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 tokenizer_config.json from marmalade/efficient-splade-VI-BT-large-query: direct link, hf CLI and curl.
- Browser
- Download file 417 Bytes
-
https://huggingface.co/marmalade/efficient-splade-VI-BT-large-query/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://marmalade/efficient-splade-VI-BT-large-query/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/marmalade/efficient-splade-VI-BT-large-query/resolve/main/tokenizer_config.json
417 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "never_split": null, "special_tokens_map_file": null, "name_or_path": "/tmp-network/user/classanc/CoCodenser/flops_mlm_together/10_epochs/tinybert_256_128_0.001/", "tokenizer_class": "BertTokenizer"} |