Ms Marco MiniLM L6 v2

--- license: apache-2.0 datasets: - sentence-transformers/msmarco language: - en base_model: - cross-encoder/ms-marco-MiniLM-L12-v2 pipeline_tag: text-ranking library_name: sentence-transformers tags: - transformers --- # Cross-Encoder for MS Marco This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/cross_encoder/training/ms_marco) ## Usage with SentenceTransformers The usage is easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2') scores = model.predict([ ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."), ("How many people live in Berlin?", "Berlin is well known for its museums."), ]) print(scores) # [ 8.607138 -4.320078] ``` ## Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2') tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2') features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` ## Performance In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](http...

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