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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from marmalade/efficient-splade-VI-BT-large-query: direct link, hf CLI and curl.
- Browser
- Download file 112 Bytes
-
https://huggingface.co/marmalade/efficient-splade-VI-BT-large-query/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://marmalade/efficient-splade-VI-BT-large-query/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/marmalade/efficient-splade-VI-BT-large-query/resolve/main/special_tokens_map.json
112 Bytes
| {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"} |