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Omartificial-Intelligence-Space/Arabic-base-all-nli-stsb-quora

sourceHugging Faceupdated 2y agoView on Hugging Face

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SentenceTransformer based on google-bert/bert-base-multilingual-cased

This is a sentence-transformers model finetuned from google-bert/bert-base-multilingual-cased on the all-nli-pair, all-nli-pair-class, all-nli-pair-score, all-nli-triplet, stsb and quora datasets. 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: google-bert/bert-base-multilingual-cased
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 768 tokens
—Similarity Function: Cosine Similarity
—Training Datasets:
—all-nli-pair
—all-nli-pair-class
—all-nli-pair-score
—all-nli-triplet
—stsb
—quora
—Language: ar

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:

bash

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/Arabic-base-all-nli-stsb-quora")
# Run inference
sentences = [
    'ما هي الدروس التي يمكن أن نتعلمها من أدولف هتلر؟',
    'ما هي الدروس التي يمكن أن نتعلمها من أدولف هتلر؟',
    'ما مدى قربنا من الحرب العالمية؟',
]
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]

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Training Details

Training Datasets

all-nli-pair

—Dataset: all-nli-pair
—Size: 314,315 training samples
—Columns: <code>anchor</code> and <code>positive</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 24.43 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.73 tokens</li><li>max: 45 tokens</li></ul> |
—Samples: | anchor | positive | |:------------------------------------------------------------|:--------------------------------------------| | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | | <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> | | <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

all-nli-pair-class

—Dataset: all-nli-pair-class
—Size: 942,069 training samples
—Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 8 tokens</li><li>mean: 24.78 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.55 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
—Samples: | premise | hypothesis | label | |:-----------------------------------------------|:--------------------------------------------|:---------------| | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص يقوم بتدريب حصانه للمنافسة</code> | <code>1</code> | | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في مطعم، يطلب عجة.</code> | <code>2</code> | | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>0</code> |
—Loss: <code>SoftmaxLoss</code>

all-nli-pair-score

—Dataset: all-nli-pair-score
—Size: 942,069 training samples
—Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
—Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 8 tokens</li><li>mean: 24.78 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.55 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
—Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------|:--------------------------------------------|:-----------------| | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص يقوم بتدريب حصانه للمنافسة</code> | <code>0.5</code> | | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في مطعم، يطلب عجة.</code> | <code>0.0</code> | | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>1.0</code> |
—Loss: <code>CoSENTLoss</code> with these parameters:

json

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

all-nli-triplet

—Dataset: all-nli-triplet
—Size: 557,850 training samples
—Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 12.54 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.06 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.13 tokens</li><li>max: 70 tokens</li></ul> |
—Samples: | anchor | positive | negative | |:------------------------------------------------------------|:--------------------------------------------|:------------------------------------| | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> | | <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> | <code>الاطفال يتجهمون</code> | | <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> | <code>الصبي يتزلج على الرصيف</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

stsb

—Dataset: stsb at 7c6c4bd
—Size: 5,749 training samples
—Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
—Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 5 tokens</li><li>mean: 11.68 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.44 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
—Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------|:--------------------------------------------------------|:------------------| | <code>طائرة ستقلع</code> | <code>طائرة جوية ستقلع</code> | <code>1.0</code> | | <code>رجل يعزف على ناي كبير</code> | <code>رجل يعزف على الناي.</code> | <code>0.76</code> | | <code>رجل ينشر الجبن الممزق على البيتزا</code> | <code>رجل ينشر الجبن الممزق على بيتزا غير مطبوخة</code> | <code>0.76</code> |
—Loss: <code>CoSENTLoss</code> with these parameters:

json

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

quora

—Dataset: quora at 7d49308
—Size: 10,000 training samples
—Columns: <code>anchor</code> and <code>positive</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 19.69 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.15 tokens</li><li>max: 73 tokens</li></ul> |
—Samples: | anchor | positive | |:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------| | <code>علم التنجيم: أنا برج الجدي الشمس القمر والقبعة الشمسية...</code> | <code>أنا برج الجدي الثلاثي (الشمس والقمر والصعود في برج الجدي) ماذا يقول هذا عني؟</code> | | <code>كيف أكون جيولوجياً جيداً؟</code> | <code>ماذا علي أن أفعل لأكون جيولوجياً عظيماً؟</code> | | <code>كيف أقرأ وأجد تعليقاتي على يوتيوب؟</code> | <code>كيف يمكنني رؤية كل تعليقاتي على اليوتيوب؟</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Evaluation Datasets

