Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use crisistransformers/CT-M1-Complete-SE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use crisistransformers/CT-M1-Complete-SE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("crisistransformers/CT-M1-Complete-SE") 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 crisistransformers/CT-M1-Complete-SE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("crisistransformers/CT-M1-Complete-SE") model = AutoModel.from_pretrained("crisistransformers/CT-M1-Complete-SE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2 opened 7 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#1 opened over 1 year ago
by
SFconvertbot