Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
Transformers.js
English
bert
feature-extraction
text-embeddings-inference
information-retrieval
knowledge-distillation
Instructions to use MongoDB/mdbr-leaf-ir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MongoDB/mdbr-leaf-ir with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MongoDB/mdbr-leaf-ir") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use MongoDB/mdbr-leaf-ir with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MongoDB/mdbr-leaf-ir") model = AutoModel.from_pretrained("MongoDB/mdbr-leaf-ir") - Transformers.js
How to use MongoDB/mdbr-leaf-ir with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'MongoDB/mdbr-leaf-ir'); - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d6f35794b8011172199d8cfc1acf9f42f798e479dc7a4a77b1946429e9d9753
- Size of remote file:
- 30 MB
- SHA256:
- 9384675f2a40458a229027dba764dce46a6ceb90e31e0090da25be52cfc7d8c7
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