Sentence Similarity
sentence-transformers
Safetensors
Transformers.js
bert
feature-extraction
mteb
arctic
snowflake-arctic-embed
text-embeddings-inference
Instructions to use electroglyph/arctic-embed-l-tech_and_fiction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use electroglyph/arctic-embed-l-tech_and_fiction with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("electroglyph/arctic-embed-l-tech_and_fiction") 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.js
How to use electroglyph/arctic-embed-l-tech_and_fiction with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'electroglyph/arctic-embed-l-tech_and_fiction'); - Notebooks
- Google Colab
- Kaggle
Invalid JSON:Unexpected token 'N', ..." "ap": NaN,
"... is not valid JSON
| { | |
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| "task_name": "MassiveIntentClassification", | |
| "mteb_version": "2.3.5", | |
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| "ap": NaN, | |
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| "hf_subset": "en", | |
| "languages": [ | |
| "eng-Latn" | |
| ] | |
| } | |
| ] | |
| }, | |
| "evaluation_time": 18.296528339385986, | |
| "kg_co2_emissions": null | |
| } |