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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MongoDB/mdbr-leaf-mt") model = AutoModel.from_pretrained("MongoDB/mdbr-leaf-mt") - 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
- Xet hash:
- cfc1904ac35e424df8a8944924e4adf969cad44f2c364939f472af1e6ea0d196
- Size of remote file:
- 91.8 MB
- SHA256:
- d23ff906eeec6861adf43f7771238323dfcd32e95ce2b9d7cce432054714eded
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