 [BEIR: Benchmarking IR](https://github.com/beir-cellar/beir)  We report the following BEIR scores as measured in normalized discounted cumulative gain (nDCG@10):  |   Model       | Avg   | MSMARCO | TREC-Covid | NFCorpus | NaturalQuestions | HotpotQA | FiQA | ArguAna | Tóuche-2020 | Quora | CQAdupstack | DBPedia | Scidocs | Fever | Climate-fever | Scifact  | |---------------|-------|---------|------------|----------|------------------|----------|------|---------|-------------|-------|-------------|---------|---------|-------|---------------|----------| | Contriever*   | 35.97 |    20.6 |       27.4 |     31.7 |             25.4 |     48.1 | 24.5 |    37.9 |        19.3 |  83.5 |       28.40 |    29.2 |    14.9 | 68.20 |          15.5 |    64.9  | | Carptriever-1 | 34.54 |   18.83 |   **52.2** |     28.5 |             21.1 |     39.4 | 23.2 |    31.7 |        15.2 |  81.3 |       26.88 |    25.4 |    14.2 | 57.36 |      **17.9** |    64.9  |  \* Results are taken from the Contriever [GitHub repository](https://github.com/facebookresearch/contriever).  Note that degradation in performance, relative to the Contriever model, was expected given the much broader diversity of our training dataset. We plan on addressing this in future updates with architectural improvements and view Carptriever-1 as our first iteration in the exploratory phase towards better language-embedding models.   