Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- sentiment-analysis
+- nlp
+- transformer
+- data-signal
+---
+
+# Data Signal Sentiment Transformer (v1.0)
+
+## Overview
+This model is a fine-tuned BERT-base architecture designed to extract the **Data Signal** (তথ্য সংকেত) of human emotion from unstructured text. In our framework, the "Data Signal" represents the core semantic sentiment isolated from linguistic noise. It is optimized for high-accuracy classification across social media, product reviews, and customer feedback datasets.
+
+
+
+## Model Architecture
+The model utilizes the standard BERT-base-uncased backbone with an added classification head:
+- **Encoder**: 12-layer, 768-hidden, 12-heads, 110M parameters.
+- **Input**: Tokenized text sequences ($max\_length=512$).
+- **Output**: Softmax distribution over three classes (Negative, Neutral, Positive).
+
+The optimization objective uses the standard Cross-Entropy Loss:
+$$\mathcal{L} = -\sum_{i=1}^{C} y_i \log(\hat{y}_i)$$
+
+## Intended Use
+- **Market Sentiment Analysis**: Monitoring the emotional "Data Signal" in real-time financial news.
+- **Brand Reputation**: Analyzing customer feedback to identify shifts in public perception.
+- **Content Moderation**: Filtering toxic interactions by identifying strong negative signals.
+
+## Limitations
+- **Sarcasm Detection**: Like most transformer-based classifiers, this model may struggle with heavy irony or context-dependent sarcasm.
+- **Domain Specificity**: While robust, the "Data Signal" extraction is most accurate on general English prose and may require further fine-tuning for specialized legal or medical jargon.
+- **Context Window**: Limited to 512 tokens; longer documents will be truncated.