metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:11347
  - loss:MultipleNegativesRankingLoss
base_model: vinai/phobert-base
widget:
  - source_sentence: >-
      Beefsteak 123 la mot dia chi ban banh mi chao, beefsteak cuc ngon tai Can
      Tho ma ban nen mot gan ghe den. Khong gian quan rong rai, sach se, phuc vu
      nhanh nhen, gia ca hop ly. Banh mi chao duong Nguyen Van Troi noi tieng
      ban banh mi thom ngon, chat luong. Banh mi tai day chia ra lam 2 phan: co
      thit bo ma khong thit bo.


      Quan Beefsteak 123 la mot dia diem ly tuong cho nhung nguoi yeu thich thit
      bo va cac mon an ngon khac. Quan noi tieng voi su ket hop tuyet voi giua
      thit bo, pate va trung op la. Neu ban muon thu nhung mon khac, quan cung
      co san xuc xich, ca moi, cha lua va xiu mai. Menu cua quan duoc chia thanh
      tung phan da duoc ket hop san de ban de dang lua chon. Vi du nhu bo op la
      pate xuc xich hoac bo op la pate cha lua. Ban cung co the tao ra cac to
      hop rieng cua rieng minh nhu op la ca moi xiu mai.Mot dieu dac biet khi
      den quan la khi ban goi mot phan, ban se duoc tang mien phi mot dia xa
      lach tron. Day la cach hoan hao de ket hop khau vi cua ban voi cac loai
      rau song tuoi ngon.Voi khong gian thoai mai va phuc vu nhanh chong, quan
      Beefsteak 123 mang den cho ban trai nghiem am thuc doc dao va ngon mieng.
      Hay ghe tham quan de thuong thuc nhung mon an tuyet voi nay!


      THONG TIN LIEN HE:

      Dia chi: 9B Nguyen Van Troi, Phuong Xuan Khanh, Can Tho

      Dien thoai: 0907 713 458

      Gio mo cua: 06:00 - 14:00

      Gia tham khao: 20.000d - 40.000d

      Fanpage: https://www.facebook.com/Beefsteak-123-143170999350605/

       Goi dien
    sentences:
      - Beefsteak 123 - Nguyen Van Troi
      - Pho Ngon 37
      - Khong tra no hay chi tien ngay Tet
  - source_sentence: >-
      KCC - Pho & Com Ga Xoi Mam la quan an duoc nhieu nguoi yeu thich tai so 6
      Ton That Thuyet, Nam Tu Liem, Ha Noi. Noi day voi khong gian am cung, rat
      thich hop cho nhung bua an ben ban be, dong nghiep. Day la quan duoc nhieu
      thuc khach danh gia cao ca dich vu lan chat luong do an. Den voi KCC - Pho
      & Com Ga Xoi Mam ngoai pho la mon duoc yeu thich nhat ra, quan con co vo
      so cac mon an hap dan nhu: com rang dui ga xoi mam, com rang dua bo, com
      rang cai bo, pho xao bo, com nong dui ga xoi mam, mi xao bo, com nong cai
      bo, com nong dua bo. Doc va la tu nhung hat com gion rum, cung voi do la
      huong vi cua nuoc sot dac trung va bi truyen ngam sau vao tan ben trong.


      Cac mon nay tuy binh di trong cach che bien nhung mang lai huong vi am
      thuc manh me, du de lam to mo bat cu thuc khach nao khi thuong thuc. KCC -
      Pho & Com Ga Xoi Mam cam ket mang den cho nguoi tieu dung nhung san pham
      ngon an toan, co loi cho suc khoe voi gia rat hop ly. Ban dang o Ton That
      Thuyet, Ha Noi va dang ban khoan khong biet dia chi an pho nao ngon thi
      hay ghe ngay quan an KCC nhe!


      THONG TIN LIEN HE:  Dia chi:  6 Ton That Thuyet, Nam Tu Liem, Ha Noi Gio
      mo cua:  06:00 - 14:00 | 17:30 - 22:00

