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NghiBuine

Add new SentenceTransformer model

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:972
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: keepitreal/vietnamese-sbert
widget:
  - source_sentence: Giày Boot Nữ Dr. Martens 1460
    sentences:
      - Boot nữ đế chống nước và dầu
      - chống nắng an toàn cho da nhạy cảm không gây kích ứng
      - sản phẩm đi kèm hộp, dây sạc Type-C và sách hướng dẫn chi tiết
  - source_sentence: Giày Sneaker Nike Air Force 1
    sentences:
      - quần tây nam vải co giãn nhẹ, linh hoạt khi di chuyển
      - quần short thể thao nữ vải mát lạnh, co giãn cực tốt
      - Giày sneaker Air Force 1 logo Swoosh
  - source_sentence: Giày Loafer Da Bóng Nam Cổ Điển
    sentences:
      - đế cao su chống trượt giúp di chuyển an toàn
      - Sony WH-1000XM5 hỗ trợ Hi-Res Audio Wireless
      - >-
        màn hình cảm ứng 13.4 inch tỉ lệ 16:10, độ phân giải 4K UHD+ sắc nét,
        màu sống động, góc nhìn rộng
  - source_sentence: Giày Cao Gót Christian Louboutin
    sentences:
      - rửa mặt bằng sữa Some By Mi giúp kiểm soát dầu hiệu quả
      - quần tây nữ xếp ly phía trước tạo hiệu ứng dáng cao
      - Giày Louboutin lót mềm thoải mái
  - source_sentence: Áo Sơ Mi Nữ Tay Dài Họa Tiết Caro
    sentences:
      - áo sơ mi nữ tay dài họa tiết caro nhỏ cổ điển trẻ trung
      - khuy cài phía trước được thiết kế ẩn tinh tế
      - các thiết kế như cạp cao tôn dáng, gấu tua rua, chi tiết rách nhẹ
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: 0.009259259259259259
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.027777777777777776
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.2777777777777778
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.0030864197530864196
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.005555555555555557
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.027777777777777783
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.009259259259259259
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.027777777777777776
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.2777777777777778
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.09190266762601638
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.03934817754262198
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.049514242466381884
            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: 0.009259259259259259
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.009259259259259259
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.25925925925925924
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.0030864197530864196
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.001851851851851852
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.02592592592592593
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.009259259259259259
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.009259259259259259
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.25925925925925924
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.08541654381551299
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.03630217519106408
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.04747912217641579
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.009259259259259259
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.018518518518518517
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.018518518518518517
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.26851851851851855
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.009259259259259259
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.006172839506172839
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.003703703703703704
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.026851851851851856
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.009259259259259259
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.018518518518518517
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.018518518518518517
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.26851851851851855
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.09499080067258153
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.046222810111699
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.05635122599163806
            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: 0.009259259259259259
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.24074074074074073
            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: 0.001851851851851852
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.024074074074074074
            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: 0.009259259259259259
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.24074074074074073
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.07767536292853462
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.03192239858906525
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.042847271207784025
            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
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.2222222222222222
            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: 0.022222222222222223
            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: 0.2222222222222222
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.0710622436721403
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.028813932980599647
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.039492961403908525
            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/search-ecommerce-product-model")
# Run inference
sentences = [
    'Áo Sơ Mi Nữ Tay Dài Họa Tiết Caro',
    'áo sơ mi nữ tay dài họa tiết caro nhỏ cổ điển trẻ trung',
    'các thiết kế như cạp cao tôn dáng, gấu tua rua, chi tiết rách nhẹ',
]
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	0.0093
cosine_accuracy@5	0.0278
cosine_accuracy@10	0.2778
cosine_precision@1	0.0
cosine_precision@3	0.0031
cosine_precision@5	0.0056
cosine_precision@10	0.0278
cosine_recall@1	0.0
cosine_recall@3	0.0093
cosine_recall@5	0.0278
cosine_recall@10	0.2778
cosine_ndcg@10	0.0919
cosine_mrr@10	0.0393
cosine_map@100	0.0495

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.0
cosine_accuracy@3	0.0093
cosine_accuracy@5	0.0093
cosine_accuracy@10	0.2593
cosine_precision@1	0.0
cosine_precision@3	0.0031
cosine_precision@5	0.0019
cosine_precision@10	0.0259
cosine_recall@1	0.0
cosine_recall@3	0.0093
cosine_recall@5	0.0093
cosine_recall@10	0.2593
cosine_ndcg@10	0.0854
cosine_mrr@10	0.0363
cosine_map@100	0.0475

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.0093
cosine_accuracy@3	0.0185
cosine_accuracy@5	0.0185
cosine_accuracy@10	0.2685
cosine_precision@1	0.0093
cosine_precision@3	0.0062
cosine_precision@5	0.0037
cosine_precision@10	0.0269
cosine_recall@1	0.0093
cosine_recall@3	0.0185
cosine_recall@5	0.0185
cosine_recall@10	0.2685
cosine_ndcg@10	0.095
cosine_mrr@10	0.0462
cosine_map@100	0.0564

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	0.0093
cosine_accuracy@10	0.2407
cosine_precision@1	0.0
cosine_precision@3	0.0
cosine_precision@5	0.0019
cosine_precision@10	0.0241
cosine_recall@1	0.0
cosine_recall@3	0.0
cosine_recall@5	0.0093
cosine_recall@10	0.2407
cosine_ndcg@10	0.0777
cosine_mrr@10	0.0319
cosine_map@100	0.0428

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	0.0
cosine_accuracy@10	0.2222
cosine_precision@1	0.0
cosine_precision@3	0.0
cosine_precision@5	0.0
cosine_precision@10	0.0222
cosine_recall@1	0.0
cosine_recall@3	0.0
cosine_recall@5	0.0
cosine_recall@10	0.2222
cosine_ndcg@10	0.0711
cosine_mrr@10	0.0288
cosine_map@100	0.0395

Training Details

Training Dataset

json

Dataset: json
Size: 972 training samples
Columns: positive and anchor
Approximate statistics based on the first 972 samples:
positive anchor
type string string
details
min: 4 tokens
mean: 11.73 tokens
max: 37 tokens

min: 6 tokens
mean: 15.29 tokens
max: 41 tokens

	positive	anchor
type	string	string
details	min: 4 tokens mean: 11.73 tokens max: 37 tokens	min: 6 tokens mean: 15.29 tokens max: 41 tokens

Samples:

positive	anchor
`Giày Thể Thao Nữ Chunky Sneaker Hồng Pastel`	`đế cao 4.5cm giúp hack dáng tăng chiều cao vẫn thoải mái`
`Bộ Xếp Hình Gỗ 3D Động Vật Rừng`	`rèn luyện kỹ năng nhận dạng hình khối và phát triển khả năng quan sát`
`Rubik Mirror Cube Biến Hình 3x3`	`vỏ Rubik mạ gương vàng bóng nổi bật và sang trọng`

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
0.5161	1	0.0934	0.0877	0.09	0.0803	0.0773
1.5484	3	0.0905	0.0836	0.0923	0.0782	0.0708
2.0645	4	0.0919	0.0854	0.0950	0.0777	0.0711

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}
}