README.md

---
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) <!-- at revision a9467ef2ef47caa6448edeabfd8e5e5ce0fa2a23 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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]
```

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## Evaluation

### Metrics

#### Binary Classification

* Dataset: `address-eval`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](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   |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 6,000 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        | label                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | float                                                          |
  | details | <ul><li>min: 14 tokens</li><li>mean: 24.55 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 24.3 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                            | sentence_1                                                                                | label            |
  |:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-----------------|
  | <code>41 /183 b số204 ngõ1 ngách48 xô viết nghệ tĩnh quận quận cầu giấy hà nội</code> | <code>41 /183 b số204 ngõ1 ngách48 xô viết nghệ tĩnh quận quận cầu giấy hà nội</code>     | <code>1.0</code> |
  | <code>235 /121 c  số119 ngõ74 nguyễn  trãi  quận  hồàn  kiếm  tphố  nam  định</code>  | <code>235 /121 c  so119 ngo74 nguyễn  trai  quan  hồan  kiem  tphố  nam  đinh</code>      | <code>1.0</code> |
  | <code>26 /74 c số16 ngõ194 ngách106 điện biên phủ quận đống đa hưng yên</code>        | <code>195 /93 b số240 ngõ241 ngách98 phố kim mã quận hai bà trưng thành phố hà nội</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](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
<details><summary>Click to expand</summary>

- `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

</details>

### 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",
}
```

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