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:
# pip install -U transformers accelerate # 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
Download config_sentence_transformers.json from mrp/simcse-model-m-bert-thai-cased: direct link, hf CLI and curl.
- Browser
- Download file 122 Bytes
-
https://huggingface.co/mrp/simcse-model-m-bert-thai-cased/resolve/4b5df5adfad92c79d66180d25c308bbb2203a780/config_sentence_transformers.json
- Command line
-
hf download hf://mrp/simcse-model-m-bert-thai-cased@4b5df5adfad92c79d66180d25c308bbb2203a780/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/mrp/simcse-model-m-bert-thai-cased/resolve/4b5df5adfad92c79d66180d25c308bbb2203a780/config_sentence_transformers.json
122 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "2.0.0", | |
| "transformers": "4.6.1", | |
| "pytorch": "1.9.0+cu111" | |
| } | |
| } |