Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
Transformers.js
English
bert
sentence-similarity
text-embeddings-inference
information-retrieval
knowledge-distillation
Instructions to use MongoDB/mdbr-leaf-mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MongoDB/mdbr-leaf-mt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MongoDB/mdbr-leaf-mt") 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 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MongoDB/mdbr-leaf-mt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MongoDB/mdbr-leaf-mt") model = AutoModel.from_pretrained("MongoDB/mdbr-leaf-mt", device_map="auto") - Transformers.js
How to use MongoDB/mdbr-leaf-mt with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'MongoDB/mdbr-leaf-mt'); - Inference
- Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from MongoDB/mdbr-leaf-mt: direct link, hf CLI and curl.
- Browser
- Download file 220 kB
-
https://huggingface.co/MongoDB/mdbr-leaf-mt/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://MongoDB/mdbr-leaf-mt/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/MongoDB/mdbr-leaf-mt/resolve/main/onnx/model_quantized.onnx
220 kB
- Xet hash:
- bb8459e4077614543d962778285d59a0c9b8499a0e9dc435f77c1739b20d7d09
- Size of remote file:
- 220 kB
- SHA256:
- 2a3541f3f156bc420d593fe8bcde37597980f0780035e6d0fb9b6a2f949d8855
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