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", device_map="auto")# 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "SparseEncoder", | |
| "__version__": { | |
| "sentence_transformers": "5.0.0", | |
| "transformers": "4.50.3", | |
| "pytorch": "2.6.0+cu124" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "dot" | |
| } |