readme: ---
  language: 
  - de
  tags:
  - cross-encoder
  widget:
  - text: "Was sind Lamas. Das Lama (Lama glama) ist eine Art der Kamele. Es ist in den südamerikanischen Anden verbreitet und eine vom Guanako abstammende Haustierform."
    example_title: "Example Query / Paragraph"
  license: apache-2.0
  metrics:
  - Rouge-Score
  ---
  # cross-encoder-mmarco-german-distilbert-base
  
  ## Model description:
  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) which is the machine translated version of the MS MARCO dataset.
  As base model for the fine-tuning we use [distilbert-base-multilingual-cased](https://huggingface.co/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](https://www.sbert.net/)
  
  ## 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 |