Update README.md

README.md

@@ -1,3 +1,177 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- aarohanverma/simple-daily-conversations-cleaned
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- lstm
+- next-word-prediction
+- text-generation
+- auto-completion
+---
+
+# Model Card
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This repository contains an LSTM-based next word prediction model implemented in PyTorch. 
+The model utilizes advanced techniques including an extra fully connected layer with ReLU and dropout, layer normalization, label smoothing loss, gradient clipping, and learning rate scheduling to improve performance. 
+It also uses SentencePiece for subword tokenization.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+The LSTM Next Word Predictor is designed to predict the next word or subword given an input sentence. 
+The model is trained on a dataset provided in CSV format (with a 'data' column) and uses an LSTM network with many enhancements.
+
+- **Developed by:** Aarohan Verma
+- **Model type:** LSTM-based Next Word Prediction
+- **Language(s) (NLP):** English 
+- **License:** Apache-2.0
+
+### Model Sources [optional]
+
+- **Repository:** https://huggingface.co/aarohanverma/lstm-next-word-predictor
+- **Demo:** [LSTM Next Word Predictor Demo](https://huggingface.co/spaces/aarohanverma/lstm-next-word-predictor-demo)
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+This model can be directly used for next word prediction in text autocompletion.
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+The model can be fine-tuned for related tasks such as:
+- Text generation.
+- Language modeling for specific domains.
+  
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+This model is not suitable for:
+- Tasks requiring deep contextual understanding beyond next-word prediction.
+- Applications where transformer-based architectures are preferred for longer contexts.
+- Sensitive applications where data bias could lead to unintended outputs.
+  
+## Risks and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+- **Risks:** Inaccurate or unexpected predictions may occur if the input context is too complex or ambiguous.
+- **Limitations:** The model’s performance is bounded by the size and quality of the training data as well as the inherent limitations of LSTM architectures in modeling long-range dependencies.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users should be aware of the above limitations and conduct appropriate evaluations before deploying the model in production. 
+Consider further fine-tuning or additional data preprocessing if the model is applied in sensitive contexts.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+To run the model, follow these steps:
+
+1. **Training:**
+   - Ensure you have a CSV file with a column named `data` containing your training sentences.
+   - Run training with:
+     ```bash
+     python next_word_prediction.py --data_path data.csv --train
+     ```
+   - This will train the model, save a checkpoint (`best_model.pth`), and export a TorchScript version (`best_model_scripted.pt`).
+
+2. **Inference:**
+   - To predict the next word, run:
+     ```bash
+     python next_word_prediction.py --inference "Your partial sentence"
+     ```
+   - The model will output the top predicted word or subword.
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+- **Data Source:** CSV file with a column `data` containing sentences.
+- **Preprocessing:** Uses SentencePiece for subword tokenization.
+- **Dataset:** The training and validation datasets are split based on a user-defined ratio.
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+
+- **Preprocessing:** Tokenization using a SentencePiece model.
+- **Training Hyperparameters:**
+  - **Batch Size:** Configurable via `--batch_size` (default: 512)
+  - **Learning Rate:** Configurable via `--learning_rate` (default: 0.001)
+  - **Epochs:** Configurable via `--num_epochs` (default: 25)
+  - **LSTM Parameters:** Configurable number of layers, dropout, and hidden dimensions.
+  - **Label Smoothing:** Applied with a configurable factor (default: 0.1)
+  - **Optimization:** Uses Adam optimizer with weight decay and gradient clipping.
+  - **Learning Rate Scheduling:** ReduceLROnPlateau scheduler based on validation loss.
+
+#### Speeds, Sizes, Times 
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+- **Checkpoint and TorchScript models** are saved during training for later inference.
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+- **Testing Data:** Derived from the same CSV, split from the training data.
+- **Metrics:** Primary metric is the loss (with label smoothing), with qualitative evaluation based on next-word accuracy.
+- **Factors:** Evaluations may vary based on sentence length and dataset diversity.
+
+#### Summary
+
+- The model demonstrates promising performance on next word prediction tasks;
+  however, quantitative results (e.g., accuracy, loss) should be validated on your specific dataset.
+
+## Model Examination
+
+<!-- Relevant interpretability work for the model goes here -->
+
+- Interpretability techniques such as examining predicted token distributions can be applied to further understand model behavior.
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+- **Architecture:** LSTM-based network with enhancements such as an extra fully connected layer, dropout, and layer normalization.
+- **Objective:** Predict the next word/subword given a sequence of tokens.
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+## Model Card Contact
+
+For inquiries or further information, please contact:
+
+LinkedIn: https://www.linkedin.com/in/aarohanverma/
+
+Email: [email protected]