---
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.