--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6000 - loss:CosineSimilarityLoss base_model: keepitreal/vietnamese-sbert widget: - source_sentence: 64 /161 c số92 phường linh trung quận quận tân bình long an sentences: - 179 /108 a số53 đường nguyễn văn cừ phường quận thanh xuân hà nội - 184 /22 c số116 ngõ196 điện biên phủ quận đống đa hải phòng - 64 /161 c số92 phường linh trung quận quận tân bình long an - source_sentence: 164 /222 c, số291 kim, mã, quận, long, biên, hải, phòng sentences: - 282 /223 b số41 ngõ39 đường kim mã quận hồàn kiếm hải phòng - 164 /222 c, số291 kim, mã, quận, long, biên, hải, phòng - 136 /25 c. số43 hem108 đuong. phường. bengõ nghe. quangõ 3 vũng. tàu - source_sentence: 168 /127 a số53 nguyễn trãi phố quận đống đa nam định sentences: - 49 /137 b. số34 ngõ123 ngách296 kim. mã. quậngõ đống. đấp nam. định - 14 /121 a so8 ngõ116 kim ma quận quan thanh xuân hai phòng - 41 /281 a số181 ngõ244 kim mã phố quận hai bà trưng tphố thái bình - source_sentence: 287 /179 a số104 phan văn trị quận long biên bắc ninh sentences: - 205 /161 a số117 kim mã quận quận hai bà trưng nam định - 295 /231 a, số125 ngõ284 nguyễn, trãi, quận, thanh, xuân, hải, phòng - 232 /206 c, so157 ngo223 ngach63 phồ, giai, phồng, quan, cau, giay, tphố, hung, yen - source_sentence: 2 71 /299 c. số212 phố. trầngõ hưng. đạo. quậngõ hồàngõ kiếm. hải. phòng sentences: - 214 /194 a, số20 đường, nguyễn, trãi, quận, cầu, giấy, thái, bình - 164 /123 c. số213 kim. mã. phố. quậngõ thanhuyện xuângõ bắc. ninh - 130 /185 a so63 ngo115 ngach279 le loi quan hai ba trung tphố ha noi pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer based on keepitreal/vietnamese-sbert results: - task: type: binary-classification name: Binary Classification dataset: name: address eval type: address-eval metrics: - type: cosine_accuracy value: 0.998 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.6475284695625305 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.998 name: Cosine F1 - type: cosine_f1_threshold value: 0.6475284695625305 name: Cosine F1 Threshold - type: cosine_precision value: 0.998 name: Cosine Precision - type: cosine_recall value: 0.998 name: Cosine Recall - type: cosine_ap value: 0.999976118968095 name: Cosine Ap - type: cosine_mcc value: 0.996 name: Cosine Mcc --- # SentenceTransformer based on keepitreal/vietnamese-sbert This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Kao1412/Classification_Address_New") # Run inference sentences = [ '2 71 /299 c. số212 phố. trầngõ hưng. đạo. quậngõ hồàngõ kiếm. hải. phòng', '164 /123 c. số213 kim. mã. phố. quậngõ thanhuyện xuângõ bắc. ninh', '214 /194 a, số20 đường, nguyễn, trãi, quận, cầu, giấy, thái, bình', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `address-eval` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:--------| | cosine_accuracy | 0.998 | | cosine_accuracy_threshold | 0.6475 | | cosine_f1 | 0.998 | | cosine_f1_threshold | 0.6475 | | cosine_precision | 0.998 | | cosine_recall | 0.998 | | **cosine_ap** | **1.0** | | cosine_mcc | 0.996 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 6,000 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-----------------| | 41 /183 b số204 ngõ1 ngách48 xô viết nghệ tĩnh quận quận cầu giấy hà nội | 41 /183 b số204 ngõ1 ngách48 xô viết nghệ tĩnh quận quận cầu giấy hà nội | 1.0 | | 235 /121 c số119 ngõ74 nguyễn trãi quận hồàn kiếm tphố nam định | 235 /121 c so119 ngo74 nguyễn trai quan hồan kiem tphố nam đinh | 1.0 | | 26 /74 c số16 ngõ194 ngách106 điện biên phủ quận đống đa hưng yên | 195 /93 b số240 ngõ241 ngách98 phố kim mã quận hai bà trưng thành phố hà nội | 0.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 5 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | address-eval_cosine_ap | |:------:|:----:|:-------------:|:----------------------:| | 1.0 | 188 | - | 0.9999 | | 2.0 | 376 | - | 0.9999 | | 2.6596 | 500 | 0.0231 | 0.9999 | | 3.0 | 564 | - | 1.0000 | ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.52.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.7.0 - Datasets: 2.14.4 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```