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
- Xet hash:
- 01471f9060ae70c0b17eb1bb5f9c7aa13967e25f204b767a9a45883aeb28c556
- Size of remote file:
- 369 kB
- SHA256:
- 2f2ee2b621c9d78d2ef9cb53ca010a2d99ecbffca867c759b744d22471b7eaf6
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