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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MongoDB/mdbr-leaf-mt-asym", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 771 Bytes
01aaa98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"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
}
} |