Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use atrytone/MIReAD-Neuro-Contrastive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use atrytone/MIReAD-Neuro-Contrastive with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("atrytone/MIReAD-Neuro-Contrastive") 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 atrytone/MIReAD-Neuro-Contrastive with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("atrytone/MIReAD-Neuro-Contrastive") model = AutoModel.from_pretrained("atrytone/MIReAD-Neuro-Contrastive", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from atrytone/MIReAD-Neuro-Contrastive: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/atrytone/MIReAD-Neuro-Contrastive/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://atrytone/MIReAD-Neuro-Contrastive/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/atrytone/MIReAD-Neuro-Contrastive/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- dad1894a1c87fe9ce649bc55079d1930d7c59ffcdd695bb5f12357892cfc4071
- Size of remote file:
- 440 MB
- SHA256:
- a724db2f0f0d8f158d247d65e4b00d06f1b482eeedbfc1be5ce2949b5932db22
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