| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:20554 |
| - loss:MultipleNegativesRankingLoss |
| base_model: AITeamVN/Vietnamese_Embedding_v2 |
| widget: |
| - source_sentence: bon |
| sentences: |
| - cây mon |
| - đổ chậu nước |
| - yên phận làm ăn |
| - source_sentence: Tua cáy chọt oóc khói doòng |
| sentences: |
| - chăn |
| - hen thở khò khè |
| - con gà xổng ra khỏi lồng |
| - source_sentence: Khảm |
| sentences: |
| - kiểm tra |
| - treo |
| - rạo rực |
| - source_sentence: khẩu hảo Bẩu |
| sentences: |
| - mẹ mắng không bằng bố sa sầm mặt |
| - cạo trọc đầu |
| - thóc chưa khô hẳn |
| - source_sentence: Các |
| sentences: |
| - mập mạp |
| - chân tay mập |
| - bắc |
| datasets: |
| - HeyDunaX/tay-vietnamese-nmt |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on AITeamVN/Vietnamese_Embedding_v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [AITeamVN/Vietnamese_Embedding_v2](https://huggingface.co/AITeamVN/Vietnamese_Embedding_v2) on the [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt) dataset. It maps sentences & paragraphs to a 1024-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:** [AITeamVN/Vietnamese_Embedding_v2](https://huggingface.co/AITeamVN/Vietnamese_Embedding_v2) <!-- at revision 18b44161e041bf1d3a333ab5144b5b7b93f914d2 --> |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt) |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'}) |
| (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## 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("HeyDunaX/Tay_Embedding") |
| # Run inference |
| sentences = [ |
| 'Các', |
| 'bắc', |
| 'chân tay mập', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 1024] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[ 1.0000, 0.3147, -0.0254], |
| # [ 0.3147, 1.0000, -0.1489], |
| # [-0.0254, -0.1489, 1.0000]]) |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### tay-vietnamese-nmt |
|
|
| * Dataset: [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt) at [2b04e13](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt/tree/2b04e139b670d8ad62693d1fbdb943940e4acc05) |
| * Size: 20,554 training samples |
| * Columns: <code>sentence1</code> and <code>sentence2</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | |
| |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 6.77 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.85 tokens</li><li>max: 17 tokens</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | |
| |:--------------------------|:------------------------| |
| | <code>me</code> | <code>bà cô</code> | |
| | <code>noọng ấc cải</code> | <code>em ngực bự</code> | |
| | <code>noọng</code> | <code>em gái</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false |
| } |
| ``` |
|
|
| ### Evaluation Dataset |
|
|
| #### tay-vietnamese-nmt |
|
|
| * Dataset: [tay-vietnamese-nmt](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt) at [2b04e13](https://huggingface.co/datasets/HeyDunaX/tay-vietnamese-nmt/tree/2b04e139b670d8ad62693d1fbdb943940e4acc05) |
| * Size: 2,295 evaluation samples |
| * Columns: <code>sentence1</code> and <code>sentence2</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | |
| |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 7.24 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.02 tokens</li><li>max: 22 tokens</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | |
| |:-------------------------------------|:--------------------------------------------| |
| | <code>Hết fiệc ác</code> | <code>làm việc khoẻ</code> | |
| | <code>slấc ác</code> | <code>giặc độc ác</code> | |
| | <code>ái chin mác rèo năm mạy</code> | <code>Muốn ăn quả thì phải trồng cây</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: epoch |
| - `gradient_accumulation_steps`: 4 |
| - `learning_rate`: 1e-05 |
| - `num_train_epochs`: 10 |
| - `warmup_ratio`: 0.1 |
| - `warmup_steps`: 0.1 |
| - `fp16`: True |
| - `load_best_model_at_end`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `do_predict`: False |
