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README.md

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+# TripClick Baselines with Improved Training Data
+
+*Establishing Strong Baselines for TripClick Health Retrieval* Sebastian Hofstätter, Sophia Althammer, Mete Sertkan and Allan Hanbury
+
+https://arxiv.org/abs/
+
+**tl;dr** We create strong re-ranking and dense retrieval baselines (BERT<sub>CAT</sub>, BERT<sub>DOT</sub>, ColBERT, and TK) for TripClick (health ad-hoc retrieval). We improve the – originally too noisy – training data with a simple negative sampling policy. We achieve large gains over BM25 in the re-ranking and retrieval setting on TripClick, which were not achieved with the original baselines. We publish the improved training files for everyone to use.
+
+If you have any questions, suggestions, or want to collaborate please don't hesitate to get in contact with us via [Twitter](https://twitter.com/s_hofstaetter) or mail to s.hofstaetter@tuwien.ac.at
+
+**Please cite our work as:**
+````
+
+````
+
+## Published Training Files
+
+We publish the improved training files without the text content instead using the ids from TripClick (with permission from the TripClick owners); for the text content please get the full TripClick dataset from [the TripClick Github page](https://github.com/https://github.com/tripdatabase/tripclick). 
+
+Our training file **improved_tripclick_train_triple-ids.tsv** has the format ``query_id pos_passage_id neg_passage_id`` (with tab separation).

improved_tripclick_train_triple-ids.tsv

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