Instructions to use BeIR/sparta-msmarco-distilbert-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
SPARTA
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