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") - Notebooks
- Google Colab
- Kaggle
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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We use SimCSE [here](https://arxiv.org/pdf/2104.08821.pdf)
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## Usage (Sentence-Transformers)
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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We use SimCSE [here](https://arxiv.org/pdf/2104.08821.pdf) and training the model with Thai Wikipedia [here](https://github.com/PyThaiNLP/ThaiWiki-clean/releases/tag/20210620?fbclid=IwAR1YcmZkb-xd1ibTWCJOcu98_FQ5x3ioZaGW1ME-VHy9fAQLhEr5tXTJygA)
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## Usage (Sentence-Transformers)
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