Update README.md

README.md

@@ -5,4 +5,97 @@ language:
 - en
 metrics:
 - accuracy
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+---
+# Model Card for SentimentTensor
+
+This modelcard provides details about the SentimentTensor model, developed by Saish Shinde, for sentiment analysis using LSTM architecture.
+
+## Model Details
+
+### Model Description
+
+The SentimentTensor model is a deep learning model based on LSTM architecture, developed by Saish Shinde, for sentiment analysis tasks. It achieves an accuracy of 81% on standard evaluation datasets. The model is designed to classify text data into three categories: negative, neutral, and positive sentiments.
+
+- **Developed by:** Saish Shinde
+- **Model type:** LSTM-based Sequence Classification
+- **Language(s) (NLP):** English
+- **License:** No specific license
+
+
+
+# Dataset Used
+
+yelp dataset with 4.04GB compressed,8.65GB uncompressed data
+
+## Uses
+
+### Direct Use
+
+The SentimentTensor model can be directly used for sentiment analysis tasks without fine-tuning.
+
+### Downstream Use [optional]
+
+This model can be fine-tuned for specific domains or integrated into larger NLP applications.
+
+### Out-of-Scope Use
+
+The model may not perform well on highly specialized or domain-specific text data.
+
+## Bias, Risks, and Limitations
+
+The SentimentTensor model, like any LSTM-based model, may have biases and limitations inherent in its training data and architecture. It might sometimes struggle with capturing long-range dependencies or understanding context in complex sentences.
+
+### Recommendations
+
+Users should be aware of potential biases and limitations and evaluate results accordingly.
+
+## How to Get Started with the Model
+
+### 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
+text = "Your text data here"
+tokenized_input = tokenizer(text, return_tensors="pt")
+
+# Sentiment Analysis
+#Forward pass through the model
+outputs = model(**tokenized_input)
+
+#Get predicted sentiment label
+predicted_label = outputs.logits.argmax().item()
+
+# Example Usage
+
+#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 Architecture and Objective
+
+The SentimentTensor model is based on LSTM architecture, which is well-suited for sequence classification tasks like sentiment analysis. It uses long short-term memory cells to capture dependencies in sequential data.
+
+# Model Card Authors
+Saish Shinde
+