msmarco MiniLM L6 en de v1 EU AI Act Compliance Profile

Cross-Encoder for MS MARCO - EN-DE

This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the MS Marco Passage Ranking task.

The model can be used for Information Retrieval: See SBERT.net Retrieve & Re-rank.

The training code is available in this repository, see train_script.py.

Usage with SentenceTransformers

When you have SentenceTransformers installed, you can use the model like this:

from sentence_transformers import CrossEncoder

model = CrossEncoder('model_name', max_length=512)

query = 'How many people live in Berlin?'
docs = ['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.']
pairs = [(query, doc) for doc in docs]
scores = model.predict(pairs)

Usage with Transformers

With the transformers library, you can use the model like this:

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)

Performance

The performance was evaluated on three datasets:

• TREC-DL19 EN-EN: The original TREC 2019 Deep Learning Track: Given an English query and 1000 documents (r

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Base Model