Sentence Similarity
sentence-transformers
Safetensors
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use ingeol/cot_half_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ingeol/cot_half_5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ingeol/cot_half_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_half_5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ingeol/cot_half_5") model = AutoModel.from_pretrained("ingeol/cot_half_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8ebf43093734a9510b6fb4e2f174864a46fae9a53486058e6b62b2eaa47b235
- Size of remote file:
- 438 MB
- SHA256:
- 1e3b7120855cf5ca257398e5b7b9ff19937894d2c43723a0621712b8e0537e15
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