readme and requirements

.gitignore

+models/GoogleNews-vectors-negative300.bin
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README.md

@@ -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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requirements.txt

+bertopic
+nltk
+rank_bm25
+rank-bm25
+scipy
+tqdm
+transformers
+gensim
+matplotlib
+numpy
+pandas
+plotly
+torch
+tensorflow
+gensim
+umap-learn 
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