leafxyz/arabic-ecom-cross-encoder-v4

CrossEncoder based on cross-encoder/mmarco-mMiniLMv2-L12-H384-v1

This is a Cross Encoder model finetuned from cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• —Model Type: Cross Encoder
• —Base model: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 <!-- at revision 1427fd652930e4ba29e8149678df786c240d8825 -->
• —Maximum Sequence Length: 128 tokens
• —Number of Output Labels: 1 label
• —Supported Modality: Text <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Documentation: Cross Encoder Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'})
)

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("leafxyz/arabic-ecom-cross-encoder-v4")
# Get scores for pairs of inputs
pairs = [
    ['اطعمة معلبة', 'درب شتورة حمص طحينة بالفلفل الحار - 400 غ'],
    ['عطر بيبي روج', 'زيت للأطفال \u200f- Babirose'],
    ['قرص فنش', 'سياقة كونتيسا - 40 سم'],
    ['فمّا قرفة مطحونة', 'زنجبيل مطحون الخليج - 70 غ'],
    ['كيرود', 'كيرود علبة هندسية لون ازرق - 8 قطع'],
]
scores = model.predict(pairs)
print(scores)
# [ 0.6948 -4.2344 -3.0059 -5.4609 -0.0091]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'اطعمة معلبة',
    [
        'درب شتورة حمص طحينة بالفلفل الحار - 400 غ',
        'زيت للأطفال \u200f- Babirose',
        'سياقة كونتيسا - 40 سم',
        'زنجبيل مطحون الخليج - 70 غ',
        'كيرود علبة هندسية لون ازرق - 8 قطع',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 334,256 training samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• —Approximate statistics based on the first 100 samples: | | sentence1 | sentence2 | label | |:---------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | <ul><li>min: 3 tokens</li><li>mean: 7.34 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.17 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.68</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:---------------------------|:------------------------------------------------------|:-----------------| | <code>أواني طبخ</code> | <code>شنقال ستيل لفاف</code> | <code>0.0</code> | | <code>ساعات نسائية</code> | <code>خاتم شي ان - DB032</code> | <code>0.0</code> | | <code>لوشن البابايا</code> | <code>لوشن البابايا لتبييض اليدين والجسم - RDL</code> | <code>1.0</code> |
• —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 6,822 evaluation samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• —Approximate statistics based on the first 100 samples: | | sentence1 | sentence2 | label | |:---------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | <ul><li>min: 4 tokens</li><li>mean: 8.0 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.86 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:--------------------------|:-------------------------------------------------------|:-----------------| | <code>اطعمة معلبة</code> | <code>درب شتورة حمص طحينة بالفلفل الحار - 400 غ</code> | <code>1.0</code> | | <code>عطر بيبي روج</code> | <code>زيت للأطفال ‏- Babirose</code> | <code>0.0</code> | | <code>قرص فنش</code> | <code>سياقة كونتيسا - 40 سم</code> | <code>0.0</code> |
• —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 32
• —num_train_epochs: 2
• —learning_rate: 2e-05
• —warmup_steps: 0.1
• —fp16: True
• —per_device_eval_batch_size: 32

All Hyperparameters

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

• —per_device_train_batch_size: 32
• —num_train_epochs: 2
• —max_steps: -1
• —learning_rate: 2e-05
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_steps: 0.1
• —optim: adamwtorchfused
• —optim_args: None
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —optim_target_modules: None
• —gradient_accumulation_steps: 1
• —average_tokens_across_devices: True
• —max_grad_norm: 1.0
• —label_smoothing_factor: 0.0
• —bf16: False
• —fp16: True
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —use_liger_kernel: False
• —liger_kernel_config: None
• —use_cache: False
• —neftune_noise_alpha: None
• —torch_empty_cache_steps: None
• —auto_find_batch_size: False
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —include_num_input_tokens_seen: no
• —log_level: passive
• —log_level_replica: warning
• —disable_tqdm: False
• —project: huggingface
• —trackio_space_id: None
• —trackio_bucket_id: None
• —trackio_static_space_id: None
• —per_device_eval_batch_size: 32
• —prediction_loss_only: True
• —eval_on_start: False
• —eval_do_concat_batches: True
• —eval_use_gather_object: False
• —eval_accumulation_steps: None
• —include_for_metrics: []
• —batch_eval_metrics: False
• —save_only_model: False
• —save_on_each_node: False
• —enable_jit_checkpoint: False
• —push_to_hub: False
• —hub_private_repo: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_always_push: False
• —hub_revision: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —restore_callback_states_from_checkpoint: False
• —full_determinism: False
• —seed: 42
• —data_seed: None
• —use_cpu: False
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —dataloader_prefetch_factor: None
• —remove_unused_columns: True
• —label_names: None
• —train_sampling_strategy: random
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —ddp_static_graph: None
• —ddp_backend: None
• —ddp_timeout: 1800
• —fsdp: None
• —fsdp_config: None
• —deepspeed: None
• —debug: []
• —skip_memory_metrics: True
• —do_predict: False
• —resume_from_checkpoint: None
• —warmup_ratio: None
• —local_rank: -1
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

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

</details>

Training Time

• —Training: 27.5 minutes

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.6.0
• —Transformers: 5.13.1
• —PyTorch: 2.11.0+cu128
• —Accelerate: 1.14.0
• —Datasets: 4.0.0
• —Tokenizers: 0.22.2

Additional Resources

• —Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
• —Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
• —Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

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",
}

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