Sentence Similarity
sentence-transformers
PyTorch
Transformers
camembert
feature-extraction
text-embeddings-inference
Instructions to use kornwtp/simcse-model-wangchanberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/simcse-model-wangchanberta with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/simcse-model-wangchanberta") 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/simcse-model-wangchanberta with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/simcse-model-wangchanberta") model = AutoModel.from_pretrained("kornwtp/simcse-model-wangchanberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 982547f09a0ac183386887d69d4da1acd664c098ae96df18f0232ec0f5c6f93b
- Size of remote file:
- 421 MB
- SHA256:
- 83e061e7d25318b74d7854338e6aebbb23107c8e8f70b37f900755c50dfd20ad
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