sample_stock_sentiment_model

My Text Classification Model

This is a BERT-based text classification model trained on custom CSV data.

Model Details

• Base Model: bert-base-uncased
• Task: Text Classification
• Languages: English
• Labels: Positive (1), Negative (0)
• Dataset: Private CSV dataset (data.csv)
• Framework: Hugging Face Transformers (PyTorch)

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

# Hugging Face Hub model ID (online)
model_id = "Sathish-Dsk/sample_stock_sentiment_model"

# Load model and tokenizer directly from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

# Create sentiment analysis pipeline
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Test input
result = nlp("Stock prices of Reliance are expected to rise tomorrow.")
print(result)