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Weak Supervision for Document Retrieval ======

This project has transitioned from: using a pretrained neural language model for document retrieval, to investigating different representations of documents and queries, to reproducing the results of [1], to investigating methods of learning from weak supervision with fewer training examples. Despite the name of this repo, the transfer learning experiments are no longer the focus of this codebase.

Usage

If the db is not available, create ./rows or ./preprocessed by running main.py wherever the db is available and loaded. Then transfer via ssh etc.

  • Create indri/in by running create_trectext.py
  • Create query_params.xml by running create_query_params_for_dataset.py
  • Run IndriBuildIndex index_params.xml to build the Indri index of the documents in in
  • Run IndriRunQuery query_params.xml -count=10 -index=PATH_TO_PROJECT/lm_ltr/indri/out -trecFormat=true > query_result to query the Indri index and save results to query_result
  • Run read_query_results.py to process the results and save them to ./indri_results.pkl
  • Run main.py with the desired arguments. To load a previously trained model, use --load_model. Training and testing results are stored using rabbit_ml (https://github.com/dmh43/rabbit-ml).

Requirements

  • See requirements.txt

Licence

GNU GPL v3

Authors

lm_ltr was written by Dany Haddad.