BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Paper • 2104.08663 • Published • 3
How to use BeIR/sparta-msmarco-distilbert-base-v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="BeIR/sparta-msmarco-distilbert-base-v1") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("BeIR/sparta-msmarco-distilbert-base-v1")
model = AutoModel.from_pretrained("BeIR/sparta-msmarco-distilbert-base-v1")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Re-Implementation of SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval. It is the re-implementation we used for BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.
Also have a look at our BEIR repository: https://github.com/UKPLab/beir
Have a look at https://github.com/nreimers/beir-sparta for the training and inference code of this SPARTA model