Fill-Mask
Transformers
PyTorch
English
bert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
Instructions to use marmalade/efficient-splade-VI-BT-large-query with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marmalade/efficient-splade-VI-BT-large-query with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="marmalade/efficient-splade-VI-BT-large-query")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("marmalade/efficient-splade-VI-BT-large-query") model = AutoModelForMaskedLM.from_pretrained("marmalade/efficient-splade-VI-BT-large-query") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1bcc53cdc5ccb17925bbc0cd85a8f907a2080ae041267c9a5cc58ee681fc929
- Size of remote file:
- 17.7 MB
- SHA256:
- 7d8415d9ac4d1d5ef92f005cbb5d39762fcd3e62cc83d946fb2adf4181955a8c
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