update

README.md

@@ -445,10 +445,12 @@ See the next section how to load the individual datasets.
 | webis-touche2020 | 32.64 | 382,545 |
 
 Notes:
-- arguana: The task of arguana is to find for a given argument (e.g. `Being vegetarian helps the environment ...`), an argument that refutes it (e.g. `Vegetarian doesn't have an impact on the environment`). Naturally, embedding models work by finding the most similar texts, hence for the given argument it would find similar arguments first that support that `vegetarian helps the environment`, which would be treated here as non-relevant. By special embedding model prompting, the model can be steered to find arguments that refute the query. This will improve the nDCG@10 score from 53.98 to 61.5.
+- arguana: The task of arguana is to find for a given argument (e.g. `Being vegetarian helps the environment ...`), an argument that refutes it (e.g. `Vegetarian doesn't have an impact on the environment`). Naturally, embedding models work by finding the most similar texts, hence for the given argument it would find similar arguments first that support that `vegetarian helps the environment`, which would be treated as non-relevant. By embedding model prompting, the model can be steered to find arguments that refute the query. This will improve the nDCG@10 score from 53.98 to 61.5.
 - climate-fever: The task is to find evidence that support or refute a claim. As with arguana, with the default mode, the model will find the evidence primarily supporting the claim. By embedding model prompting, we can tell the model to find support and contra evidence for a claim. This improves the nDCG@10 score to 38.4.
-- Quora: As the corpus consists of question, they have been encoded with the `input_type='search_query'` in order to find similar/duplicate questions.
+- Quora: As the corpus consists of questions, they have been encoded with the `input_type='search_query'` in order to find similar/duplicate questions.
 - cqadupstack: The datasets consists of several sub-datasets, where the nDCG@10 scores will be averaged in BEIR.
+- bioasq/robust04/trec-news/signal1m: For these datasets we just provide the IDs and the embeddings, but not title/text fields. See the [BEIR repository](https://github.com/beir-cellar/beir) how to obtain the respective text corpora. You can still evaluate search quality on these datasets.
+
 
 ## Loading the dataset