divyansh126/SentimentAnalysisBert

Model Details

• Model type: BERT-based sequence classification
• Base model: bert-base-uncased
• Number of classes: 3
• Trained on: Custom labeled dataset
• Framework: PyTorch with 🤗 Transformers
• Max sequence length: 512
• Tokenizer: bert-base-uncased

Model Description

This is a fine-tuned bert-base-uncased model for multi-class text classification. The model was trained on a custom dataset to classify text into 3 categories: Negative, Neutral, and Positive.

• Developed by: Divyansh Rajput
• Model type: Transformer(Bert-Base-Uncased)
• Language(s) (NLP): English
• License: Mit
• Finetuned from model : Bert-Base-Uncased

Model Sources

• Repository: https://huggingface.co/divyansh126/SentimentAnalysisBert/

📊 Classes

Label	Description
0	Negative
1	Neutral
2	Positive

🚀 How to Get Started with the Model##

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("divyansh126/") model = AutoModelForSequenceClassification.from_pretrained("your-username/your-model-name")

inputs = tokenizer("I loved the product!", return_tensors="pt") outputs = model(**inputs) predicted_class = outputs.logits.argmax(dim=-1).item()

🧪 Training Details

• Training framework: Hugging Face Trainer
• Epochs: 1
• Batch size: 32
• Learning rate: 2e-5
• Device: Trained on GPU (cuda)

Training Data

Dataset Link - https://www.kaggle.com/datasets/jp797498e/twitter-entity-sentiment-analysis

Evaluation

📈 Evaluation

Metric	Score
Accuracy	0.85
F1 Score	0.85
Precision	0.85
Recall	0.85

Summary

This model is a fine-tuned version of BERT (bert-base-uncased) for multi-class text classification. It has been trained to classify input text into three sentiment-based categories: Negative (0), Neutral (1), and Positive (2). The model was trained on a custom dataset using the Hugging Face Trainer API with PyTorch. It uses standard preprocessing with a maximum sequence length of 512 tokens. This fine-tuned BERT model achieves strong performance in sentiment classification tasks and is suitable for real-time predictions in production environments or APIs.