---
license: mit
language:
- en
metrics:
- accuracy
library_name: sklearn
pipeline_tag: text-classification
tags:
- code
---


## Model Training

The sentiment analysis model is trained using a Support Vector Machine (SVM) classifier with a linear kernel. The cleaned text data is transformed into a bag-of-words representation using the CountVectorizer. The trained model is saved as `Sentiment_classifier_model.joblib`, and the corresponding TF-IDF vectorizer is saved as `vectorizer_model.joblib`.



# Usage :

from huggingface_hub import hf_hub_download
import joblib
from sklearn.preprocessing import LabelEncoder

model = joblib.load(
    hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
)

tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your path

def clean_text(text):
  return text.lower()

def predict_sentiment(user_input):
  """Predicts sentiment for a given user input."""
  cleaned_text = clean_text(user_input)
  input_matrix = tfidf_vectorizer.transform([cleaned_text])
  prediction = model.predict(input_matrix)[0]

  if isinstance(model.classes_, LabelEncoder):
    prediction = model.classes_.inverse_transform([prediction])[0]

  return prediction

user_input = input("Enter a sentence: ")

predicted_sentiment = predict_sentiment(user_input)

print(f"Predicted Sentiment: {predicted_sentiment}")



from transformers import AutoTokenizer, AutoModelForSequenceClassification
from sklearn.preprocessing import LabelEncoder
import joblib


def load_model_and_tokenizer(model_name="DineshKumar1329/Sentiment_Analysis"):
    """Loads the sentiment analysis model and tokenizer from Hugging Face Hub."""

    # Replace with desired model name if using a different model
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)

    return model, tokenizer


def clean_text(text):
    """Converts the input text to lowercase for case-insensitive processing."""
    return text.lower()


def predict_sentiment(user_input, model, tokenizer):
    """Predicts sentiment for a given user input."""

    cleaned_text = clean_text(user_input)
    encoded_text = tokenizer(cleaned_text, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**encoded_text)
        logits = outputs.logits
        prediction = torch.argmax(logits, dim=-1).item()

    if isinstance(model.config.label_list, LabelEncoder):
        prediction = model.config.label_list.inverse_transform([prediction])[0]

    return prediction


if __name__ == "__main__":
    model, tokenizer = load_model_and_tokenizer()

    user_input = input("Enter a sentence: ")

    predicted_sentiment = predict_sentiment(user_input, model, tokenizer)

    print(f"Predicted Sentiment: {predicted_sentiment}")