CrossEncoder based on aubmindlab/bert-base-arabertv02

This is a Cross Encoder model finetuned from aubmindlab/bert-base-arabertv02 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 Sources

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 ๐Ÿค— Hub
model = CrossEncoder("yoriis/checkpoint")
# Get scores for pairs of texts
pairs = [
    ['ูƒู… ุนุงู…ุงู‹ ุญูƒู… ุงู„ุณู„ุทุงู† ุณู„ูŠู…ุงู† ุงู„ู‚ุงู†ูˆู†ูŠ ุงู„ุฏูˆู„ุฉ ุงู„ุนุซู…ุงู†ูŠุฉุŸ', 'ุดุจู‡ ุฌุฒูŠุฑุฉ ุชุงู…ุงู†  () ู‡ูŠ ุดุจู‡ ุฌุฒูŠุฑุฉ ุชู‚ุน ููŠ ุฑูˆุณูŠุง ููŠ ุฅู‚ู„ูŠู… ูƒุฑุงุณู†ูˆุฏุงุฑ ูƒุฑุงูŠ.'],
    ['ู…ู† ู‡ูˆ ุนู…ุฑ ุงู„ุฎูŠุงู…ุŸ', 'ุงู„ุฑูŽู‘ุงุบูุจ ุงู„ุฃูŽุตู’ููŽู‡ูŽุงู†ูŠ (ุชูˆููŠ 502 ู‡ู€ / 1108 ู…) ู‡ูˆ ุฃุฏูŠุจ ูˆุนุงู„ู…ุŒ ุฃุตู„ู‡ ู…ู† ุฃุตูู‡ุงู†ุŒ ูˆุนุงุด ุจุจุบุฏุงุฏ. ุฃู„ู ุนุฏุฉ ูƒุชุจ ููŠ ุงู„ุชูุณูŠุฑ ูˆุงู„ุฃุฏุจ ูˆุงู„ุจู„ุงุบุฉ.[1]'],
    ['ู…ุง ู‡ู‰ ุทุฑูŠู‚ุฉ ุชูˆุฒูŠุน ุงู„ุฐูŠู„ ุŸ', 'ููŠ ุงู„ุฅุญุตุงุก ูˆุงู„ุฃุนู…ุงู„ ุงู„ุชุฌุงุฑูŠุฉ ุŒ ูŠู…ุซู„ ุงู„ุฐูŠู„ ุงู„ุทูˆูŠู„ ู„ุจุนุถ ุชูˆุฒูŠุนุงุช ุงู„ุฃุฑู‚ุงู… ุฌุฒุกู‹ุง ู…ู† ุงู„ุชูˆุฒูŠุน ุจุนุฏุฏ ูƒุจูŠุฑ ู…ู† ุงู„ุชูˆุงุฌุฏุงุช ุจุนูŠุฏู‹ุง ุนู† "ุงู„ุฑุฃุณ" ุฃูˆ ุงู„ุฌุฒุก ุงู„ู…ุฑูƒุฒูŠ ู…ู† ุงู„ุชูˆุฒูŠุน. ูŠู…ูƒู† ุฃู† ูŠุชุถู…ู† ุงู„ุชูˆุฒูŠุน ุดุนุจูŠุฉ ุŒ ูˆุฃุนุฏุงุฏู‹ุง ุนุดูˆุงุฆูŠุฉ ู„ูˆู‚ุงุฆุน ุฃุญุฏุงุซ ุฐุงุช ุงุญุชู…ุงู„ุงุช ู…ุฎุชู„ูุฉ ุŒ ุฅู„ุฎ. ุบุงู„ุจุงู‹ ู…ุง ูŠุณุชุฎุฏู… ุงู„ู…ุตุทู„ุญ ุจุดูƒู„ ูุถูุงุถ ุŒ ุจุฏูˆู† ุชุนุฑูŠู ุฃูˆ ุชุนุฑูŠู ุชุนุณููŠ ุŒ ู„ูƒู† ุงู„ุชุนุงุฑูŠู ุงู„ุฏู‚ูŠู‚ุฉ ู…ู…ูƒู†ุฉ.'],
