Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
fill-mask
learned sparse
opensearch
retrieval
passage-retrieval
query-expansion
document-expansion
bag-of-words
sparse-encoder
sparse
splade
text-embeddings-inference
Instructions to use opensearch-project/opensearch-neural-sparse-encoding-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use opensearch-project/opensearch-neural-sparse-encoding-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("opensearch-project/opensearch-neural-sparse-encoding-v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use opensearch-project/opensearch-neural-sparse-encoding-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="opensearch-project/opensearch-neural-sparse-encoding-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v1") model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from opensearch-project/opensearch-neural-sparse-encoding-v1: direct link, hf CLI and curl.
- Browser
- Download file 532 MB
-
https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://opensearch-project/opensearch-neural-sparse-encoding-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1/resolve/main/pytorch_model.bin
532 MB
- Xet hash:
- 12717450aa39bfd39aebdd1e42156cfa17f4126f1262bf1e18883f59f0e5837c
- Size of remote file:
- 532 MB
- SHA256:
- a359b09196aa613e875df91fe3d6a3b94e10e0d21e840b392ff3027e36ac97af
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