Sentence Similarity
sentence-transformers
PyTorch
Transformers
Korean
bert
feature-extraction
TAACO
text-embeddings-inference
Instructions to use KDHyun08/TAACO_STS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KDHyun08/TAACO_STS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KDHyun08/TAACO_STS") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use KDHyun08/TAACO_STS with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KDHyun08/TAACO_STS") model = AutoModel.from_pretrained("KDHyun08/TAACO_STS") - Notebooks
- Google Colab
- Kaggle
File size: 624 Bytes
e91a73e 752d6bc e91a73e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"_name_or_path": "KDHyun08/TAACO_STS",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"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,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.19.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 32000
}
|