Cross Encoder Mmarco German Distilbert Base

🚀 cross-encoder-mmarco-german-distilbert-base

This is a fine - tuned cross - encoder model on the MMARCO dataset, which is useful for text ranking tasks.

🚀 Quick Start

This model is a fine - tuned [cross - encoder](https://www.sbert.net/examples/training/cross - encoder/README.html) on the [MMARCO dataset](https://huggingface.co/datasets/unicamp - dl/mmarco), a machine - translated version of the MS MARCO dataset. For fine - tuning, we use [distilbert - base - multilingual - cased](https://huggingface.co/distilbert - base - multilingual - cased) as the base model.

Model input samples are tuples in the following format: either <query, positive_paragraph> assigned to 1 or <query, negative_paragraph> assigned to 0. The model was trained for 1 epoch.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic 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

📚 Documentation

Model Performance

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

📄 License

This model is released under the apache - 2.0 license.