Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use NeuML/hgbert-small-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/hgbert-small-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/hgbert-small-embeddings") 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 NeuML/hgbert-small-embeddings with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NeuML/hgbert-small-embeddings") model = AutoModel.from_pretrained("NeuML/hgbert-small-embeddings", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 284 Bytes
e1104ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"__version__": {
"pytorch": "2.13.0+cu130",
"sentence_transformers": "5.5.0",
"transformers": "5.13.1"
},
"default_prompt_name": null,
"model_type": "SentenceTransformer",
"prompts": {
"document": "",
"query": ""
},
"similarity_fn_name": "cosine"
} |