carles-undergrad-thesis/indobert-crossencoder-mmarco
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Indobert Cross-Encoder
This is a Cross-Encoder model for ID that can be used for passage re-ranking. It was trained on the multilingual version of MS Marco Passage Ranking task.
The model can be used for Information Retrieval: See SBERT.net Retrieve & Re-rank.
Usage with SentenceTransformers
When you have SentenceTransformers installed, you can use the model like this:
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
query = 'How many people live in Berlin?'
docs = ['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.']
pairs = [(query, doc) for doc in docs]
scores = model.predict(pairs)Usage with Transformers
With the transformers library, you can use the model like this:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
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)