cross-encoder_mmarco-mMiniLMv2-L12-H384-v1

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license: apache-2.0 language:

• en
• ar
• zh
• nl
• fr
• de
• hi
• in
• it
• ja
• pt
• ru
• es
• vi
• multilingual datasets:
• unicamp-dl/mmarco base_model:
• nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large pipeline_tag: text-ranking library_name: sentence-transformers tags:
• transformers

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Cross-Encoder for multilingual MS Marco

This model was trained on the MMARCO dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages.

As a base model, we used the multilingual MiniLMv2 model.

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 for more details. The training code is available here: SBERT.net Training MS Marco

Usage with SentenceTransformers

The usage becomes easy when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])

Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')

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)