ml6team / cross-encoder-mmarco-german-distilbert-base

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Model's Last Updated: April 03 2025
text-ranking

Introduction of cross-encoder-mmarco-german-distilbert-base

Model Details of cross-encoder-mmarco-german-distilbert-base

cross-encoder-mmarco-german-distilbert-base

Model description:

This model is a fine-tuned cross-encoder on the MMARCO dataset which is the machine translated version of the MS MARCO dataset. As base model for the fine-tuning we use distilbert-base-multilingual-cased

Model input samples are tuples of the following format, either <query, positive_paragraph> assigned to 1 or <query, negative_paragraph> assigned to 0.

The model was trained for 1 epoch.

Model usage

The cross-encoder model can be used like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query 1', 'Paragraph 1'), ('Query 2', 'Paragraph 2')])

The model will predict scores for the pairs ('Query 1', 'Paragraph 1') and ('Query 2', 'Paragraph 2') .

For more details on the usage of the cross-encoder models have a look into the Sentence-Transformers

Model Performance:

Model evaluation was done on 2000 evaluation paragraphs of the dataset.

Accuracy F1-Score Precision Recall
89.70 86.82 86.82 93.50

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