Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
bert
fill-mask
learned sparse
opensearch
retrieval
passage-retrieval
document-expansion
bag-of-words
sparse-encoder
sparse
asymmetric
inference-free
splade
text-embeddings-inference
Instructions to use seerware/opensearch-neural-sparse-encoding-doc-v2-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seerware/opensearch-neural-sparse-encoding-doc-v2-mini with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seerware/opensearch-neural-sparse-encoding-doc-v2-mini") 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 seerware/opensearch-neural-sparse-encoding-doc-v2-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="seerware/opensearch-neural-sparse-encoding-doc-v2-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("seerware/opensearch-neural-sparse-encoding-doc-v2-mini") model = AutoModelForMaskedLM.from_pretrained("seerware/opensearch-neural-sparse-encoding-doc-v2-mini") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b9607cd507fc8a5ffeff176210cc587a4900652e44ccc50b2220df926705fb9
- Size of remote file:
- 91 MB
- SHA256:
- 9907af551ccbcb8080e0c13b9210b5026f1dd7fc018bcb0dceb3e229f2dde7d7
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