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", device_map="auto") - 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
Upload README.md
Browse files
README.md
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@@ -133,7 +133,7 @@ You can then use the model to compute embeddings like this:
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import { AutoModel, AutoTokenizer, matmul } from "@huggingface/transformers";
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// Download from the 🤗 Hub
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const model_id = "
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const tokenizer = await AutoTokenizer.from_pretrained(model_id);
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const model = await AutoModel.from_pretrained(model_id, {
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dtype: "fp32", // Options: "fp32" | "q8" | "q4"
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import { AutoModel, AutoTokenizer, matmul } from "@huggingface/transformers";
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// Download from the 🤗 Hub
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const model_id = "MongoDB/mdbr-leaf-ir";
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const tokenizer = await AutoTokenizer.from_pretrained(model_id);
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const model = await AutoModel.from_pretrained(model_id, {
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dtype: "fp32", // Options: "fp32" | "q8" | "q4"
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