Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:6500
loss:CosineSimilarityLoss
Eval Results (legacy)
Instructions to use Kao1412/Classification_Address with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Kao1412/Classification_Address with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Kao1412/Classification_Address") sentences = [ "64 đường tố hữu đông anh hải phòng", "64 đường tố hữu đông anh hải phòng", "80 mễ trì phú nhuận tp cà mau", "81 phùng khồang phường quận 6 đà nẵng" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "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-05, | |
| "max_position_embeddings": 258, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "tokenizer_class": "PhobertTokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.3", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 64001 | |
| } | |