MiniLM-L-6-rerank-m3

Model Card for Model ID

This model is finetuned starting from the well-known ms-marco-MiniLM-L-6-v2 using KL distillation techniques as described here, using bge-reranker-v2-m3 as teacher

Usage

Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("juanluisdb/MiniLM-L-6-rerank-m3")
tokenizer = AutoTokenizer.from_pretrained("juanluisdb/MiniLM-L-6-rerank-m3")
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

from sentence_transformers import CrossEncoder
model = CrossEncoder("juanluisdb/MiniLM-L-6-rerank-m3", max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])

Evaluation

BEIR (NDCG@10)

I've run tests on different BEIR datasets. Cross Encoders rerank top100 BM25 results.

* Training splits of NQ and Fever were used as part of the training data.

Comparison with ablated model trained only on MSMarco:

Datasets Used

~900k queries with 32-way triplets were used from these datasets:

• MSMarco
• TriviaQA
• Natural Questions
• FEVER