文件列表:

LICENSE (1069, 2022-09-07)
RNNModel (0, 2022-09-07)
RNNModel\InferSentModel.py (9936, 2022-09-07)
RNNModel\RNN_model.ipynb (79762, 2022-09-07)
conda_install.sh (5266, 2022-09-07)
data_figs_base (0, 2022-09-07)
data_figs_base\F1-bert.png (27221, 2022-09-07)
data_figs_base\Number_of_articles_per_category.png (49322, 2022-09-07)
data_figs_base\Words_per_category.png (46668, 2022-09-07)
data_figs_base\acc-bert.png (24417, 2022-09-07)
data_figs_base\confusion_matrix_unweighted.png (410637, 2022-09-07)
data_figs_base\jenshan_hist_full.png (41867, 2022-09-07)
data_figs_base\jsdiv_full.png (638558, 2022-09-07)
data_figs_base\loss-bert.png (33954, 2022-09-07)
data_figs_new (0, 2022-09-07)
data_figs_new\F1-bert.png (24486, 2022-09-07)
data_figs_new\Number_of_articles_per_category.png (49322, 2022-09-07)
data_figs_new\Words_per_category.png (39325, 2022-09-07)
data_figs_new\acc-bert.png (23215, 2022-09-07)
data_figs_new\loss-bert.png (27259, 2022-09-07)
helper.py (7337, 2022-09-07)
main_base.py (17246, 2022-09-07)
main_new.py (14679, 2022-09-07)
model.py (2631, 2022-09-07)
overlap_algo.py (5244, 2022-09-07)
preprocessing.py (2270, 2022-09-07)
requirements.txt (161, 2022-09-07)
slides_final.pdf (2194809, 2022-09-07)
tSNE_plots (0, 2022-09-07)
tSNE_plots\ARTS & CULTURE.png (97162, 2022-09-07)
tSNE_plots\BLACK VOICES.png (109844, 2022-09-07)
tSNE_plots\BUSINESS.png (130398, 2022-09-07)
tSNE_plots\COLLEGE.png (71567, 2022-09-07)
... ...

# Attentive-neural-networks-for-news-classification * The goal of this this project is to build a news classification neural network model which can classify a hierarchial news dataset. Along with this, we propose a novel algorithm to detect class overlap in news categories, which can be learnt from computation on our model's intermediate stages. ## Requirments to run : Inside the created Python environment (conda, venv, py-env etc.), run $ pip install -r requirements.txt ## GPU requirements : * A GPU (CUDA enabled) is required to run the project. Average running time for main model on a single 12 GB GPU for us was ~7 hours. * We provide a detailed method to setup PyTorch and Torchvision inside a Conda environment with CUDA support. * Follow these steps: + Use the script [`conda_install.sh`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/conda_install.sh) + Make it executable with `chmod +x ./install_conda.sh` + Run with `./install_conda.sh` * The installation creates the following two directories in the install location: + `conda`: Contains the miniconda installation + `conda_pkgs`: Contains the cache for downloaded and decompressed packages * Creating the first environment creates an additional directory in the install location: + `conda_envs`: Contains the created environment(s) * In `conda/bin/` run `./conda init` * Now run `conda create --name pytorch torchvision cudatoolkit=10.1 --channel pytorch` (this is our specific version) * Run `conda activate ` to activate the environment * Run `conda deactivate` to deactivate the current environment * Install missing requirements mentioned abobe with `pip install >= ` ## Dataset * [`preprocessing.py`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/preprocessing.py) generates the required data files. * Base dataset with all news categories is stored in `news_data_test` in form of `.txt` files for every category. * Updated dataset with less categories is stored in `news_data_test2` in the same way. * These folders are not available in the repository anymore, to reduce storage size. They can be downloaded from [`here`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/https://drive.google.com/drive/folders/1ursXywn-R9P9t-DU3SMy6s359kZsGOSN?usp=sharing) on request. ## Training and validating the model: * To train the base model on all news classes , run [`main_base.py`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/main_base.py). If there is no saved model, it will train and automatically save the model. * To train the base model on reduced news classes , run [`main_new.py`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/main_new.py). If there is no saved model, it will train and automatically save the model. In the next run, it will automatically load the saved model instead of retraining. * Training and validation metrics are saved in [`values_base.txt`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/values_base.txt) for the base model. * Training and and validation metrics are saved in [`values_new.txt`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/values_new.txt) for the new model. * Plots for the base model are in [`data_figs_base`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/data_figs_bas). * Plots for the updated model are in [`data_figs_new`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/data_figs_new). ## Visualizing the similarity algorithm: * Run [`overlap_algo.py`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/overlap_algo.py) to visualize the class overlap on the base dataset. tSNE values are generated from `main_base.py` and will be stored in `tSNE_values.csv`. The results are available [`here`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/https://drive.google.com/drive/folders/1ursXywn-R9P9t-DU3SMy6s359kZsGOSN?usp=sharing) * Thresholds can be changed in the file to adapt the algorithm. ## Other files: * [`model.py`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/model.py) contains our transformer model defintion. * [`helper.py`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/helper.py) contains utility methods for dataset loading for the model, evaluation metric methods etc. ## Presentation slides: * [`slides_final`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/slides_final.pdf) ## Written thesis: * [`thesis.pdf`](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/thesis.pdf) ## Note : * To change the dataset from base to updated, you need to change the dataset source in `helper.py` from `news_data_test` to `news_data_test2` and vice-versa. * We have the preliminary implementation of our RNN model in `RNNMODEL`. This requires some pre-requisites to be installed which are not `Pypi` standard. To install those, we recommend you visit the original source [InferSent](https://github.com/Siddhant-Ray/Attentive-neural-networks-for-news-classification/blob/master/https://github.com/facebookresearch/InferSent)

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