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scdv's Introduction

SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

Train Model

To train a model, run the following command:

python train.py \
    --data path_to_filename \
    --init_vector_type initial_vector_type \
    --vector_size dimension_of_vector \
    --num_clusters number_of_clusters \
    --save_model path_to_save_model \
    --log path_to_save_logging

Load Model

from scdv import SCDV
model = SCDV.load('model_path.pkl')

Run IR Task

To run the information retrieval task, run the following command

python src/ir.py \
    --model path_to_model \
    --query path_to_query_file \
    --documents path_to_documents_folder \
    --output path_to_output_folder \
    --lm_ngram ngram_size_for_lm_score \
    --lambda_ lmabda_for_pv_score \
    --top k_top_results_to_return \
    --log path_to_save_logging

Acknowledgements

This project was made as a part of the Algorithms for Intelligence Web and Information Retrieval course (UE19CS332) at PES University.

Yashi Chawla
Suhas Thalanki
Vinay V
Anurita Bose

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