Step-by-step instructions for model init

📋 BUOD_ Setup.md

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+# 📋 BUOD: Text Summarization Model for the Filipino Language Documentation and Initialization
+[![Model:distilBART](https://img.shields.io/badge/model-distilBART-green)](https://huggingface.co/jamesesguerra/distilbart-cnn-12-6-finetuned-1.3.1) [![Model:Bert2Bert](https://img.shields.io/badge/model-bert2bert-green)](https://huggingface.co/0xhaz/bert2bert-cnn_dailymail-fp16-finetuned-1.0.0) ![Last Updated](https://img.shields.io/badge/last%20updated%3A-031923-lightgrey)
+Authors: [James Esguerra](https://huggingface.co/jamesesguerra), [Julia Avila](), [Hazielle Bugayong](https://huggingface.co/0xhaz)
+
+
+> Foreword: This research was done in two parts, gathering the data and running transformer models,
+> namely distilBART and bert2bert. Below is the step-by-step process of the experientaton of the study:
+
+
+## 📚 Steps
+
+- 📝 **Gathering the data**
+- 🔧 **Initializing the transfomer models; fine-tuning of the models:**
+-- via Google Colab
+-- via Google Colab (Local runtime)
+-- via Jupyter Notebook
+
+
+## 📝 Gathering data
+
+An [article scraper](https://github.com/jamesesguerra/article_scraper) was used in this experimentation which can gather bodies of text from various news sites. The data gathered was used to pre-train and finetune the models in the next step. This also includes instructions on how to use the article scraper.
+
+
+## 🔧 Initialization of transformer models
+#### via Google Colab
+Two models, distilBART and bert2bert were used to compar abstractive text summarization performance. They can be found here:
+- [distilBART](https://colab.research.google.com/drive/1Lv78nHqQh2I7KaFkUzWsn_MXsyP_PP1I?authuser=3#scrollTo=moK3d7mTQ1v-)
+- [bert2bert](https://colab.research.google.com/drive/1Lv78nHqQh2I7KaFkUzWsn_MXsyP_PP1I?authuser=3#scrollTo=moK3d7mTQ1v-)
+
+
+#### via Google Colab Local Runtime
+
+##### Dependencies
+- Jupyter Notebook
+- Anaconda 
+- _Optional:_ CUDA Toolkit for Nvidia, requires an account to install 
+- Tensorflow
+
+##### Installing dependencies
+Create an anaconda environment. This can also be used for tensorflow, which links your GPU to Google colab's Local runtime:
+
+```sh
+conda create -n tf-gpu
+conda activate tf-gpu
+```
+
+##### Optional Step: GPU Utilization (if you are using an external GPU)
+
+Next, install the **CUDA toolkit**, this is the version that was used in this experiment. You may find a more compatible version for your hardware:
+```sh
+conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
+```
+Then, upgrade pip and install tensorflow:
+```sh
+pip install –upgrade pip
+pip install “tensorflow<2.11” –user
+```
+
+Now, check if tensorflow has been configured to use the GPU,
+Type in termnial:
+```sh
+python
+```
+Next, type the following to verify:
+```sh
+import tensorflow as tf
+tf.test.is_built_with_cuda()
+```
+
+If it returns `true`, you have succesfully initialized the environment with your external GPU. If not, you may follow the tutorials found here:
+
+- CUDA Toolkit Tutorial [here](https://medium.com/geekculture/install-cuda-and-cudnn-on-windows-linux-52d1501a8805)
+- Creating and Anaconda environment [step-by-step](https://stackoverflow.com/questions/51002045/how-to-make-jupyter-notebook-to-run-on-gpu)
+- Installing Tensorflow locally using [this tutorial](https://www.tensorflow.org/install/pip#windows-native_1)
+
+##### Connecting to a Google Colab Local Runtime
+To connect this on a Google Colab Local Runtime, [this tutorial](https://research.google.com/colaboratory/local-runtimes.html) was used.
+
+First, install Jupyter notebook (if you haven't) and enable server permissions:
+```sh
+pip install jupyter_http_over_ws
+jupyter serverextension enable --py jupyter_http_over_ws
+```
+Next, start and authenticate the server:
+```sh
+jupyter notebook --NotebookApp.allow_origin='https://colab.research.google.com' --port=8888  --NotebookApp.port_retries=0
+```
+You can now copy the token url and paste it on your Google Colab.
+
+#### Running the notebook using Jupyter Notebook
+##### Dependencies
+- Jupyter Notebook
+- Anaconda 
+- _Optional:_ CUDA Toolkit for Nvidia, requires an account to install 
+- Tensorflow
+
+Download the notebooks and save them in your chosen directory.
+Create an environment where you can run the notebook via Anaconda
+```sh
+conda create -n env
+conda activate env
+```
+**You may also opt to install the CUDA toolkit and tensforflow in this environment.
+Next, run the notebooks via Jupyter Notebook.
+
+```sh
+jupyter notebook
+```
+##### After you're done
+Deactivate the environment and also disable the server using the commands in your console.
+
+```sh
+conda deactivate
+```
+```sh
+jupyter serverextension disable --py jupyter_http_over_ws
+```
+## 🔗 Additional Links/ Directory
+Here are some links to resources and or references.
+
+| Name | Link |
+| ------ | ------ |
+| Ateneo Social Computing Lab | https://huggingface.co/ateneoscsl |
+
+
+