ms-marco-MiniLM-L12-v2

ms-marco-MiniLM-L12-v2 (RKNN2)

This is an RKNN-compatible version of the cross-encoder/ms-marco-MiniLM-L12-v2 model. It has been optimized for Rockchip NPUs using the rk-transformers library.

<details><summary>Click to see the RKNN model details and usage examples</summary>

Model Details

• Original Model: cross-encoder/ms-marco-MiniLM-L12-v2
• Target Platform: rk3588
• rknn-toolkit2 Version: 2.3.2
• rk-transformers Version: 0.3.0

Available Model Files

Usage

Installation

Install rk-transformers with inference dependencies to use this model:

pip install rk-transformers[inference]

RK-Transformers API

from rktransformers import RKModelForSequenceClassification
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("rk-transformers/ms-marco-MiniLM-L12-v2")
model = RKModelForSequenceClassification.from_pretrained(
    "rk-transformers/ms-marco-MiniLM-L12-v2",
    platform="rk3588",
    core_mask="auto",
)

inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
outputs = model(**inputs)
logits = outputs.logits
print(logits.shape)

# Load specific optimized/quantized model file
model = RKModelForSequenceClassification.from_pretrained(
    "rk-transformers/ms-marco-MiniLM-L12-v2",
    platform="rk3588",
    file_name="rknn/model_w8a8.rknn"
)

Configuration

The full configuration for all exported RKNN models is available in the config.json file.

</details>

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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 SentenceTransformers

The usage is easy 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-MiniLM-L12-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)
# [ 9.218911  -4.0780287]

Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L12-v2')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L12-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 and the MS Marco Passage Reranking dataset.

Note: Runtime was computed on a V100 GPU.