-base model optimized for classifying the sentiment of financial news headlines, earnings call transcripts, and social media posts related to the finance sector. It supports three distinct sentiment labels: **NEGATIVE (0)**, **NEUTRAL (1)**, and **POSITIVE (2)**. The model achieves high F1-scores on proprietary financial sentiment datasets, making it suitable for high-stakes analysis.
+
+## Model Architecture
+
+The model is based on the **BERT (Bidirectional Encoder Representations from Transformers)** architecture.
+
+* **Base Model:** `bert-base-uncased`
+* **Modification:** The model is wrapped with a `BertForSequenceClassification` head. This means the pooled output of the BERT encoder (corresponding to the `[CLS]` token) is passed through a dropout layer and then a single linear layer (classifier) with 3 output units (one for each sentiment class), followed by a Softmax activation function.
+* **Input:** Tokenized text sequences (max length 512).
+* **Output:** Logits for the three sentiment classes.
+
+## Intended Use
+
+This model is intended for:
+
+* **Quantitative Finance:** Automating the categorization of large volumes of unstructured financial text data.
+* **Market Analysis:** Tracking sentiment shifts in specific stocks, sectors, or the overall market.
+* **Risk Management:** Early identification of negative media sentiment that may precede market events.
+* **Academic Research:** Studying the correlation between public sentiment and market movements.
+
+## Limitations and Ethical Considerations
+
+* **Domain Specificity:** While strong in finance, performance may degrade significantly on general domain text (e.g., movie reviews).
+* **Sarcasm/Context:** Like all NLP models, it may struggle with highly contextual, subtle, or sarcastic financial commentary that requires external knowledge.
+* **Bias:** The training data may implicitly contain biases related to specific companies or market events, which could affect prediction accuracy. Users should monitor for drift and bias in real-world application.
+* **Not Financial Advice:** The model's predictions are purely analytical and should **not** be used as the sole basis for making investment decisions.
+
+## Example Code
+
+To use the model in Python with the HuggingFace `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "YourOrg/finance-sentiment-classifier-bert-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example financial texts
+texts = [
+    "Stock price soared 15% after better-than-expected earnings report.", # Positive
+    "Company X faces significant regulatory hurdles, stock dropped 8%.",  # Negative
+    "Analyst issues a neutral 'Hold' rating on Company Y.",              # Neutral
+]
+
+# Tokenize and predict
+inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+predictions = torch.argmax(outputs.logits, dim=-1)
+labels = [model.config.id2label[p.item()] for p in predictions]
+
+for text, label in zip(texts, labels):
+    print(f"Text: '{text}' -> Sentiment: {label}")