main_v2 / README.md

Add new SentenceTransformer model

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:49500
  - loss:MultipleNegativesRankingLoss
base_model: prestoai/qwen3-embedding-0.6b-arabic-ecom
widget:
  - source_sentence: >-
      Instruct: Given an Arabic e-commerce search query, retrieve the product
      that best matches it

      Query: عناية بالفم
    sentences:
      - تن ريقا بزيت الزيتون 160جم
      - Foramen Denture Clean Box
      - صبغة شعر L'Oréal Paris - 5.45 Excellence
  - source_sentence: >-
      Instruct: Given an Arabic e-commerce search query, retrieve the product
      that best matches it

      Query: بسكويت شوكولاتة بالحليب
    sentences:
      - بسكويت - Bahlsen
      - حقيبة هدايا - RA040
      - Cicabio Arnica+ - Bioderma
  - source_sentence: >-
      Instruct: Given an Arabic e-commerce search query, retrieve the product
      that best matches it

      Query: بيتر صودا
    sentences:
      - ميرندا صودا
      - زبدة فول السوداني حدائق كاليفورنيا ناعمه - 510 غ
      - مشروب بيتر صودا - ميرندا
  - source_sentence: >-
      Instruct: Given an Arabic e-commerce search query, retrieve the product
      that best matches it

      Query: شيجلام بودرة
    sentences:
      - بودرة SHEGLAM - High Coverage Linen
      - برايمر فائق الترطيب - SHEGLAM
      - سباتلة حجم صغير تريبولي سنتر
  - source_sentence: >-
      Instruct: Given an Arabic e-commerce search query, retrieve the product
      that best matches it

      Query: عطور نسائية
    sentences:
      - تن الوفاء سكيب جاك بالزيت الزيتون - 160 غ
      - مجموعة عطر نسائي - ابراهيم القرشي سكر
      - قبعة رجالية - 07
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on prestoai/qwen3-embedding-0.6b-arabic-ecom

This is a sentence-transformers model finetuned from prestoai/qwen3-embedding-0.6b-arabic-ecom on the pairs_with_negatives and positives datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: prestoai/qwen3-embedding-0.6b-arabic-ecom
Maximum Sequence Length: 128 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text
Training Datasets:
- pairs_with_negatives
- positives

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen3Model'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', '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("leafxyz/main_v2")
# Run inference
queries = [
    'Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: عطور نسائية',
]
documents = [
    'مجموعة عطر نسائي - ابراهيم القرشي سكر',
    'قبعة رجالية - 07',
    'تن الوفاء سكيب جاك بالزيت الزيتون - 160 غ',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.5593, -0.0069,  0.0400]])

Training Details

Training Datasets

pairs_with_negatives

Dataset: pairs_with_negatives
Size: 9,900 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 24 tokens mean: 29.36 tokens max: 42 tokens	min: 3 tokens mean: 15.82 tokens max: 38 tokens	min: 2 tokens mean: 15.12 tokens max: 44 tokens

Samples:

anchor	positive	negative
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: زيت بابايا WKL`	`زيت جسم - WKL Papaya`	`زيت جسم - Vaseline Cocoa`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: منكير`	`اظافر هيفا - TWINKLE`	`اظافر هيفا - SPARKLE`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: توب فريش حفاضات`	`توب فريش حفاضات رقم 1 - 44 قطعة`	`توب فريش حفاضات رقم 2 - 40 قطعة`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

positives

Dataset: positives
Size: 39,600 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 23 tokens
mean: 29.52 tokens
max: 41 tokens

min: 3 tokens
mean: 13.77 tokens
max: 39 tokens

	anchor	positive
type	string	string
details	min: 23 tokens mean: 29.52 tokens max: 41 tokens	min: 3 tokens mean: 13.77 tokens max: 39 tokens

Samples:

anchor	positive
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: نبي فيكسول ارجواني`	`منظف الحمام الذكي فيكسول ارجواني - 900 مل`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: شربة نجمة اريغي 500`	`شربة نجمة اريغي - 500 غ`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: Gas relife drops`	`Gas relife drops`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Evaluation Datasets

pairs_with_negatives

Dataset: pairs_with_negatives
Size: 100 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 100 samples:

	anchor	positive	negative
type	string	string	string
details	min: 24 tokens mean: 29.35 tokens max: 43 tokens	min: 5 tokens mean: 15.71 tokens max: 31 tokens	min: 4 tokens mean: 15.2 tokens max: 34 tokens

Samples:

anchor	positive	negative
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: عناية بالجسم`	`معطر جسم وشعر نسائي - Sol de Janeiro Água Mística`	`قارورة عصير - AS02`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: بخاخ تشيكو 100 مل`	`بخاخ تشيكو للحماية من البعوض - 100 مل`	`مناديل الحماية من البعوض تشيكو - 20 قطعة`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: بخاخ مانع التصاق`	`بخاخ الطبخ بنكهة الفلفل مانع للالتصاق - 200 مل`	`بخاخ الطبخ بنكهة الثوم مانع للالتصاق - 200 مل`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

positives

Dataset: positives
Size: 400 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 400 samples:
anchor positive
type string string
details
min: 24 tokens
mean: 29.43 tokens
max: 43 tokens

min: 3 tokens
mean: 13.74 tokens
max: 35 tokens

	anchor	positive
type	string	string
details	min: 24 tokens mean: 29.43 tokens max: 43 tokens	min: 3 tokens mean: 13.74 tokens max: 35 tokens

Samples:

anchor	positive
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: سوار نسائي ذهبي`	`سوار نسائي - DX052`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: سناكس`	`شوكلاتة كندر ترونكي 8*48`
`Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it Query: مشروب حليب`	`حليب - Safi`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Training Hyperparameters

Non-Default Hyperparameters

gradient_accumulation_steps: 4
learning_rate: 3e-05
num_train_epochs: 1
warmup_steps: 0.05
fp16: True
dataloader_num_workers: 2
gradient_checkpointing: True

All Hyperparameters

Click to expand

do_predict: False
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: 3e-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: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0.05
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: 2
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
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: True
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	pairs with negatives loss	positives loss
0.0323	50	0.2092	-	-
0.0646	100	0.2008	-	-
0.0970	150	0.1948	-	-
0.1293	200	0.1749	-	-
0.1616	250	0.1455	-	-
0.1939	300	0.1924	-	-
0.2262	350	0.1959	-	-
0.2586	400	0.1606	-	-
0.2909	450	0.1679	-	-
0.3232	500	0.1774	0.3304	0.1113
0.3555	550	0.1924	-	-
0.3878	600	0.1487	-	-
0.4202	650	0.1859	-	-
0.4525	700	0.1807	-	-
0.4848	750	0.1785	-	-
0.5171	800	0.1534	-	-
0.5495	850	0.1468	-	-
0.5818	900	0.1566	-	-
0.6141	950	0.1153	-	-
0.6464	1000	0.1322	0.3138	0.0943
0.6787	1050	0.1320	-	-
0.7111	1100	0.1533	-	-
0.7434	1150	0.1358	-	-
0.7757	1200	0.1457	-	-
0.8080	1250	0.1320	-	-
0.8403	1300	0.1680	-	-
0.8727	1350	0.1280	-	-
0.9050	1400	0.1632	-	-
0.9373	1450	0.1656	-	-
0.9696	1500	0.1363	0.3024	0.0914

Training Time

Training: 1.9 hours

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.4.1
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 5.0.0
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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}