Feature Extraction
Transformers
Safetensors
English
bert
fill-mask
splade
sparse-retrieval
information-retrieval
beir
code-search
static-query
distilled
text-embeddings-inference
Instructions to use Akshat131/splade-multi-static-pruned-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akshat131/splade-multi-static-pruned-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Akshat131/splade-multi-static-pruned-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Akshat131/splade-multi-static-pruned-v2") model = AutoModelForMaskedLM.from_pretrained("Akshat131/splade-multi-static-pruned-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 633 Bytes
e323666 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"is_local": true,
"local_files_only": false,
"mask_token": "[MASK]",
"max_length": 128,
"model_max_length": 512,
"never_split": null,
"pad_to_multiple_of": null,
"pad_token": "[PAD]",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "[SEP]",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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