Sentence Similarity
Transformers
Safetensors
sentence-transformers
Transformers.js
English
new
feature-extraction
gte
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use pingkeest/learning2_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pingkeest/learning2_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pingkeest/learning2_model", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use pingkeest/learning2_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pingkeest/learning2_model", trust_remote_code=True) 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 pingkeest/learning2_model with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'pingkeest/learning2_model'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fab481fb6c2ffd09946485e51233292f472a361a5ed6a255a605295ca1f2429
- Size of remote file:
- 1.74 GB
- SHA256:
- fe6e4200b833d5332b7c61859d7f4ff204211b1583d732353efe1b7594176cf2
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