--- 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) - **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) ### 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]]) ``` ## 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: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------|:------------------------| | me | bà cô | | noọng ấc cải | em ngực bự | | noọng | em gái | * Loss: [MultipleNegativesRankingLoss](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: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------|:--------------------------------------------| | Hết fiệc ác | làm việc khoẻ | | slấc ác | giặc độc ác | | ái chin mác rèo năm mạy | Muốn ăn quả thì phải trồng cây | * Loss: [MultipleNegativesRankingLoss](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
Click to expand - `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`: {}
### 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} } ```