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") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.sparse_encoder.models.MLMTransformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_SpladePooling", | |
| "type": "sentence_transformers.sparse_encoder.models.SpladePooling" | |
| } | |
| ] |