Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- nlp
+- sentiment-analysis
+- bert
+- classification
+metrics:
+- accuracy
+- f1
+---
+
+# customer_feedback_sentiment_bert
+
+## Overview
+This model is a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model designed to categorize customer feedback into three distinct sentiment classes: Negative, Neutral, and Positive. It is optimized for short-to-medium length text such as product reviews, survey responses, and social media mentions.
+
+
+
+## Model Architecture
+The model utilizes the **BERT-Base-Uncased** backbone. 
+- **Layers**: 12 Transformer blocks
+- **Attention Heads**: 12
+- **Hidden Size**: 768
+- **Classification Head**: A linear layer on top of the `[CLS]` token output, followed by a softmax function to produce class probabilities.
+
+## Intended Use
+- **E-commerce**: Automating the analysis of product reviews to identify common pain points.
+- **Customer Support**: Prioritizing tickets based on the urgency/frustration detected in user messages.
+- **Market Research**: Aggregating sentiment trends across different platforms in real-time.
+
+## Limitations
+- **Language**: This specific instance is trained only on English text.
+- **Context Length**: Limited to 512 tokens; longer documents will be truncated, potentially losing critical sentiment cues at the end of the text.
+- **Sarcasm**: Like most NLP models, it may struggle with highly sarcastic or nuanced figurative language.