Update and rename readme.md to README.md

Author: ananas304

Recommendation Models/sentenceAutoCompletion/README.md

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+## Sentence Auto-Completion
+
+This project implements a sentence auto-completion model using a deep learning approach, specifically leveraging LSTM (Long Short-Term Memory) networks from the TensorFlow/Keras library. The goal is to predict the next word in a sequence of text, providing automatic sentence completion functionality.
+
+### Project Structure
+```
+├── SentenceAutoCompletion.ipynb  # Jupyter notebook containing the entire implementation
+├── README.md                     # Project overview and instructions
+└── holmes.txt                    # Input text file used for training the model
+```
+
+### Model Overview
+
+The project builds a sentence auto-completion model with the following components:
+- **LSTM-based model**: Uses a recurrent neural network (RNN) with LSTM layers to predict the next word in a sequence of text.
+- **Tokenizer and Padding**: Text data is tokenized, and sequences are padded to ensure uniform input size for the neural network.
+- **Bidirectional LSTM**: A bidirectional LSTM is used to capture both past and future context in text sequences.
+  
+The training text is taken from *Project Gutenberg* and is preprocessed to remove special characters, emojis, and extra spaces.
+
+### Setup and Dependencies
+
+To set up this project, you need to install the following libraries:
+
+```bash
+pip install tensorflow nltk pandas
+```
+
+### Data Preprocessing
+
+Before training, the data undergoes several preprocessing steps:
+- **Loading the dataset**: The text data is read from the `holmes.txt` file.
+- **Cleaning the text**: Special characters, emojis, and excessive whitespace are removed.
+- **Tokenization**: The text is tokenized into sequences of words, and these sequences are then transformed into numerical format.
+- **Padding sequences**: To ensure consistent input size, sequences are padded.
+
+### Model Training
+
+The model is trained on the cleaned and tokenized dataset using the following process:
+1. **Embedding layer**: Converts words into dense vectors of fixed size.
+2. **LSTM layers**: A bidirectional LSTM processes the input text sequence.
+3. **Dense layers**: The final layers output predictions for the next word in the sequence.
+
+Training uses the Adam optimizer, and the loss function is `categorical_crossentropy`.
+
+### Usage
+
+To run the model:
+1. Clone the repository or download the Jupyter notebook.
+2. Download or prepare a dataset and save it as `holmes.txt` (or any other text file).
+3. Run the notebook to preprocess the text, build the model, and train it.
+4. After training, use the model to predict the next word given a sequence of words.
+
+
+