Feature Extraction
sentence-transformers
Safetensors
Transformers
English
sentence-similarity
text-embeddings-inference
information-retrieval
knowledge-distillation
Instructions to use MongoDB/mdbr-leaf-mt-asym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MongoDB/mdbr-leaf-mt-asym with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MongoDB/mdbr-leaf-mt-asym") 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 MongoDB/mdbr-leaf-mt-asym with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MongoDB/mdbr-leaf-mt-asym")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MongoDB/mdbr-leaf-mt-asym", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "types": { | |
| "query_0_Transformer": "sentence_transformers.models.Transformer.Transformer", | |
| "query_1_Pooling": "sentence_transformers.models.Pooling.Pooling", | |
| "query_2_Dense": "sentence_transformers.models.Dense.Dense", | |
| "document_0_Transformer": "sentence_transformers.models.Transformer.Transformer", | |
| "document_1_Pooling": "sentence_transformers.models.Pooling.Pooling" | |
| }, | |
| "structure": { | |
| "query": [ | |
| "query_0_Transformer", | |
| "query_1_Pooling", | |
| "query_2_Dense" | |
| ], | |
| "document": [ | |
| "document_0_Transformer", | |
| "document_1_Pooling" | |
| ] | |
| }, | |
| "parameters": { | |
| "default_route": "document", | |
| "allow_empty_key": true | |
| } | |
| } |