README.md

---
license: apache-2.0
datasets:
- sentence-transformers/msmarco
language:
- en
base_model:
- cross-encoder/ms-marco-MiniLM-L12-v2
pipeline_tag: text-ranking
library_name: sentence-transformers
tags:
- transformers
---
# Cross-Encoder for MS Marco

This model was trained on the [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 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/cross_encoder/training/ms_marco)


## Usage with SentenceTransformers

The usage is easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder

model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L2-v2')
scores = model.predict([
    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
    ("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)
# [ 8.510401 -4.860082]
```


## Usage with Transformers

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L2-v2')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L2-v2')

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")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    print(scores)
```


## Performance
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 |
| ------------- |:-------------| -----| --- | 
| **Version 2 models** | | | 
| cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | | 
| cross-encoder/ms-marco-TinyBERT-L2  | 67.43 | 30.15  | 9000
| cross-encoder/ms-marco-TinyBERT-L4  | 68.09 | 34.50  | 2900
| cross-encoder/ms-marco-TinyBERT-L6 |  69.57 | 36.13  | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | | 
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
 
 Note: Runtime was computed on a V100 GPU.