      Dat mua ngay
    sentences:
      - Nem Nuong Hai Anh
      - Ca basa kho thom
      - KCC - Pho & Com Ga Xoi Mam
  - source_sentence: >-
      Banh canh ca loc duoc lam tu bot gao va ca loc. Bot gao sau khi duoc can
      mong thanh soi vua an thi duoc tha vao noi nuoc luoc Ca loc go lay phan
      thit, uop chut gia vi cho dam vi. Phan xuong ca khong bi bo di ma duoc giu
      lai gia nhuyen, loc lay phan nuoc ca roi do vao phan nuoc dung. Mon banh
      canh ca loc ngon nhat la khi an con nong, vua chan vua hup vua xuyt xoa
      cai vi cay nong. Neu an trong ngay dong thi qua tuyet voi roi phai khong
      nao. Mot to banh canh ca loc chi co gia khoang 30.000 dong thoi cac ban
      nhe.
    sentences:
      - Banh canh ca loc
      - Bun oc, bun oc chan
      - Nha hang Trung Duong Marina
  - source_sentence: >-
      Nguyen lieu:Bap chuoi 1 cai Chanh 1 trai Bot chien gion 75 gr Dau an 100
      ml Nuoc mam 3 muong canh Bot ngot 1 muong ca phe Tuong ot 1 muong canh
      Duong 1 muong canh Ot bot 1 muong ca pheCach che bien:So che bap chuoi:
      Dung tay tach bap chuoi thanh nhung cong nho, sau do ngam bap chuoi vao
      trong thau nuoc chanh pha loang de giup bap chuoi khong bi tham den. Tiep
      tuc go bo nhuy trong bap chuoi roi rua sach lai voi nuoc.Nhung bot va
      chien bap chuoi: Bap chuoi sau khi tach roi va rua sach ban cho bap chuoi
      ra to, do vao 75gr bot chien gion, dao deu cho bot tham vao bap chuoi. Bac
      chao len bep cung voi 100ml dau an dun soi (luong dau ngap bap chuoi), sau
      do cho bap chuoi da ao bot vao chien tren lua vua khoang 5 - 10 phut cho
      bap chuoi chin vang deu thi vot ra de rao dau.Lam bap chuoi chien nuoc
      mam: Bac mot cai chao khac cho vao 10ml dau an (tan dung luong dau con du
      khi chien bap chuoi), roi cho vao 3 muong canh nuoc mam, 1 muong ca phe
      bot ngot, 1 muong canh tuong ot, 1 muong canh duong, 1 muong ca phe ot bot
      khuay tan hon hop cho sanh vang lai khoang 3 phut tren lua vua. Cuoi cung
      ban cho bap chuoi da chien vang vao dao deu them 3 phut roi tat bep.Thanh
      pham: Bap chuoi gion rum hoa quyen voi vi man man ngot ngot cua nuoc mam,
      an kem com trang se cuc ki ngon mieng day. Mon an vo cung de lam nay se
      khien gia dinh ban tam tac khen ngon.
    sentences:
      - Nha Hang Ca Hoi Song Nhi
      - Com nhoi thit hap ot chuong
      - Hoa chuoi chien nuoc mam
  - source_sentence: >-
      Noi tieng ve do lau doi va huong vi mon an nay o Ha Noi thi phai ke den
      hang Banh Duc Nong Thanh Tung. Banh o day hap dan o do deo dai cua bot,
      thit nam du day va nem nem vua mieng. Khi phuc vu, mon an nong sot toa ra
      mui huong thom lung tu bot, hanh phi, nuoc mam. Mon banh duc o day duoc
      chan ngap nuoc mam pha loang vi ngot, hoi man man, co thit bam voi nam meo
      va rat nhieu hanh kho da phi vang.Mon banh duc o Banh Duc Nong Thanh Tung
      duoc chan ngap nuoc mam pha loang vi ngot, hoi man man, co thit bam voi
      nam meo va rat nhieu hanh kho da phi vang. Cach an nay hoi giong voi mon
      banh gio chan nuoc mam thit bam o quan pho chua Lang Son gan cho Ban Co.
      La mon qua an nhe nhang, vua du lung lung bung, co ve dan da nen rat nhieu
      nguoi them them, nho nho. Banh duc nong Ha Noi o day khong bi pha them bot
      dau xanh nen van giu nguyen duoc huong vi dac trung. Dac biet, phan nhan
      con duoc tron them mot it cu dau xao tren ngon lua lon nen giu duoc do
      ngot gion.THONG TIN LIEN HE:Dia chi: 112 Truong Dinh, Quan Hai Ba Trung,
      Ha NoiGio mo cua: 10:00 - 21:00Dia diem chat luong: 4.7/5 (14 danh gia
      tren Google)
       Chi duong Danh gia Google
    sentences:
      - Banh Duc
      - Let's Eat Buffet
      - Banh bi do
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on vinai/phobert-base

This is a sentence-transformers model finetuned from vinai/phobert-base. 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: vinai/phobert-base
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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': 128, '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("trongvox/Phobert-Sentence-2")
# Run inference
sentences = [
    'Noi tieng ve do lau doi va huong vi mon an nay o Ha Noi thi phai ke den hang Banh Duc Nong Thanh Tung. Banh o day hap dan o do deo dai cua bot, thit nam du day va nem nem vua mieng. Khi phuc vu, mon an nong sot toa ra mui huong thom lung tu bot, hanh phi, nuoc mam. Mon banh duc o day duoc chan ngap nuoc mam pha loang vi ngot, hoi man man, co thit bam voi nam meo va rat nhieu hanh kho da phi vang.Mon banh duc o Banh Duc Nong Thanh Tung duoc chan ngap nuoc mam pha loang vi ngot, hoi man man, co thit bam voi nam meo va rat nhieu hanh kho da phi vang. Cach an nay hoi giong voi mon banh gio chan nuoc mam thit bam o quan pho chua Lang Son gan cho Ban Co. La mon qua an nhe nhang, vua du lung lung bung, co ve dan da nen rat nhieu nguoi them them, nho nho. Banh duc nong Ha Noi o day khong bi pha them bot dau xanh nen van giu nguyen duoc huong vi dac trung. Dac biet, phan nhan con duoc tron them mot it cu dau xao tren ngon lua lon nen giu duoc do ngot gion.THONG TIN LIEN HE:Dia chi: 112 Truong Dinh, Quan Hai Ba Trung, Ha NoiGio mo cua: 10:00 - 21:00Dia diem chat luong: 4.7/5 (14 danh gia tren Google)\n Chi duong Danh gia Google',
    'Banh Duc',
    'Banh bi do',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 11,347 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 70 tokens
mean: 127.61 tokens
max: 128 tokens

