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model_card.md

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+---
+language: en
+tags:
+- sentiment-analysis
+- text-classification
+license: MIT
+---
+
+# LSTM Sentiment Analysis Model
+
+## Model Description
+
+This is a sentiment analysis model trained on the IMDB Movie Review Dataset. It can classify the sentiment of a given movie review as positive or negative.
+
+The model architecture consists of an embedding layer, followed by an LSTM layer and a dense layer with sigmoid activation for binary classification.
+
+## Intended Use
+
+This model is intended to be used for sentiment analysis tasks, where the goal is to classify the sentiment of text data as positive or negative. It can be used to analyze movie reviews or other forms of text data.
+
+## Limitations and Ethical Considerations
+
+- The model's performance may vary depending on the quality and diversity of the input data. It is recommended to evaluate the model on your specific dataset to ensure its suitability for your task.
+- The model may not capture nuanced sentiments or sarcasm present in the text.
+- Care should be taken when interpreting the model's predictions and making decisions based on them. It is advisable to have human review and validation of the predictions for critical applications.
+
+## Training Data
+
+The model was trained on the IMDB Movie Review Dataset, which contains 50,000 movie reviews with binary sentiment labels (positive or negative).
+
+## Evaluated Metrics
+
+During training, the model's performance was evaluated using binary cross-entropy loss and accuracy metrics.
+
+## References
+
+- Dataset: [IMDB Movie Review Dataset](https://ai.stanford.edu/~amaas/data/sentiment/)
+- TensorFlow/Keras Documentation: [https://www.tensorflow.org/api_docs/python/tf/keras](https://www.tensorflow.org/api_docs/python/tf/keras)