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Omartificial-Intelligence-Space/
Arabic-mpnet-base-all-nli-triplet

Name: Arabic-mpnet-base-all-nli-triplet
Rating: 2.5 (567 reviews)

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SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet

This is a sentence-transformers model finetuned from tomaarsen/mpnet-base-all-nli-triplet on the Omartificial-Intelligence-Space/arabic-n_li-triplet 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: tomaarsen/mpnet-base-all-nli-triplet
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- Omartificial-Intelligence-Space/arabic-n_li-triplet

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: MPNetModel 
  (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("Omartificial-Intelligence-Space/mpnet-base-all-nli-triplet-Arabic-mpnet_base")
# Run inference
sentences = [
    'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
    'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
    'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
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

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6699
spearman_cosine	0.6757
pearson_manhattan	0.6943
spearman_manhattan	0.684
pearson_euclidean	0.6973
spearman_euclidean	0.6873
pearson_dot	0.5534
spearman_dot	0.5422
pearson_max	0.6973
spearman_max	0.6873

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6628
spearman_cosine	0.6703
pearson_manhattan	0.6917
spearman_manhattan	0.6816
pearson_euclidean	0.6949
spearman_euclidean	0.6853
pearson_dot	0.5229
spearman_dot	0.5114
pearson_max	0.6949
spearman_max	0.6853

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6368
spearman_cosine	0.6513
pearson_manhattan	0.6832
spearman_manhattan	0.6746
pearson_euclidean	0.6844
spearman_euclidean	0.676
pearson_dot	0.4266
spearman_dot	0.4179
pearson_max	0.6844
spearman_max	0.676

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6148
spearman_cosine	0.6355
pearson_manhattan	0.6731
spearman_manhattan	0.6653
pearson_euclidean	0.6764
spearman_euclidean	0.6691
pearson_dot	0.3513
spearman_dot	0.3445
pearson_max	0.6764
spearman_max	0.6691

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5789
spearman_cosine	0.6081
pearson_manhattan	0.6579
spearman_manhattan	0.6519
pearson_euclidean	0.663
spearman_euclidean	0.6571
pearson_dot	0.2403
spearman_dot	0.2331
pearson_max	0.663
spearman_max	0.6571

Training Details

Training Dataset

Omartificial-Intelligence-Space/arabic-n_li-triplet

Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
Size: 557,850 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 12 tokens
mean: 23.93 tokens
max: 155 tokens
min: 9 tokens
mean: 29.62 tokens
max: 117 tokens
min: 13 tokens
mean: 33.95 tokens
max: 149 tokens

Samples:

anchor	positive	negative
`شخص على حصان يقفز فوق طائرة معطلة`	`شخص في الهواء الطلق، على حصان.`	`شخص في مطعم، يطلب عجة.`
`أطفال يبتسمون و يلوحون للكاميرا`	`هناك أطفال حاضرون`	`الاطفال يتجهمون`
`صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.`	`الفتى يقوم بخدعة التزلج`	`الصبي يتزلج على الرصيف`

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

Omartificial-Intelligence-Space/arabic-n_li-triplet

Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
Size: 6,584 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 12 tokens
mean: 49.5 tokens
max: 246 tokens
min: 8 tokens
mean: 23.66 tokens
max: 103 tokens
min: 9 tokens
mean: 25.33 tokens
max: 82 tokens

Samples:

anchor	positive	negative
`امرأتان يتعانقان بينما يحملان حزمة`	`إمرأتان يحملان حزمة`	`الرجال يتشاجرون خارج مطعم`
`طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.`	`طفلين يرتديان قميصاً مرقماً يغسلون أيديهم`	`طفلين يرتديان سترة يذهبان إلى المدرسة`
`رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس`	`رجل يبيع الدونات لعميل`	`امرأة تشرب قهوتها في مقهى صغير`

