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.json from mrp/simcse-model-m-bert-thai-cased: direct link, hf CLI and curl.
- Browser
- Download file 802 Bytes
-
https://huggingface.co/mrp/simcse-model-m-bert-thai-cased/resolve/b260ec7e03d006c96e5537f8ebe634bc70205321/config.json
- Command line
-
hf download hf://mrp/simcse-model-m-bert-thai-cased@b260ec7e03d006c96e5537f8ebe634bc70205321/config.json
-
curl -L -o config.json https://huggingface.co/mrp/simcse-model-m-bert-thai-cased/resolve/b260ec7e03d006c96e5537f8ebe634bc70205321/config.json
802 Bytes
| { | |
| "_name_or_path": "bert-base-multilingual-cased", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "directionality": "bidi", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.6.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 119547 | |
| } | |