    ['ุฃูŠู† ูƒุงู†ุช ุชู‚ุงู… ุจุทูˆู„ุฉ ูƒุฃุณ ุงู„ุนุงู„ู… ุงู„ู…ุตุบุฑุฉ ู„ู„ุฃู†ุฏูŠุฉุŸ', 'ุฃูู‚ูŠู… ูƒุฃุณ ุงู„ุนุงู„ู… ู„ู„ุฃู†ุฏูŠุฉ ู„ุฃูˆู„ ู…ุฑุฉ ููŠ 2000 ูˆู„ู… ุชู‚ู… ุจูŠู† 2001 ูˆ2004 ุจุณุจุจ ุงู†ู‡ูŠุงุฑ ุดุฑูŠูƒุฉ ุงู„ููŠูุง ุงู„ุชุณูˆูŠู‚ูŠุฉ. ุชูู‚ุงู… ุงู„ุจุทูˆู„ุฉ ูƒู„ ุณู†ุฉ ู…ู†ุฐ 2005. ุงุณุชุถุงู ุงู„ุจุทูˆู„ุฉูŽ ุงู„ุจุฑุงุฒูŠู„ ูˆุงู„ูŠุงุจุงู† ูˆุงู„ุฅู…ุงุฑุงุช ูˆุงู„ู…ุบุฑุจ.'],
    ['ูƒู… ู…ุฏูŠู†ุฉ ุชุญุชูˆูŠ ุฑูˆู…ุงู†ูŠุงุŸ', 'ูˆู„ุงูŠุฉ ู…ูŠู„ุฉ ุชู‚ุน ุจุงู„ุดู…ุงู„ ุงู„ุดุฑู‚ูŠ ุงู„ุฌุฒุงุฆุฑูŠ ุชุญุฏู‡ุง ุดุฑู‚ุง ูˆู„ุงูŠุฉ ู‚ุณู†ุทูŠู†ุฉ  ูˆุบุฑุจุง ูˆู„ุงูŠุฉ ุณุทูŠู ูˆูˆู„ุงูŠุฉ ุฌูŠุฌู„ ูˆุฌู†ูˆุจุง ูˆู„ุงูŠุฉ ุฃู… ุงู„ุจูˆุงู‚ูŠ ูˆูˆู„ุงูŠุฉ ุจุงุชู†ุฉ  ูˆุดู…ุงู„ุง ูˆู„ุงูŠุฉ ุฌูŠุฌู„ ูˆูˆู„ุงูŠุฉ ุณูƒูŠูƒุฏุฉ  ุชุจู„ุบ ู…ุณุงุญุชู‡ุง  3,407\xa0ูƒู…ยฒ ุจุชุนุฏุงุฏ ุณูƒุงู†ูŠ ู‚ุฏู‘ุฑ(ุณู†ุฉ 2008) ุจู€: 766,886 ู†ุณู…ุฉ...ุฃู…ุง ุงู„ูƒุซุงูุฉ ุงู„ุณูƒุงู†ูŠุฉ ูุจู„ุบุช 225 ู†ุณู…ุฉ/ูƒู…ยฒ ููŠ ู†ูุณ ุงู„ุณู†ุฉ.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'ูƒู… ุนุงู…ุงู‹ ุญูƒู… ุงู„ุณู„ุทุงู† ุณู„ูŠู…ุงู† ุงู„ู‚ุงู†ูˆู†ูŠ ุงู„ุฏูˆู„ุฉ ุงู„ุนุซู…ุงู†ูŠุฉุŸ',
    [
        'ุดุจู‡ ุฌุฒูŠุฑุฉ ุชุงู…ุงู†  () ู‡ูŠ ุดุจู‡ ุฌุฒูŠุฑุฉ ุชู‚ุน ููŠ ุฑูˆุณูŠุง ููŠ ุฅู‚ู„ูŠู… ูƒุฑุงุณู†ูˆุฏุงุฑ ูƒุฑุงูŠ.',
        'ุงู„ุฑูŽู‘ุงุบูุจ ุงู„ุฃูŽุตู’ููŽู‡ูŽุงู†ูŠ (ุชูˆููŠ 502 ู‡ู€ / 1108 ู…) ู‡ูˆ ุฃุฏูŠุจ ูˆุนุงู„ู…ุŒ ุฃุตู„ู‡ ู…ู† ุฃุตูู‡ุงู†ุŒ ูˆุนุงุด ุจุจุบุฏุงุฏ. ุฃู„ู ุนุฏุฉ ูƒุชุจ ููŠ ุงู„ุชูุณูŠุฑ ูˆุงู„ุฃุฏุจ ูˆุงู„ุจู„ุงุบุฉ.[1]',
        'ููŠ ุงู„ุฅุญุตุงุก ูˆุงู„ุฃุนู…ุงู„ ุงู„ุชุฌุงุฑูŠุฉ ุŒ ูŠู…ุซู„ ุงู„ุฐูŠู„ ุงู„ุทูˆูŠู„ ู„ุจุนุถ ุชูˆุฒูŠุนุงุช ุงู„ุฃุฑู‚ุงู… ุฌุฒุกู‹ุง ู…ู† ุงู„ุชูˆุฒูŠุน ุจุนุฏุฏ ูƒุจูŠุฑ ู…ู† ุงู„ุชูˆุงุฌุฏุงุช ุจุนูŠุฏู‹ุง ุนู† "ุงู„ุฑุฃุณ" ุฃูˆ ุงู„ุฌุฒุก ุงู„ู…ุฑูƒุฒูŠ ู…ู† ุงู„ุชูˆุฒูŠุน. ูŠู…ูƒู† ุฃู† ูŠุชุถู…ู† ุงู„ุชูˆุฒูŠุน ุดุนุจูŠุฉ ุŒ ูˆุฃุนุฏุงุฏู‹ุง ุนุดูˆุงุฆูŠุฉ ู„ูˆู‚ุงุฆุน ุฃุญุฏุงุซ ุฐุงุช ุงุญุชู…ุงู„ุงุช ู…ุฎุชู„ูุฉ ุŒ ุฅู„ุฎ. ุบุงู„ุจุงู‹ ู…ุง ูŠุณุชุฎุฏู… ุงู„ู…ุตุทู„ุญ ุจุดูƒู„ ูุถูุงุถ ุŒ ุจุฏูˆู† ุชุนุฑูŠู ุฃูˆ ุชุนุฑูŠู ุชุนุณููŠ ุŒ ู„ูƒู† ุงู„ุชุนุงุฑูŠู ุงู„ุฏู‚ูŠู‚ุฉ ู…ู…ูƒู†ุฉ.',
