Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
distilbert
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-v2-distill 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-v2-distill with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("opensearch-project/opensearch-neural-sparse-encoding-v2-distill") 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-v2-distill 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-v2-distill")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v2-distill") model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-v2-distill") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b9abaca19fd2db1ea829c4f583ffee5a0ce2c1fc921f5c616dad1cf5d582966
- Size of remote file:
- 268 MB
- SHA256:
- 0ad751e73d26c21c92fa07a0cc913806b3c27884bb0a0bacae515543bde93db0
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