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AhmadAfles/my-first-AraGenre

sourceHugging Faceupdated 2mo agoView on Hugging Face

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Model Card

SentenceTransformer based on U4RASD/NeoAraBERT

This is a sentence-transformers model finetuned from U4RASD/NeoAraBERT. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

—Model Type: Sentence Transformer
—Base model: U4RASD/NeoAraBERT
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 768 dimensions
—Similarity Function: Cosine Similarity
—Supported Modality: Text

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): NeoWrapper(
    (model): NeoBERT(
      (encoder): Embedding(65000, 768, padding_idx=0)
      (transformer_encoder): ModuleList(
        (0-27): 28 x EncoderBlock(
          (qkv): Linear(in_features=768, out_features=2304, bias=False)
          (wo): Linear(in_features=768, out_features=768, bias=False)
          (ffn): SwiGLU(
            (w12): Linear(in_features=768, out_features=4096, bias=False)
            (w3): Linear(in_features=2048, out_features=768, bias=False)
          )
          (attention_norm): RMSNorm((768,), eps=1e-05, elementwise_affine=True)
          (ffn_norm): RMSNorm((768,), eps=1e-05, elementwise_affine=True)
        )
      )
      (layer_norm): RMSNorm((768,), eps=1e-05, elementwise_affine=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("sentence_transformers_model_id")
# Run inference
sentences = [
    'يرتبط تراجع التصنيف الائتماني للدولة بارتفاع نسبة الدين العام إلى الناتج المحلي الإجمالي، مما يرفع تكلفة الاقتراض مستقبلاً من الأسواق الدولية.',
    'إن تنامي ظاهرة التغير المناخي يفرض تكاليف باهظة على قطاع التأمين العالمي، الذي بدأ يعيد تقييم بوالص التأمين ضد الكوارث في المناطق الساحلية.',
    'أبهر استوديو يوفوتبل المتابعين بقدرته الفائقة على دمج الرسوم ثلاثية الأبعاد (CGI) مع الرسم التقليدي في أنمي قاتل الشياطين، ليقدم تجربة قتال بصرية غير مسبوقة.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8434, 0.1994],
#         [0.8434, 1.0000, 0.2142],
#         [0.1994, 0.2142, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 899 training samples
—Columns: <code>sentence0</code> and <code>sentence1</code>
—Approximate statistics based on the first 100 samples: | | sentence0 | sentence1 | |:---------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | <ul><li>min: 40 tokens</li><li>mean: 63.1 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 63.86 tokens</li><li>max: 95 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>الكتب في المكتبة دي بتنقل نفسها من رف لرف، دخلت لقيت كتاب عن 'اختفاء الأشخاص' مفتوح على الصفحة اللي مكتوب فيها اسمي بالظبط.</code> | <code>بَعْدَ مُرُورِ عَشْرِ سَنَوَاتٍ، عَادَ الرَّجُلُ لِيَجِدَ كُلَّ شَيْءٍ فِي مَكَانِهِ بِدِقَّةٍ؛ لَا غُبَارَ عَلَى الأَثَاثِ، وَلَا أَثَرَ لِأَيِّ بَشَرٍ فِيهِ.</code> | | <code>الأسعار كل يوم في الطالع، والمرتب زي ما هو مش راضي يتحرك. المواطن البسيط مبقاش ملاحق على مصاريف الأكل والشرب والكهربا.</code> | <code>الحكاية مش حكاية فلوس وبس، الفكرة كلها في إدارة الموارد وصناعة بدائل محليّة عشان نقدر نستغني عن الاستيراد اللي بياكل الأخضر واليابس.</code> | | <code>وفي نهاية مطافنا الاستشاري لهذا الأسبوع، تذكروا دائماً أن السلام الداخلي يبدأ من التصالح مع الماضي وتقبل الأمور التي لا نملك القدرة على تغييرها.</code> | <code>لو ابنك المراهق بدأ يتغير ويبعد عنكم، بلاش أسلوب التحقيق والزعق، صادقه واسمعه أكتر ما بتتكلم، خليه يحس إن البيت هو أمانه مش مكان للمحاكمة.</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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

—per_device_train_batch_size: 16
—per_device_eval_batch_size: 16
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

—overwrite_output_dir: False
—do_predict: False
—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
—torch_empty_cache_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
—num_train_epochs: 3
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: {}
—warmup_ratio: 0.0
—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: 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, 'nonblocking': False, 'gradientaccumulationkwargs': 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: None
—hub_always_push: False
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—include_inputs_for_metrics: False
—include_for_metrics: []
—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
—use_liger_kernel: False
—eval_use_gather_object: False
—average_tokens_across_devices: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Time

—Training: 3.6 minutes

Framework Versions

—Python: 3.12.13
—Sentence Transformers: 5.5.1
—Transformers: 4.49.0
—PyTorch: 2.11.0+cu128
—Accelerate: 1.14.0
—Datasets: 4.0.0
—Tokenizers: 0.21.0

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",
}

MultipleNegativesRankingLoss

bibtex

@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},
}

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