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
Download document_0_Transformer/model.safetensors from MongoDB/mdbr-leaf-mt-asym: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/MongoDB/mdbr-leaf-mt-asym/resolve/main/document_0_Transformer/model.safetensors
- Command line
-
hf download hf://MongoDB/mdbr-leaf-mt-asym/document_0_Transformer/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MongoDB/mdbr-leaf-mt-asym/resolve/main/document_0_Transformer/model.safetensors
1.34 GB
- Xet hash:
- 24bc787e606a2cb9c1d0fe37cb2b8436c409204bcbd91c1927fa47ec34eb76eb
- Size of remote file:
- 1.34 GB
- SHA256:
- e86b2a89f7f8933cf7bd90586cdf69d0012140e412818234b234f807e51ee574
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.