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
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: 5e-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: 3
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, '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_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	sts-test-128_spearman_cosine	sts-test-256_spearman_cosine	sts-test-512_spearman_cosine	sts-test-64_spearman_cosine	sts-test-768_spearman_cosine
0.0229	200	21.5318	-	-	-	-	-
0.0459	400	17.2344	-	-	-	-	-
0.0688	600	15.393	-	-	-	-	-
0.0918	800	13.7897	-	-	-	-	-
0.1147	1000	13.534	-	-	-	-	-
0.1377	1200	12.2683	-	-	-	-	-
0.1606	1400	10.9271	-	-	-	-	-
0.1835	1600	11.071	-	-	-	-	-
0.2065	1800	10.0153	-	-	-	-	-
0.2294	2000	9.8463	-	-	-	-	-
0.2524	2200	10.0194	-	-	-	-	-
0.2753	2400	9.8371	-	-	-	-	-
0.2983	2600	9.6315	-	-	-	-	-
0.3212	2800	8.9858	-	-	-	-	-
0.3442	3000	9.1876	-	-	-	-	-
0.3671	3200	8.8028	-	-	-	-	-
0.3900	3400	8.6075	-	-	-	-	-
0.4130	3600	8.4285	-	-	-	-	-
0.4359	3800	8.1258	-	-	-	-	-
0.4589	4000	8.2508	-	-	-	-	-
0.4818	4200	7.8037	-	-	-	-	-
0.5048	4400	7.7133	-	-	-	-	-
0.5277	4600	7.5006	-	-	-	-	-
0.5506	4800	7.7025	-	-	-	-	-
0.5736	5000	7.7593	-	-	-	-	-
0.5965	5200	7.6305	-	-	-	-	-
0.6195	5400	7.7502	-	-	-	-	-
0.6424	5600	7.5624	-	-	-	-	-
0.6654	5800	7.5287	-	-	-	-	-
0.6883	6000	7.4261	-	-	-	-	-
0.7113	6200	7.239	-	-	-	-	-
0.7342	6400	7.1631	-	-	-	-	-
0.7571	6600	7.6865	-	-	-	-	-
0.7801	6800	7.6124	-	-	-	-	-
0.8030	7000	6.9936	-	-	-	-	-
0.8260	7200	6.7331	-	-	-	-	-
0.8489	7400	6.4542	-	-	-	-	-
0.8719	7600	6.1994	-	-	-	-	-
0.8948	7800	5.9798	-	-	-	-	-
0.9177	8000	5.7808	-	-	-	-	-
0.9407	8200	5.6952	-	-	-	-	-
0.9636	8400	5.5082	-	-	-	-	-
0.9866	8600	5.4421	-	-	-	-	-
1.0095	8800	3.0309	-	-	-	-	-
1.0026	9000	1.1835	-	-	-	-	-
1.0256	9200	8.1196	-	-	-	-	-
1.0485	9400	8.0326	-	-	-	-	-
1.0715	9600	8.5028	-	-	-	-	-
1.0944	9800	7.6923	-	-	-	-	-
1.1174	10000	8.029	-	-	-	-	-
1.1403	10200	7.5052	-	-	-	-	-
1.1632	10400	7.1177	-	-	-	-	-
1.1862	10600	6.9594	-	-	-	-	-
1.2091	10800	6.6662	-	-	-	-	-
1.2321	11000	6.6903	-	-	-	-	-
1.2550	11200	6.9523	-	-	-	-	-
1.2780	11400	6.676	-	-	-	-	-
1.3009	11600	6.7141	-	-	-	-	-
1.3238	11800	6.568	-	-	-	-	-
1.3468	12000	6.8938	-	-	-	-	-
1.3697	12200	6.3745	-	-	-	-	-
1.3927	12400	6.2513	-	-	-	-	-
1.4156	12600	6.2589	-	-	-	-	-
1.4386	12800	6.1388	-	-	-	-	-
1.4615	13000	6.1835	-	-	-	-	-
1.4845	13200	5.9004	-	-	-	-	-
1.5074	13400	5.7891	-	-	-	-	-
1.5303	13600	5.6184	-	-	-	-	-
1.5533	13800	5.9762	-	-	-	-	-
1.5762	14000	5.9737	-	-	-	-	-
1.5992	14200	5.8563	-	-	-	-	-
1.6221	14400	5.8904	-	-	-	-	-
1.6451	14600	5.8484	-	-	-	-	-
1.6680	14800	5.8906	-	-	-	-	-
1.6909	15000	5.7613	-	-	-	-	-
1.7139	15200	5.5744	-	-	-	-	-
1.7368	15400	5.6569	-	-	-	-	-
1.7598	15600	5.7439	-	-	-	-	-
1.7827	15800	5.5593	-	-	-	-	-
1.8057	16000	5.2935	-	-	-	-	-
