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
Upload README.md
Browse files
README.md
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# Introduction
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`mdbr-leaf-mt` is a compact high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks.
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# Evaluation
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The checkpoint used to produce the scores presented in the paper [is here](https://huggingface.co/MongoDB/mdbr-leaf-mt/commit/
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# Citation
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# Content
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1. [Introduction](#introduction)
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2. [Technical Report](#technical-report)
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3. [Highlights](#highlights)
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4. [Quickstart](#quickstart)
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5. [Citation](#citation)
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# Introduction
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`mdbr-leaf-mt` is a compact high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks.
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# Evaluation
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The checkpoint used to produce the scores presented in the paper [is here](https://huggingface.co/MongoDB/mdbr-leaf-mt/commit/c342f945a6855346bd5f48d5ee8b7e39120b0ce9).
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# Citation
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