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Commit a757b7e4 authored by EliasGatternig's avatar EliasGatternig
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readme and requirements

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models/GoogleNews-vectors-negative300.bin
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......@@ -15,4 +15,20 @@ We used the publicly available [CISI collection](https://ir.dcs.gla.ac.uk/resour
## How to run
TODO: describe how to run our experiment(s)
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To run the first make sure all the requirements are met. Simply use the command "pip install -r requirements.txt" in your terminal. This will install all the packages required to run our code. Check if all libraries are installed.
Secondly for running the "reranker-cosine.ipynb" notebook it is necessary to add the "GoogleNews-vectors-negative300.bin" word-embeddings file into the "models" -folder, they were not uploaded to the repository initially, because of the large size. However they can be downloaded [here](https://www.kaggle.com/datasets/leadbest/googlenewsvectorsnegative300).
## Files
<u>**initial-retrieval.ipynb**</u>
Creating the intial-retrieval of the 76 queries using bm-25. Will retrieve 100 documents out of 1460 documents per query and save the results in _initial_retrieval_with_bm25_scores.pkl_.
<u>**reranker-cosine.ipynb**</u>
Re-ranks the intial documents with the help of cosine similartiy and the pre-trained embeddings of _GoogleNews-vectors-negative300_. Will retrieve 50 documents out of the initial 100 per query and save the results in _reranker_embeddings_cosine_results.pkl_.
<u>**reranker-bertopic.ipynb**</u>
Will create topics for all documents and queries and re-rank the intial retrieval. Will retrieve 50 documents per query and save the results in _reranker_bertopic_results_topic_model.pkl_.
<u>**evaluation.ipynb**</u>
Takes the results of all three methods and calculates Recall@k, Precicsion@k, F1@k and nDCG@k. Will create plots for visualizing the results.
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