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
| from transformers.models.bert.configuration_bert import BertConfig | |
| class BertHashConfig(BertConfig): | |
| """ | |
| Extension of Bert configuration to add projections parameter. | |
| """ | |
| model_type = "bert_hash" | |
| def __init__(self, projections=5, **kwargs): | |
| super().__init__(**kwargs) | |
| self.projections = projections | |