metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:333
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: keepitreal/vietnamese-sbert
widget:
  - source_sentence: Tôi Thấy Hoa Vàng Trên Cỏ Xanh
    sentences:
      - mềm mại, thoáng khí và bền đẹp
      - Nike Air Force 1 phong cách không lỗi mốt
      - >-
        Tôi Thấy Hoa Vàng Trên Cỏ Xanh thông điệp trân trọng tuổi thơ và cuộc
        sống bình dị
  - source_sentence: iPhone 16
    sentences:
      - Cà Phê Cùng Tony kết hợp giải trí và giáo dục
      - iPhone 16 Pro RAM 12GB đa nhiệm mạnh mẽ
      - Loafer Gucci size từ 38 đến 45
  - source_sentence: Áo Thun
    sentences:
      - phù hợp trong thời tiết nóng bức
      - thấm hút mồ hôi, nhẹ và thoáng khí
      - Giày chạy đường dài bền nhẹ
  - source_sentence: Son Môi MAC Matte Lipstick - Ruby Woo
    sentences:
      - >-
        bảo quản dễ dàng bằng cách lộn trái khi giặt, tránh chất tẩy mạnh và
        phơi nơi thoáng mát
      - chất son lì mịn, bám màu 6-8 giờ
      - tác phẩm kinh điển về tâm linh và triết học
  - source_sentence: LEGO City Police Station
    sentences:
      - mô hình đẹp mắt để trưng bày
      - dễ dàng phối đồ từ áo thun, sơ mi đến blazer
      - chỉ số SPF 50+ PA+++ bảo vệ tối ưu khỏi tia UV
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on keepitreal/vietnamese-sbert
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.02702702702702703
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5675675675675675
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.005405405405405406
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.056756756756756774
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.02702702702702703
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5675675675675675
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.1783581729179075
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.07062419562419564
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.07973358512714
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5405405405405406
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.054054054054054064
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5405405405405406
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.1701742309301506
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.06747104247104248
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.0782135520060237
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5405405405405406
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.054054054054054064
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5405405405405406
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.17224374024595593
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.06948734448734449
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.07938312163919391
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5405405405405406
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.054054054054054064
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5405405405405406
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.1706353981690823
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.06785714285714285
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.07606072355570134
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.02702702702702703
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5135135135135135
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.005405405405405406
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05135135135135136
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.02702702702702703
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5135135135135135
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.16481648451068456
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.06733161733161734
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.07793528025726168
            name: Cosine Map@100

SentenceTransformer based on keepitreal/vietnamese-sbert

This is a sentence-transformers model finetuned from keepitreal/vietnamese-sbert on the json dataset. 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
Maximum Sequence Length: 256 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

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': 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:

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("NghiBuine/ecommerce-search-model")
# Run inference
sentences = [
    'LEGO City Police Station',
    'mô hình đẹp mắt để trưng bày',
    'dễ dàng phối đồ từ áo thun, sơ mi đến blazer',
]
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

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.0
cosine_accuracy@3	0.0
cosine_accuracy@5	0.027
cosine_accuracy@10	0.5676
cosine_precision@1	0.0
cosine_precision@3	0.0
cosine_precision@5	0.0054
cosine_precision@10	0.0568
cosine_recall@1	0.0
cosine_recall@3	0.0
cosine_recall@5	0.027
cosine_recall@10	0.5676
cosine_ndcg@10	0.1784
cosine_mrr@10	0.0706
cosine_map@100	0.0797

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.0
cosine_accuracy@3	0.0
cosine_accuracy@5	0.0
cosine_accuracy@10	0.5405
cosine_precision@1	0.0
cosine_precision@3	0.0
cosine_precision@5	0.0
cosine_precision@10	0.0541
cosine_recall@1	0.0
cosine_recall@3	0.0
cosine_recall@5	0.0
cosine_recall@10	0.5405
cosine_ndcg@10	0.1702
cosine_mrr@10	0.0675
cosine_map@100	0.0782

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.0
cosine_accuracy@3	0.0
cosine_accuracy@5	0.0
cosine_accuracy@10	0.5405
cosine_precision@1	0.0
cosine_precision@3	0.0
cosine_precision@5	0.0
cosine_precision@10	0.0541
cosine_recall@1	0.0
cosine_recall@3	0.0
cosine_recall@5	0.0
cosine_recall@10	0.5405
cosine_ndcg@10	0.1722
cosine_mrr@10	0.0695
cosine_map@100	0.0794

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.0
cosine_accuracy@3	0.0
cosine_accuracy@5	0.0
cosine_accuracy@10	0.5405
cosine_precision@1	0.0
cosine_precision@3	0.0
cosine_precision@5	0.0
cosine_precision@10	0.0541
cosine_recall@1	0.0
cosine_recall@3	0.0
cosine_recall@5	0.0
cosine_recall@10	0.5405
cosine_ndcg@10	0.1706
cosine_mrr@10	0.0679
cosine_map@100	0.0761

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.0
cosine_accuracy@3	0.0
cosine_accuracy@5	0.027
cosine_accuracy@10	0.5135
cosine_precision@1	0.0
cosine_precision@3	0.0
cosine_precision@5	0.0054
cosine_precision@10	0.0514
cosine_recall@1	0.0
cosine_recall@3	0.0
cosine_recall@5	0.027
cosine_recall@10	0.5135
cosine_ndcg@10	0.1648
cosine_mrr@10	0.0673
cosine_map@100	0.0779

Training Details

Training Dataset

json

Dataset: json
Size: 333 training samples
Columns: positive and anchor
Approximate statistics based on the first 333 samples:
positive anchor
type string string
details
min: 4 tokens
mean: 9.73 tokens
max: 37 tokens

min: 6 tokens
mean: 13.71 tokens
max: 41 tokens

	positive	anchor
type	string	string
details	min: 4 tokens mean: 9.73 tokens max: 37 tokens	min: 6 tokens mean: 13.71 tokens max: 41 tokens

Samples:

positive	anchor
`Giày Chạy Bộ Adidas Ultraboost`	`Ultraboost đế continental chống trượt`
`Cà Phê Cùng Tony`	`Cà Phê Cùng Tony chia sẻ bài học phát triển bản thân và sống tích cực`
`Đắc Nhân Tâm`	`phát triển kỹ năng thuyết phục và giao tiếp tự nhiên`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
bf16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
learning_rate: 2e-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: 4
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: True
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: True
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
1.0	1	0.1716	0.1897	0.1450	0.1699	0.1542
2.0	3	0.179	0.171	0.1722	0.1719	0.1644
2.9091	4	0.1784	0.1702	0.1722	0.1706	0.1648

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
Sentence Transformers: 4.1.0
Transformers: 4.41.2
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 2.19.1
Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}