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clarin-knext/herbert-base-reranker-msmarco
herbert-base-reranker-msmarco is a text classification model from clarin-knext. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-sa-4.0.
Part of BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language.
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From the Hugging Face model README
Part of BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
How to use:
With sentence transformers:
from sentence_transformers import CrossEncoder
model_path = "clarin-knext/herbert-base-reranker-msmarco"
model = CrossEncoder(model_path, max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
With transformers:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_path = "clarin-knext/herbert-base-reranker-msmarco"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
features = tokenizer(['Jakie miasto jest stolica Polski?', 'Stolicą Polski jest Warszawa.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)