Sentence Similarity
sentence-transformers
Safetensors
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use ingeol/cot_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ingeol/cot_5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ingeol/cot_5") 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 ingeol/cot_5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ingeol/cot_5") model = AutoModel.from_pretrained("ingeol/cot_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22a8f255771bfd5648b0885dc994b2b863875aec0902da3305b6efecf86fe53d
- Size of remote file:
- 438 MB
- SHA256:
- 84492d4ec9cc4ad6545d5a9052876f5502047a9a7df01468161bddee06129206
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