README.md

metadata

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 model finetuned from AITeamVN/Vietnamese_Embedding_v2 on the 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
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • tay-vietnamese-nmt

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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 at 2b04e13
• Size: 20,554 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 3 tokens mean: 6.77 tokens max: 21 tokens	min: 3 tokens mean: 5.85 tokens max: 17 tokens

• Samples:

  sentence1	sentence2
  me	bà cô
  noọng ấc cải	em ngực bự
  noọng	em gái

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Evaluation Dataset

tay-vietnamese-nmt

• Dataset: tay-vietnamese-nmt at 2b04e13
• Size: 2,295 evaluation samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 3 tokens mean: 7.24 tokens max: 26 tokens	min: 3 tokens mean: 6.02 tokens max: 22 tokens

• 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 with these parameters:

  {
      "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

@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

@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}
}