Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use kornwtp/mixsp-diffaug-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/mixsp-diffaug-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/mixsp-diffaug-bert-base") 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 kornwtp/mixsp-diffaug-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/mixsp-diffaug-bert-base") model = AutoModel.from_pretrained("kornwtp/mixsp-diffaug-bert-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c25e6a3f3846065c020093118e59eb4eeffe1bcdc1c93bfb2e34d80afb383972
- Size of remote file:
- 7.3 kB
- SHA256:
- 1d3e3e934ba238bf84a18316809265e522c745a00341d85ebaa836f457f1124a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.