Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use mrp/simcse-model-m-bert-thai-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrp/simcse-model-m-bert-thai-cased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrp/simcse-model-m-bert-thai-cased") 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 mrp/simcse-model-m-bert-thai-cased with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mrp/simcse-model-m-bert-thai-cased") model = AutoModel.from_pretrained("mrp/simcse-model-m-bert-thai-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c3593eef5d7a0d143d657ae6e72887f44a41fdd3459e1adbc4dee9107ff3de1
- Size of remote file:
- 711 MB
- SHA256:
- 9acc6b9c55479b6a12aa5cb2eb86136644c411799d5d82478d8e8a55df280d56
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