- Libraries
- sentence-transformers
How to use hilabs/tm_custom_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("hilabs/tm_custom_embedding", 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
How to use hilabs/tm_custom_embedding with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("hilabs/tm_custom_embedding", trust_remote_code=True)
model = AutoModel.from_pretrained("hilabs/tm_custom_embedding", trust_remote_code=True, device_map="auto") - Transformers.js
How to use hilabs/tm_custom_embedding with Transformers.js:
// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';
// Allocate pipeline
const pipe = await pipeline('sentence-similarity', 'hilabs/tm_custom_embedding'); - Notebooks
- Google Colab
- Kaggle
