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Browse files# 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