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---
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license: apache-2.0
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datasets:
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- sentence-transformers/msmarco
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language:
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- en
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base_model:
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- google/electra-base-discriminator
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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tags:
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- transformers
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---
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# Cross-Encoder for MS Marco
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This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
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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 Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) 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)
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## Usage with SentenceTransformers
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The usage is easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then you can use the pre-trained models like this:
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```python
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from sentence_transformers import CrossEncoder
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model = CrossEncoder('cross-encoder/ms-marco-electra-base')
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scores = model.predict([
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("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
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("How many people live in Berlin?", "Berlin is well known for its museums."),
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])
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print(scores)
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# [9.9227107e-01 2.0136760e-05]
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```
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## Usage with Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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model = AutoModelForSequenceClassification.from_pretrained('model_name')
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tokenizer = AutoTokenizer.from_pretrained('model_name')
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features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
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model.eval()
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with torch.no_grad():
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scores = model(**features).logits
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print(scores)
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```
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## Performance
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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.
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| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
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| ------------- |:-------------| -----| --- |
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| **Version 2 models** | | |
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| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
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| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
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| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
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| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
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| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
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| **Version 1 models** | | |
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| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
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| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
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| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
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| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
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| **Other models** | | |
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| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
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| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
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| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
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| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
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| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
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| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
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Note: Runtime was computed on a V100 GPU. |