| - `eval_strategy`: epoch |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 8 |
| - `per_device_eval_batch_size`: 8 |
| - `gradient_accumulation_steps`: 4 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 1e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 10 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: 0.1 |
| - `warmup_steps`: 0.1 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `enable_jit_checkpoint`: False |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `use_cpu`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `bf16`: False |
| - `fp16`: True |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: -1 |
| - `ddp_backend`: None |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `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 |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `use_cache`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.1556 | 100 | 1.7414 | - | |
| | 0.3113 | 200 | 1.3566 | - | |
| | 0.4669 | 300 | 1.1332 | - | |
| | 0.6226 | 400 | 1.0198 | - | |
| | 0.7782 | 500 | 0.8943 | - | |
| | 0.9339 | 600 | 0.7909 | - | |
| | 1.0 | 643 | - | 0.7135 | |
| | 1.0887 | 700 | 0.7070 | - | |
| | 1.2444 | 800 | 0.6029 | - | |
| | 1.4 | 900 | 0.6095 | - | |
| | 1.5556 | 1000 | 0.5436 | - | |
| | 1.7113 | 1100 | 0.5534 | - | |
| | 1.8669 | 1200 | 0.5363 | - | |
| | 2.0 | 1286 | - | 0.5121 | |
| | 2.0218 | 1300 | 0.4886 | - | |
| | 2.1774 | 1400 | 0.3853 | - | |
| | 2.3331 | 1500 | 0.3940 | - | |
| | 2.4887 | 1600 | 0.3859 | - | |
| | 2.6444 | 1700 | 0.4035 | - | |
| | 2.8 | 1800 | 0.3686 | - | |
| | 2.9556 | 1900 | 0.3662 | - | |
| | 3.0 | 1929 | - | 0.4505 | |
| | 3.1105 | 2000 | 0.3276 | - | |
| | 3.2661 | 2100 | 0.2877 | - | |
| | 3.4218 | 2200 | 0.2991 | - | |
| | 3.5774 | 2300 | 0.2898 | - | |
| | 3.7331 | 2400 | 0.2704 | - | |
| | 3.8887 | 2500 | 0.2807 | - | |
| | 4.0 | 2572 | - | 0.4247 | |
| | 4.0436 | 2600 | 0.2879 | - | |
| | 4.1992 | 2700 | 0.2300 | - | |
| | 4.3549 | 2800 | 0.2233 | - | |
| | 4.5105 | 2900 | 0.2169 | - | |
| | 4.6661 | 3000 | 0.2273 | - | |
| | 4.8218 | 3100 | 0.2149 | - | |
| | 4.9774 | 3200 | 0.2277 | - | |
| | 5.0 | 3215 | - | 0.4163 | |
| | 5.1323 | 3300 | 0.1973 | - | |
| | 5.2879 | 3400 | 0.1856 | - | |
| | 5.4436 | 3500 | 0.1686 | - | |
| | 5.5992 | 3600 | 0.1797 | - | |
| | 5.7549 | 3700 | 0.1830 | - | |
| | 5.9105 | 3800 | 0.1701 | - | |
| | 6.0 | 3858 | - | 0.4066 | |
| | 6.0654 | 3900 | 0.1620 | - | |
| | 6.2210 | 4000 | 0.1453 | - | |
| | 6.3767 | 4100 | 0.1593 | - | |
| | 6.5323 | 4200 | 0.1481 | - | |
| | 6.6879 | 4300 | 0.1506 | - | |
| | 6.8436 | 4400 | 0.1534 | - | |
| | 6.9992 | 4500 | 0.1554 | - | |
| | 7.0 | 4501 | - | 0.3907 | |
| | 7.1541 | 4600 | 0.1284 | - | |
| | 7.3097 | 4700 | 0.1266 | - | |
| | 7.4654 | 4800 | 0.1392 | - | |
| | 7.6210 | 4900 | 0.1292 | - | |
| | 7.7767 | 5000 | 0.1309 | - | |
| | 7.9323 | 5100 | 0.1318 | - | |
| | 8.0 | 5144 | - | 0.3922 | |
| | 8.0872 | 5200 | 0.1263 | - | |
| | 8.2428 | 5300 | 0.1136 | - | |
| | 8.3984 | 5400 | 0.1161 | - | |
| | 8.5541 | 5500 | 0.1137 | - | |
| | 8.7097 | 5600 | 0.1231 | - | |
| | 8.8654 | 5700 | 0.1187 | - | |
| | 9.0 | 5787 | - | 0.3875 | |
| | 9.0202 | 5800 | 0.1182 | - | |
| | 9.1759 | 5900 | 0.1059 | - | |
| | 9.3315 | 6000 | 0.1062 | - | |
| | 9.4872 | 6100 | 0.1044 | - | |
| | 9.6428 | 6200 | 0.0992 | - | |
| | 9.7984 | 6300 | 0.1057 | - | |
| | 9.9541 | 6400 | 0.1048 | - | |
| | 10.0 | 6430 | - | 0.3878 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.2.2 |
| - Transformers: 5.0.0 |
| - PyTorch: 2.9.0+cu126 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{henderson2017efficient, |
| title={Efficient Natural Language Response Suggestion for Smart Reply}, |
| author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
| year={2017}, |
| eprint={1705.00652}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
|
|
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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