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Model: ml6team/cross-encoder-mmarco-german-distilbert-base
Source: Original Platform

README.md

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+---
+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
+library_name: sentence-transformers
+pipeline_tag: text-ranking
+---
+# 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 |