Create README.md

# SentimentTensor Model:

The SentimentTensor model is a custom sentiment analysis model trained using deep learning techniques. It is designed to analyze the sentiment of text data and classify it into three categories: positive, negative, and neutral.

# Overview:

The SentimentTensor model was developed to provide accurate sentiment analysis for text data. It uses a custom tokenization approach and is based on a deep learning architecture for efficient sentiment classification.

# Usage:

#Loading the Model:
You can load the SentimentTensor model using the Hugging Face library:

Python Code:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("your-model-name")
tokenizer = AutoTokenizer.from_pretrained("your-tokenizer-name")

# Tokenization:
Before using the model for sentiment analysis, tokenize your text data using the tokenizer:

Python Code:
text = "Your text data here"
tokenized_input = tokenizer(text, return_tensors="pt")

# Sentiment Analysis:

Perform sentiment analysis using the loaded model:

Python Code:
#Forward pass through the model
outputs = model(**tokenized_input)

#Get predicted sentiment label
predicted_label = outputs.logits.argmax().item()

# Example Usage:
Here's an example of how to use the SentimentTensor model for sentiment analysis:

Python Code:
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("your-model-name")
tokenizer = AutoTokenizer.from_pretrained("your-tokenizer-name")

# Tokenize text data
text = "This is a great movie!"
tokenized_input = tokenizer(text, return_tensors="pt")

# Perform sentiment analysis
outputs = model(**tokenized_input)
predicted_label = outputs.logits.argmax().item()

# Print predicted sentiment
sentiment_labels = ["negative", "neutral", "positive"]
print(f"Predicted Sentiment: {sentiment_labels[predicted_label]}")
Model Deployment

The SentimentTensor model is available for deployment on Hugging Face's model hub

# Acknowledgements

The SentimentTensor model was developed by Saish Shinde

README.md

@@ -0,0 +1,8 @@
+---
+datasets:
+- yelp_review_full
+language:
+- en
+metrics:
+- accuracy
+---