Instructions to use sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco with Transformers:
# Load model directly from transformers import BERT_Cat model = BERT_Cat.from_pretrained("sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
sebastian-hofstaetter commited on
Commit ·
7ebe97d
1
Parent(s): 5ddf7ff
update model card
Browse files
README.md
CHANGED
|
@@ -10,7 +10,7 @@ datasets:
|
|
| 10 |
|
| 11 |
# Margin-MSE Trained DistilBERT-Cat (vanilla/mono/concatenated DistilBERT re-ranker)
|
| 12 |
|
| 13 |
-
We provide a retrieval trained DistilBERT-Cat model
|
| 14 |
|
| 15 |
This instance can be used to **re-rank a candidate set**. The architecure is a 6-layer DistilBERT, with an additional single linear layer at the end.
|
| 16 |
|
|
|
|
| 10 |
|
| 11 |
# Margin-MSE Trained DistilBERT-Cat (vanilla/mono/concatenated DistilBERT re-ranker)
|
| 12 |
|
| 13 |
+
We provide a retrieval trained DistilBERT-Cat model. Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
|
| 14 |
|
| 15 |
This instance can be used to **re-rank a candidate set**. The architecure is a 6-layer DistilBERT, with an additional single linear layer at the end.
|
| 16 |
|