cross-encoder/ms-marco-TinyBERT-L6
39.8k
Cross-Encoder for MS Marco
This model was trained on the MS Marco 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 for more details. The training code is available here: SBERT.net Training MS Marco
Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-TinyBERT-L6')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-TinyBERT-L6')
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)Usage with SentenceTransformers
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-TinyBERT-L6', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.