all-nli-triplet

—Dataset: all-nli-triplet
—Size: 6,584 evaluation samples
—Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 25.81 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.09 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.35 tokens</li><li>max: 42 tokens</li></ul> |
—Samples: | anchor | positive | negative | |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------| | <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>إمرأتان يحملان حزمة</code> | <code>الرجال يتشاجرون خارج مطعم</code> | | <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> | <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> | <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> | | <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code> | <code>رجل يبيع الدونات لعميل</code> | <code>امرأة تشرب قهوتها في مقهى صغير</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

stsb

—Dataset: stsb at 7c6c4bd
—Size: 1,500 evaluation samples
—Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
—Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 5 tokens</li><li>mean: 20.19 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.09 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
—Samples: | sentence1 | sentence2 | score | |:--------------------------------------|:---------------------------------------|:------------------| | <code>رجل يرتدي قبعة صلبة يرقص</code> | <code>رجل يرتدي قبعة صلبة يرقص.</code> | <code>1.0</code> | | <code>طفل صغير يركب حصاناً.</code> | <code>طفل يركب حصاناً.</code> | <code>0.95</code> | | <code>رجل يطعم فأراً لأفعى</code> | <code>الرجل يطعم الفأر للثعبان.</code> | <code>1.0</code> |
—Loss: <code>CoSENTLoss</code> with these parameters:

json

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

quora

—Dataset: quora at 7d49308
—Size: 1,000 evaluation samples
—Columns: <code>anchor</code> and <code>positive</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 19.66 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.17 tokens</li><li>max: 96 tokens</li></ul> |
—Samples: | anchor | positive | |:-------------------------------------------------------------------|:---------------------------------------------------------------------------| | <code>ما هو قرارك في السنة الجديدة؟</code> | <code>ما الذي يمكن أن يكون قراري للعام الجديد لعام 2017؟</code> | | <code>هل يجب أن أشتري هاتف آيفون 6 أو سامسونج غالاكسي إس 7؟</code> | <code>أيهما أفضل: الـ iPhone 6S Plus أو الـ Samsung Galaxy S7 Edge؟</code> | | <code>ما هي الاختلافات بين التجاوز والتراجع؟</code> | <code>ما الفرق بين التجاوز والتراجع؟</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 128
—num_train_epochs: 1
—warmup_ratio: 0.1

All Hyperparameters

<details><summary>Click to expand</summary>

—overwrite_output_dir: False
—do_predict: False
—prediction_loss_only: True
—per_device_train_batch_size: 128
—per_device_eval_batch_size: 8
—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: 1
—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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'gradientaccumulation_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: batch_sampler
—multi_dataset_batch_sampler: proportional

</details>

Training Logs

Epoch	Step	Training Loss
0.0231	500	5.0061
0.0462	1000	4.7876
0.0693	1500	4.6618
0.0923	2000	4.7337
0.1154	2500	4.5945
0.1385	3000	4.7536
0.1616	3500	4.619
0.1847	4000	4.4761
0.2078	4500	4.4454
0.2309	5000	4.6376
0.2539	5500	4.5513
0.2770	6000	4.5619
0.3001	6500	4.3416
0.3232	7000	4.7372
0.3463	7500	4.5906
0.3694	8000	4.6546
0.3924	8500	4.2452
0.4155	9000	4.684
0.4386	9500	4.426
0.4617	10000	4.2539
0.4848	10500	4.3224
0.5079	11000	4.4046
0.5310	11500	4.4644
0.5540	12000	4.4542
0.5771	12500	4.6026
0.6002	13000	4.3519
0.6233	13500	4.5135
0.6464	14000	4.3318
0.6695	14500	4.4465
0.6926	15000	3.9692
0.7156	15500	4.2084
0.7387	16000	4.2217
0.7618	16500	4.2791
0.7849	17000	4.5962
0.8080	17500	4.5871
0.8311	18000	4.3271
0.8541	18500	4.1688
0.8772	19000	4.2081
0.9003	19500	4.2867
0.9234	20000	4.5474
0.9465	20500	4.5257
0.9696	21000	3.8461
0.9927	21500	4.1254

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 and SoftmaxLoss

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}
}

CoSENTLoss

bibtex

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

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