Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert_hash
feature-extraction
custom_code
Instructions to use NeuML/bert-hash-pico-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/bert-hash-pico-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/bert-hash-pico-embeddings", 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 NeuML/bert-hash-pico-embeddings with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeuML/bert-hash-pico-embeddings", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 283 Bytes
f0bd247 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.1.2",
"transformers": "4.57.3",
"pytorch": "2.9.0+cu128"
},
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |