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Arabic-SBERT-100K

This is a sentence-transformers model finetuned from aubmindlab/bert-base-arabertv02. 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. This model is trained on 100K samples filtered from the akhooli/arabic-triplets-1m-curated-sims-len dataset with 75K training and 25K validation. Trained for 5 epochs, with final training loss of 0.133 (using MatryoshkaLoss).

The rest of this file is auto generated.

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Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: aubmindlab/bert-base-arabertv02
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity

Model Sources

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'ما هو نوع الدهون الموجودة في الأفوكادو',
    'حوالي 15 في المائة من الدهون في الأفوكادو مشبعة ، مع كل كوب واحد من الأفوكادو المفروم يحتوي على 3.2 جرام من الدهون المشبعة ، وهو ما يمثل 16 في المائة من DV البالغ 20 جرامًا. تحتوي الأفوكادو في الغالب على دهون أحادية غير مشبعة ، مع 67 في المائة من إجمالي الدهون ، أو 14.7 جرامًا لكل كوب مفروم ، ويتكون من هذا النوع من الدهون.',
    'يمكن أن يؤدي ارتفاع مستوى الدهون الثلاثية ، وهي نوع من الدهون (الدهون) في الدم ، إلى زيادة خطر الإصابة بأمراض القلب ، ويمكن أن يؤدي توفير مستوى مرتفع من الدهون الثلاثية ، وهي نوع من الدهون (الدهون) في الدم ، إلى زيادة خطر الإصابة بأمراض القلب. مرض.',
]
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

Unnamed Dataset

  • Size: 75,000 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: 12.88 tokens
    • max: 58 tokens
    • min: 4 tokens
    • mean: 13.74 tokens
    • max: 126 tokens
    • min: 4 tokens
    • mean: 13.38 tokens
    • max: 146 tokens
  • Samples:
    anchor positive negative
    هل تشاجر (سي إس لويس) و (جي آر آر تولكين) ؟ إن كان الأمر كذلك، فما هو السبب؟ هل صحيح أن (سي إس لويس) و (تولكين) تشاجرا؟ ما هي أفضل الكتب للدراسة في الجامعة؟
    ما هي اعراض فقر الدم؟ ما هي اعراض الانيميا؟ كيف احضر كيكة العسل؟
    من ستصوت له، دونالد ترامب أم هيلاري كلينتون؟ هل تؤيدون دونالد ترامب أم هيلاري كلينتون؟ لماذا؟ كيف أتغلب على إدمان المواد الإباحية؟
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "MultipleNegativesRankingLoss",
        "matryoshka_dims": [
            768,
            512,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            1,
            1,
            1,
            1
        ],
        "n_dims_per_step": -1
    }
    

Evaluation Dataset

Unnamed Dataset

  • Size: 25,000 evaluation 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: 12.6 tokens
    • max: 70 tokens
    • min: 4 tokens
    • mean: 14.82 tokens
    • max: 239 tokens
    • min: 4 tokens
    • mean: 13.78 tokens
    • max: 128 tokens
  • Samples:
    anchor positive negative
    نعم , نعم , أو رأيت " تشيما بارا ديسو " نعم، أو "تشيما بارا ديسو" كانت تلك التي شاهدتها أنا لم أرى "تشيما بارا ديسو".
    رجل وامرأة يجلسان على الشاطئ بينما تغرب الشمس هناك رجل وامرأة يجلسان على الشاطئ إنهم يشاهدون شروق الشمس
    كيف أسيطر على غضبي؟ ما هي أفضل طريقة للسيطرة على الغضب؟ كيف أعرف إن كانت زوجتي تخونني؟
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "MultipleNegativesRankingLoss",
        "matryoshka_dims": [
            768,
            512,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            1,
            1,
            1,
            1
        ],
        "n_dims_per_step": -1
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • warmup_ratio: 0.1
  • fp16: True
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • learning_rate: 2e-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: 5
  • 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: 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, '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: 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_eval_metrics: False
  • eval_on_start: False
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss loss
0.2133 500 1.4163 0.3134
0.4266 1000 0.3306 0.1912
0.6399 1500 0.2263 0.1527
0.8532 2000 0.1818 0.1297
1.0666 2500 0.1658 0.1167
1.2799 3000 0.1139 0.1040
1.4932 3500 0.0808 0.1018
1.7065 4000 0.0692 0.0959
1.9198 4500 0.058 0.0958
2.1331 5000 0.0653 0.0882
2.3464 5500 0.0503 0.0912
2.5597 6000 0.0338 0.0970
2.7730 6500 0.0363 0.0906
2.9863 7000 0.0375 0.0856
3.1997 7500 0.0401 0.0879
3.4130 8000 0.031 0.0848
3.6263 8500 0.0255 0.0938
3.8396 9000 0.0239 0.0858
4.0529 9500 0.0305 0.0840
4.2662 10000 0.0281 0.0833
4.4795 10500 0.0174 0.0840
4.6928 11000 0.0216 0.0882
4.9061 11500 0.022 0.0866

Framework Versions

  • Python: 3.10.13
  • Sentence Transformers: 3.0.1
  • Transformers: 4.42.3
  • PyTorch: 2.1.2
  • Accelerate: 0.32.1
  • Datasets: 2.20.0
  • Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

@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

@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}
}
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