This is a sentence-transformers model trained on the AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative 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

Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative

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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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("AbderrahmanSkiredj1/Arabic_text_embedding_for_sts")
# Run inference
sentences = [
    'يتم إنتاج أمثلة جميلة من المينا، والسيراميك، والفخار في وفرة كبيرة، وغالبا ما تتبع موضوع سلتيكي.',
    'يتم إنتاج عدد كبير من العناصر ذات المواضيع السلتية.',
    'يتم إنتاج الفخار الصغير الذي له موضوع سلتيكي.',
]
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

AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative

Dataset: AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative
Size: 853,827 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 4 tokens
mean: 14.54 tokens
max: 91 tokens
min: 4 tokens
mean: 10.62 tokens
max: 43 tokens
min: 4 tokens
mean: 10.32 tokens
max: 35 tokens

Samples:

anchor	positive	negative
`هل يمكنك أن تأكل نفس الشيء كل يوم وتحصل على كل التغذية التي تحتاجها؟`	`هل الأكل نفس الشيء كل يوم صحي؟`	`ما هي القوة الخارقة التي تتمنى أن تملكها؟`
`ثلاثة لاعبي كرة قدم، رقم 16 يرمي الكرة، رقم 71 يمنع الخصم الآخر.`	`لاعبي كرة القدم يرمون ويمنعون بعضهم البعض`	`الفريق يأكل البيتزا في مطعم`
`كيف تحسن مهاراتك في الكتابة؟`	`كيف أستمر في تحسين كتابتي؟`	`كيف يتم تحديد أرقام الضمان الاجتماعي؟`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Evaluation Dataset

AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative

Dataset: AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative
Size: 11,584 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 3 tokens
mean: 16.03 tokens
max: 88 tokens
min: 3 tokens
mean: 11.72 tokens
max: 221 tokens
min: 3 tokens
mean: 10.59 tokens
max: 42 tokens

Samples:

anchor	positive	negative
`ماذا سيحدث لو توقفت الأرض عن الدوران وتدور في نفس الوقت؟`	`ماذا سيحدث إذا توقفت الأرض عن الدوران؟`	`ما هو أفضل هاتف ذكي تحت 15000؟`
`ثلاثة متفرجين بالغين وطفل واحد ينظرون إلى السماء بينما يقفون على الرصيف.`	`أربعة أشخاص ينظرون إلى السماء.`	`رجل وثلاثة أطفال يشاهدون بالونات الهيليوم تطفو أعلى في الهواء`
`ماذا تفعل الدول لمنع الحرب؟`	`كيف يجب على الدول أن تمنع الحرب؟`	`كيف يمكنني كسب المال من بدء مدونة؟`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 1e-06
num_train_epochs: 10
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 1e-06
weight_decay: 0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
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
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: True
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}
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
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_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.0120	40	3.1459
0.0240	80	3.2058
0.0360	120	3.0837
0.0480	160	3.1024
0.0600	200	3.015
0.0719	240	3.1311
0.0839	280	3.1101
0.0959	320	3.1288
0.1079	360	3.045
0.1199	400	3.0488
0.1319	440	3.1001
0.1439	480	3.2334
0.1559	520	3.0581
0.1679	560	2.9821
0.1799	600	3.1733
0.1918	640	3.0658
0.2038	680	3.0721
0.2158	720	3.1647
0.2278	760	3.0326
0.2398	800	3.1014
0.2518	840	2.9365
0.2638	880	3.0642
0.2758	920	2.9864
0.2878	960	3.0939
0.2998	1000	3.0676
0.3118	1040	2.9717
0.3237	1080	2.9908
0.3357	1120	2.9506
0.3477	1160	2.907
0.3597	1200	3.0451
0.3717	1240	3.0002
0.3837	1280	2.8842
0.3957	1320	3.0697
0.4077	1360	2.8967
0.4197	1400	3.0008
0.4317	1440	3.0027
0.4436	1480	2.9229
0.4556	1520	2.9539
0.4676	1560	2.9415
0.4796	1600	2.9401
0.4916	1640	2.8498
0.5036	1680	2.9646
0.5156	1720	2.9231
0.5276	1760	2.942
0.5396	1800	2.8521
0.5516	1840	2.8362
0.5635	1880	2.8497
0.5755	1920	2.8867
0.5875	1960	2.9148
0.5995	2000	2.9343
0.6115	2040	2.8537
0.6235	2080	2.7989
0.6355	2120	2.8508
0.6475	2160	2.916
0.6595	2200	2.926
0.6715	2240	2.752
0.6835	2280	2.7792
0.6954	2320	2.8381
0.7074	2360	2.7455
0.7194	2400	2.8953
0.7314	2440	2.8179
0.7434	2480	2.8471
0.7554	2520	2.7538
0.7674	2560	2.8271
0.7794	2600	2.8401
0.7914	2640	2.7402
0.8034	2680	2.6439

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.0.1
Transformers: 4.39.3
PyTorch: 2.2.2+cu121
Accelerate: 0.29.1
Datasets: 2.18.0
Tokenizers: 0.15.2

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",
}

MatryoshkaLoss

bibtex

@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

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	negative
type	string	string	string
details	min: 4 tokens mean: 14.54 tokens max: 91 tokens	min: 4 tokens mean: 10.62 tokens max: 43 tokens	min: 4 tokens mean: 10.32 tokens max: 35 tokens