| 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 | |