README.md

---
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.