Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
Instructions to use baseplate/splade-cocondenser-selfdistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baseplate/splade-cocondenser-selfdistil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="baseplate/splade-cocondenser-selfdistil")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("baseplate/splade-cocondenser-selfdistil") model = AutoModelForMaskedLM.from_pretrained("baseplate/splade-cocondenser-selfdistil") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 752dc47be60b1b8dd0a3897e1628a926f7343d63859266bfb66f56db57ca43ac
- Size of remote file:
- 438 MB
- SHA256:
- 0a44b51cf90c504462e69a0b84d68d15d1f4552c0d3f5483efab137495c9b9f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.