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I'm trying to run this pre-trained Cross Encoder model (MS Marco TinyBERT) not available in Transformers.js.
I've managed to convert it using the handy script, and I'm successfully running it with the "feature-extraction" task:
const pairs = [
["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."]
];
const model = await pipeline("feature-extraction", modelName);
const out = await model(pairs[0]);
console.log(Array.from(out.data)) // [-8.387903213500977, -9.811422348022461]But I'm trying to run it as a Cross Encoder model as it's intended to, like the Python example code:
from sentence_transformers import CrossEncoder
model_name = 'cross-encoder/ms-marco-TinyBERT-L-2-v2'
model = CrossEncoder(model_name, max_length=512)
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) // [ 7.1523685 -6.2870455]How can I infer a similarity score from two sentences?
PS: if there are existing models/techniques for sentence similarity I'll take it!
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