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
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "additional_special_tokens": ["<s>NOTUSED", "</s>NOTUSED", "<_>"], "special_tokens_map_file": null, "name_or_path": "airesearch/wangchanberta-base-att-spm-uncased", "sp_model_kwargs": {}, "tokenizer_class": "CamembertTokenizer"} |