Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use NeuML/bert-tiny-prompts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/bert-tiny-prompts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/bert-tiny-prompts") 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-tiny-prompts with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NeuML/bert-tiny-prompts") model = AutoModel.from_pretrained("NeuML/bert-tiny-prompts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 394 Bytes
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library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# BERT Tiny Prompts
This model is a copy of [stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) with prompts added for testing. It's intended for use with unit tests.
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