ColBERT XM 🛠️ Usage 📊 Evaluation 🤖 Training 🔗 Citation 💻 Code 📄 Paper This is a ColBERT model that can be used for semantic search in many languages. It encodes queries and passages into matrices of token level embeddings and efficiently finds passages that contextually match the query using scalable vector similarity (MaxSim) operators. The model uses an XMOD backbone, which allows it to learn from monolingual fine tuning in a high resource language, like English, and perform zero shot retrieval across multiple languages. Usage Start by installing the colbert ai and some extra requirements: Then, you can use the model like this: Evaluation mMARCO : We evaluate our model on the small development sets of mMARCO, which consists of 6,980 queries for a corpus of 8.8M candidate passages in 14 languages. Below, we compared its multilingual performance with other retrieval models on the dataset official metrics, i.e., mean reciprocal rank at cut off 10 (MRR@10). model Type Samples Params en es fr it pt id de ru zh ja nl vi hi ar Avg. : : : : : : : : : : : : : : : : : : : : : : 1 BM25 (Pyserini) lexical 18.4 15.8 15.5 15.3 15.2 14.9 13.6 12.4 11.6 14.1 14.0 13.6 13.4 11.1 14.2 2 mono…
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