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 |
- Downloads last month
- 784
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.