| # Cross-Encoder for MS Marco | |
| This model uses [BERT-Tiny](https://github.com/google-research/bert), a tiny BERT model with only 2 layers, 2 attention heads and 128 dimension size. | |
| It was trained on [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. | |
| The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Information Retrieval](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications/information-retrieval) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco) | |
| ## Usage and Performance | |
| Pre-trained models can be used like this: | |
| ``` | |
| from sentence_transformers import CrossEncoder | |
| model = CrossEncoder('model_name', max_length=512) | |
| scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]) | |
| ``` | |
| In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. | |
| | Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec (BertTokenizerFast) | Docs / Sec (Python Tokenizer) | | |
| | ------------- |:-------------| -----| --- | --- | | |
| | cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000 | 780 | |
| | cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900 | 760 | |
| | cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680 | 660 | |
| | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 | 340 | |
| | *Other models* | | | | | |
| | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 | 760 | |
| | nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 | 340| | |
| | nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 | 100 | | |
| | Capreolus/electra-base-msmarco | 71.23 | | 340 | 340 | | |
| | amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | | 330 | 330 | |
| Note: Runtime was computed on a V100 GPU. A bottleneck for smaller models is the standard Python tokenizer from Huggingface in version 3. Replacing it with the fast tokenizer based on Rust, the throughput is significantly improved: | |