Feature Extraction
Transformers.js
ONNX
sentence-transformers
bert
medical
on-device
text-embeddings-inference
Instructions to use evum/lab-marker-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use evum/lab-marker-e5-small with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'evum/lab-marker-e5-small'); - sentence-transformers
How to use evum/lab-marker-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("evum/lab-marker-e5-small") 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] - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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## Eval (held-out fixtures, precision-first)
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| model | precision | recall | threshold | margin |
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| base multilingual-e5-small | 1.0 | 0.
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| **this model (q8 ONNX)** | 1.0 |
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## Use
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Feature-extraction; prefix report labels with `query: ` and marker names with `passage: `,
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## Eval (held-out fixtures, precision-first)
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| model | precision | recall | threshold | margin |
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| base multilingual-e5-small | 1.0 | 0.833 | 0.89 | 0.02 |
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| **this model (q8 ONNX)** | 1.0 | 1.0 | 0.7 | 0 |
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## Use
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Feature-extraction; prefix report labels with `query: ` and marker names with `passage: `,
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