1.8286	16200	5.088	-	-	-	-	-
1.8516	16400	5.0167	-	-	-	-	-
1.8745	16600	4.84	-	-	-	-	-
1.8974	16800	4.6731	-	-	-	-	-
1.9204	17000	4.6404	-	-	-	-	-
1.9433	17200	4.6413	-	-	-	-	-
1.9663	17400	4.4495	-	-	-	-	-
1.9892	17600	4.4262	-	-	-	-	-
2.0122	17800	2.01	-	-	-	-	-
2.0053	18000	1.8418	-	-	-	-	-
2.0282	18200	6.2714	-	-	-	-	-
2.0512	18400	6.1742	-	-	-	-	-
2.0741	18600	6.5996	-	-	-	-	-
2.0971	18800	6.0907	-	-	-	-	-
2.1200	19000	6.2418	-	-	-	-	-
2.1429	19200	5.7817	-	-	-	-	-
2.1659	19400	5.7073	-	-	-	-	-
2.1888	19600	5.2645	-	-	-	-	-
2.2118	19800	5.3451	-	-	-	-	-
2.2347	20000	5.2453	-	-	-	-	-
2.2577	20200	5.6161	-	-	-	-	-
2.2806	20400	5.2289	-	-	-	-	-
2.3035	20600	5.3888	-	-	-	-	-
2.3265	20800	5.2483	-	-	-	-	-
2.3494	21000	5.5791	-	-	-	-	-
2.3724	21200	5.1643	-	-	-	-	-
2.3953	21400	5.1231	-	-	-	-	-
2.4183	21600	5.1055	-	-	-	-	-
2.4412	21800	5.1778	-	-	-	-	-
2.4642	22000	5.0466	-	-	-	-	-
2.4871	22200	4.8321	-	-	-	-	-
2.5100	22400	4.7056	-	-	-	-	-
2.5330	22600	4.6858	-	-	-	-	-
2.5559	22800	4.9189	-	-	-	-	-
2.5789	23000	4.912	-	-	-	-	-
2.6018	23200	4.8289	-	-	-	-	-
2.6248	23400	4.8959	-	-	-	-	-
2.6477	23600	4.9441	-	-	-	-	-
2.6706	23800	4.9334	-	-	-	-	-
2.6936	24000	4.8328	-	-	-	-	-
2.7165	24200	4.601	-	-	-	-	-
2.7395	24400	4.834	-	-	-	-	-
2.7624	24600	5.152	-	-	-	-	-
2.7854	24800	4.9232	-	-	-	-	-
2.8083	25000	4.6556	-	-	-	-	-
2.8312	25200	4.6229	-	-	-	-	-
2.8542	25400	4.5768	-	-	-	-	-
2.8771	25600	4.3619	-	-	-	-	-
2.9001	25800	4.3608	-	-	-	-	-
2.9230	26000	4.2834	-	-	-	-	-
2.9403	26151	-	0.6355	0.6513	0.6703	0.6081	0.6757

Framework Versions

Python: 3.9.18
Sentence Transformers: 3.0.1
Transformers: 4.40.0
PyTorch: 2.2.2+cu121
Accelerate: 0.26.1
Datasets: 2.19.0
Tokenizers: 0.19.1

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}
}

Acknowledgments

The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.

markdown

## Citation

If you use the Arabic Matryoshka Embeddings Model, please cite it as follows:

@misc{nacar2024enhancingsemanticsimilarityunderstanding,
      title={Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning}, 
      author={Omer Nacar and Anis Koubaa},
      year={2024},
      eprint={2407.21139},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.21139}, 
}

	anchor	positive	negative
type	string	string	string
details	min: 12 tokens mean: 23.93 tokens max: 155 tokens	min: 9 tokens mean: 29.62 tokens max: 117 tokens	min: 13 tokens mean: 33.95 tokens max: 149 tokens

Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet

SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Training Details

Training Dataset

Omartificial-Intelligence-Space/arabic-n_li-triplet

Evaluation Dataset

Omartificial-Intelligence-Space/arabic-n_li-triplet

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Acknowledgments

Omartificial-Intelligence-Space/
Arabic-mpnet-base-all-nli-triplet