Sentence Similarity
sentence-transformers
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use hilabs/tm_custom_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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
- Xet hash:
- 6e37d6b73b3fa79276b039d7ddd196b7ba8f399dc5021359dfa97ecc291ad39b
- Size of remote file:
- 711 kB
- SHA256:
- d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66
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