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abhinav1857/
nlp_for_journal

Name: nlp_for_journal
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BERT + LSTM Sentiment Analysis Model

This repository contains a custom fine-tuned BERT + LSTM model for sentiment classification, trained on journal-style emotional writing. The model is designed to detect nuanced emotional states and works well for reflective, personal text (e.g., journaling, social media, diary entries).

🧠 Model Architecture

Base Encoder: bert-base-uncased (Hugging Face Transformers)
Sequence Modeling: One-layer LSTM to capture sentence-level emotional flow
Classification Layer: Fully connected layer with softmax
Loss Function: CrossEntropyLoss
Optimizer: AdamW

📦 Files Included

File	Description
`bert_lstm_sentiment_model.pt`	Trained PyTorch model (BERT + LSTM + Classifier)
`label_encoder.pkl`	`sklearn.preprocessing.LabelEncoder` used during training

🧾 Classes

The model was trained to classify the following emotional labels:

Positive
Negative
Neutral

🚀 How to Use

python

#pip install torch transformers scikit-learn #if not already installed, remove the first comment

import torch
import pickle
from transformers import BertTokenizer

# Load model
model = torch.load("bert_lstm_sentiment_model.pt", map_location=torch.device('cpu'))
model.eval()

# Load tokenizer and label encoder
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
with open("label_encoder.pkl", "rb") as f:
    label_encoder = pickle.load(f)

# Sample input
text = "I feel empty and overwhelmed today."

# Tokenize
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
    outputs = model(**inputs)
    predicted_class = torch.argmax(outputs.logits, dim=1).item()
    emotion = label_encoder.inverse_transform([predicted_class])[0]

print(f"Predicted Emotion: {emotion}")

abhinav1857/nlp_for_journal