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Thai Food Ingredients → Dish Prediction

Name: thai-food-mpnet-new-v9
Availability: InStock

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2. 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: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Language: th
License: apache-2.0

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: XLMRobertaModel 
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("thai_food_prediction1")
# Run inference
sentences = [
    'ปลาทูน่า,  พริกขี้หนู,  ไข่ไก่,  น้ำปลา, เล็กน้อย, น้ำมันพืช',
    'ไข่เจียวทูน่าพริกสับ',
    'ข้าวแต๋น',
]
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: thai-food-eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6053
cosine_accuracy@3	0.8421
cosine_accuracy@5	0.9605
cosine_accuracy@10	0.9737
cosine_precision@1	0.6053
cosine_precision@3	0.2807
cosine_precision@5	0.1921
cosine_recall@1	0.6053
cosine_recall@3	0.8421
cosine_recall@5	0.9605
cosine_ndcg@10	0.7949
cosine_mrr@10	0.736
cosine_map@100	0.7372

Training Details

Training Dataset

Unnamed Dataset

Size: 2,452 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 7 tokens
mean: 29.15 tokens
max: 125 tokens
min: 4 tokens
mean: 9.91 tokens
max: 22 tokens

Samples:

anchor	positive
`ปลาหมึก, ซีอิ๊วดำ, ผงขมิ้น, น้ำปูนใส, กระเทียมสับ, รากผักชี, พริกแดง, น้ำตาลปี๊บ, เกลือ, น้ำปลา, น้ำมะนาว`	`ปลาหมึกย่าง`
`ไปตกหมึกมา อยากทำอะไรกินง่ายๆ ได้รสชาติของปลาหมึกแท้ๆ`	`ปลาหมึกย่าง`
`อยากกินปลาหมึกๆ ซีฟุ้ด อร่อยๆ`	`ปลาหมึกย่าง`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 76 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 76 samples:
anchor positive
type string string
details
min: 4 tokens
mean: 47.42 tokens
max: 86 tokens
min: 4 tokens
mean: 10.24 tokens
max: 20 tokens

Samples:

anchor	positive
`น้ำมันพืช, กระเทียม, น้ำตาลทราย, น้ำปลา, ซีอิ๊วขาว, ซอสปรุงรส, ซีอิ๊วดำเค็ม, น้ำส้มสายชู, พริกไทย, เส้นหมี่แห้ง, ลูกชิ้น, ถั่วงอก`	`หมี่คลุก`
`น้ำมัน, กระเทียม, หมูหมัก, เส้นใหญ่, ซีอิ้วดำ, คะน้า, กระหล่ำปลี, แครอท, ไข่เป็ด, ไข่ไก่, ผงปรุงรส, น้ำตาลทราย, ซอสหอยนางรม, ซอสปรุงรส, พริกไทย`	`ผัดซีอิ้วเส้นใหญ่`
`สะโพกหมู, น้ำตาลทราย, น้ำตาลปี๊บ, ซีอิ๊วขาว, เกลือ, น้ำเปล่า, ลูกผักชี, ยี่หร่า, กระเทียมไทย, สับละเอียด, น้ำมันพืช`	`หมูสวรรค์`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
learning_rate: 5e-06
num_train_epochs: 8
warmup_ratio: 0.1
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
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-06
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 8
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 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: 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
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	thai-food-eval_cosine_ndcg@10
0.0971	10	3.2623	-	-
0.1942	20	2.7506	-	-
0.2913	30	2.45	-	-
0.3883	40	2.1205	-	-
0.4854	50	2.0216	-	-
0.5825	60	1.7865	-	-
0.6796	70	1.7075	-	-
0.7767	80	1.4338	-	-
0.8738	90	1.5122	-	-
0.9709	100	1.4975	-	-
1.0	103	-	1.0337	0.6411
1.0680	110	1.2398	-	-
1.1650	120	1.1619	-	-
1.2621	130	1.1641	-	-
1.3592	140	1.084	-	-
1.4563	150	0.992	-	-
1.5534	160	0.9877	-	-
1.6505	170	1.0527	-	-
1.7476	180	1.0431	-	-
1.8447	190	1.0235	-	-
1.9417	200	1.0231	-	-
2.0	206	-	0.6985	0.7429
2.0388	210	0.8387	-	-
2.1359	220	0.6738	-	-
2.2330	230	0.7837	-	-
2.3301	240	0.8629	-	-
2.4272	250	0.6708	-	-
2.5243	260	0.8917	-	-
2.6214	270	0.7686	-	-
2.7184	280	0.7352	-	-
2.8155	290	0.6844	-	-
2.9126	300	0.7821	-	-
3.0	309	-	0.6023	0.7508
3.0097	310	0.6968	-	-
3.1068	320	0.6536	-	-
3.2039	330	0.6157	-	-
3.3010	340	0.6562	-	-
3.3981	350	0.563	-	-
3.4951	360	0.6401	-	-
3.5922	370	0.6167	-	-
3.6893	380	0.5221	-	-
3.7864	390	0.5609	-	-
3.8835	400	0.6595	-	-
3.9806	410	0.5761	-	-
4.0	412	-	0.5541	0.7728
4.0777	420	0.4465	-	-
4.1748	430	0.4011	-	-
4.2718	440	0.4988	-	-
4.3689	450	0.5891	-	-
4.4660	460	0.6107	-	-
4.5631	470	0.5573	-	-
4.6602	480	0.5007	-	-
4.7573	490	0.4907	-	-
4.8544	500	0.4756	-	-
4.9515	510	0.5233	-	-
5.0	515	-	0.4831	0.7920
5.0485	520	0.4877	-	-
5.1456	530	0.5158	-	-
5.2427	540	0.4769	-	-
5.3398	550	0.4461	-	-
5.4369	560	0.4684	-	-
5.5340	570	0.347	-	-
5.6311	580	0.4203	-	-
5.7282	590	0.4448	-	-
5.8252	600	0.3725	-	-
5.9223	610	0.4598	-	-
6.0	618	-	0.4754	0.7938
6.0194	620	0.5246	-	-
6.1165	630	0.3768	-	-
6.2136	640	0.3567	-	-
6.3107	650	0.4294	-	-
6.4078	660	0.356	-	-
6.5049	670	0.4915	-	-
6.6019	680	0.3908	-	-
6.6990	690	0.3245	-	-
6.7961	700	0.4382	-	-
6.8932	710	0.4935	-	-
6.9903	720	0.4248	-	-
7.0	721	-	0.4928	0.7886
7.0874	730	0.2804	-	-
7.1845	740	0.3395	-	-
7.2816	750	0.3559	-	-
7.3786	760	0.4312	-	-
7.4757	770	0.3929	-	-
7.5728	780	0.3772	-	-
7.6699	790	0.3205	-	-
7.7670	800	0.3914	-	-
7.8641	810	0.4501	-	-
7.9612	820	0.4708	-	-
8.0	824	-	0.4766	0.7949

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 2.14.4
Tokenizers: 0.21.2

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}
}

	anchor	positive
type	string	string
details	min: 7 tokens mean: 29.15 tokens max: 125 tokens	min: 4 tokens mean: 9.91 tokens max: 22 tokens

Chanisorn/thai-food-mpnet-new-v9

Thai Food Ingredients → Dish Prediction

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

Chanisorn/
thai-food-mpnet-new-v9