# Dense Encoder - Distilbert - Frozen Token Embeddings

This model is a distilbert-base-uncased model trained for 30 epochs (235k steps), 64 batch size with MarginMSE Loss on MS MARCO dataset.

The token embeddings were frozen.

| Dataset | Model with updated token embeddings | Model with frozen embeddings |
| --- | :---: | :---: |
| TREC-DL 19 | 70.68 | 68.60 |
| TREC-DL 20 | 67.69 | 70.21 |
| FiQA | 28.89 | 28.60 |
| Robust04 | 39.56 | 39.08 |
| TREC-COVID v2 | 69.80 | 69.84 |
| TREC-NEWS | 37.97 | 38.27 |
| Avg. 4 BEIR tasks | 44.06 | 43.95 |


{}
transformers pytorch distilbert feature-extraction endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us 

Dense Encoder - Distilbert - Frozen Token Embeddings
====================================================


This model is a distilbert-base-uncased model trained for 30 epochs (235k steps), 64 batch size with MarginMSE Loss on MS MARCO dataset.


The token embeddings were frozen.




TAGS
#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us 