min: 3 tokens
mean: 7.9 tokens
max: 20 tokens

	sentence_0	sentence_1
type	string	string
details	min: 70 tokens mean: 127.61 tokens max: 128 tokens	min: 3 tokens mean: 7.9 tokens max: 20 tokens

Samples:

sentence_0	sentence_1
Nhung cu ca rot tuoi ngon duoc tam uop mot lop gia vi chua chua, ngot ngot va dem nuong chung voi toi thom lung tao nen huong vi hap dan den kho long cuong lai, vi ngot tu nhien kich thich vi giac cua nguoi thuong thuc lam day. Ban co the lam mon ca rot nuong toi nay de an cung thit nuong hay dung lam mon an kem trong bua an rat tuyet nha.Cach che bien: Ban chi can mo lo nuong o 190 do C truoc 10 phut. Trong dau tron deu 1 muong dau olive, 2 muong bo va 2 muong giam Balsamic. Ca rot cat bo phan la xanh, giu nguyen vo, rua that sach, cat lam doi. Cho ca rot vao khay nuong, xep cho deu. Toi lot vo, bao mong. Sau do ruoi hon hop dau olive da chuan bi len ca rot. Sau do cho toi bao mong len cung voi ngo tay, muoi va tieu, tron deu len. Cho khay ca rot vao lo nuong 30 phut la ca rot chin. Lay ra dia va thuong thuc.	`Ca rot nuong`
Banh chung Bo Dau la mot trong nhung mon ngon noi tieng nhat cua Thai Nguyen. Lang banh chung Bo Dau thuoc xa Co Lung, huyen Phu Luong duoc coi la noi luu giu mon banh mang tinh hoa am thuc Viet. "Banh chung luoc nuoc gieng than, thom ngon mui vi co phan troi cho", co le cau ca dao nay da tu lau tro thanh niem tu hao cua nguoi dan noi day - mot trong 5 lang lam banh chung noi tieng nhat mien Bac. Banh chung Bo Dau phai duoc lam tu gao nep nuong thom ngon Dinh Hoa, thit lon sach cua nguoi dan toc va la dong rung duoc hai tai Na Ry, Bac Kan. Voi ban tay kheo leo day dan kinh nghiem lanh nghe cho ra nhung chiec banh dep mat. Co le vi the ma huong vi banh chung Bo Dau khong the tron lan voi cac loai khac. Do la thu dac san quanh nam khong chi dip Tet moi co, da keo chan biet bao du khach tu moi mien den thuong thuc. Huong vi cua troi dat, thien nhien va con nguoi giao hoa, hoa quyen va duoc ket tinh thanh thuc qua dac san noi tieng cua manh dat Thai Nguyen - banh chung Bo Dau.	`Banh chung Bo Dau`
Mi Ramen la mot trong nhung mon an ngon nuc tieng ma nguoi Nhat rat ua chuong va tu hao. Tham chi, nguoi Nhat da mo han mot bao tang mi Ramen voi rat nhieu nhung hien vat trung bay ve lich su ra doi, phat trien cua mon an nay. Phan mi cua Ramen thuong duoc lam tu lua mi, muoi va kansui, co mau vang sam rat hap dan. Linh hon cua mon mi Ramen chac han la phan nuoc dung chu yeu duoc ham tu xuong heo hoac xuong ga trong it nhat 10 tieng tao nen vi ngon ngot, dam da. Va khi thuong thuc, ban se an kem voi thit heo thai lat mong, rong bien, trung, cha ca Nhat, ngo va bap cai de huong vi tro nen hoan hao nhat. Vay con chan chu gi ma khong ghe ngay Nha Hang Tho Tuyet de co ngay mon mi ngon kho cuong nay nao! Nha Hang Tho Tuyet da tro thanh moi ruot cua nhieu thuc khach boi gia rat phai chang, menu khong co qua nhieu mon nhu may cho khac nhung hau nhu thu mon nao cung ngon. Mon Ramen Tho Tuyet Special ngon tuyet voi chac chan ban khong the bo lo. Trong do, an tuong nhat co le chinh la phan nuoc ...	`Nha Hang Tho Tuyet`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 3
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: False
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: False
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.7042	500	0.9125
1.4085	1000	0.2277
2.1127	1500	0.1527
2.8169	2000	0.1009
0.7042	500	0.1098
1.4085	1000	0.0842
2.1127	1500	0.0553
2.8169	2000	0.0356

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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}
}