        'ุฃูู‚ูŠู… ูƒุฃุณ ุงู„ุนุงู„ู… ู„ู„ุฃู†ุฏูŠุฉ ู„ุฃูˆู„ ู…ุฑุฉ ููŠ 2000 ูˆู„ู… ุชู‚ู… ุจูŠู† 2001 ูˆ2004 ุจุณุจุจ ุงู†ู‡ูŠุงุฑ ุดุฑูŠูƒุฉ ุงู„ููŠูุง ุงู„ุชุณูˆูŠู‚ูŠุฉ. ุชูู‚ุงู… ุงู„ุจุทูˆู„ุฉ ูƒู„ ุณู†ุฉ ู…ู†ุฐ 2005. ุงุณุชุถุงู ุงู„ุจุทูˆู„ุฉูŽ ุงู„ุจุฑุงุฒูŠู„ ูˆุงู„ูŠุงุจุงู† ูˆุงู„ุฅู…ุงุฑุงุช ูˆุงู„ู…ุบุฑุจ.',
        'ูˆู„ุงูŠุฉ ู…ูŠู„ุฉ ุชู‚ุน ุจุงู„ุดู…ุงู„ ุงู„ุดุฑู‚ูŠ ุงู„ุฌุฒุงุฆุฑูŠ ุชุญุฏู‡ุง ุดุฑู‚ุง ูˆู„ุงูŠุฉ ู‚ุณู†ุทูŠู†ุฉ  ูˆุบุฑุจุง ูˆู„ุงูŠุฉ ุณุทูŠู ูˆูˆู„ุงูŠุฉ ุฌูŠุฌู„ ูˆุฌู†ูˆุจุง ูˆู„ุงูŠุฉ ุฃู… ุงู„ุจูˆุงู‚ูŠ ูˆูˆู„ุงูŠุฉ ุจุงุชู†ุฉ  ูˆุดู…ุงู„ุง ูˆู„ุงูŠุฉ ุฌูŠุฌู„ ูˆูˆู„ุงูŠุฉ ุณูƒูŠูƒุฏุฉ  ุชุจู„ุบ ู…ุณุงุญุชู‡ุง  3,407\xa0ูƒู…ยฒ ุจุชุนุฏุงุฏ ุณูƒุงู†ูŠ ู‚ุฏู‘ุฑ(ุณู†ุฉ 2008) ุจู€: 766,886 ู†ุณู…ุฉ...ุฃู…ุง ุงู„ูƒุซุงูุฉ ุงู„ุณูƒุงู†ูŠุฉ ูุจู„ุบุช 225 ู†ุณู…ุฉ/ูƒู…ยฒ ููŠ ู†ูุณ ุงู„ุณู†ุฉ.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Metric Value
accuracy 0.9953
accuracy_threshold 0.9396
f1 0.993
f1_threshold 0.9252
precision 0.9949
recall 0.9911
average_precision 0.9991

Training Details

Training Dataset

Unnamed Dataset

  • Size: 42,460 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string float
    details
    • min: 10 characters
    • mean: 29.38 characters
    • max: 86 characters
    • min: 42 characters
    • mean: 474.79 characters
    • max: 3512 characters
    • min: 0.0
    • mean: 0.32
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    ูƒู… ุนุงู…ุงู‹ ุญูƒู… ุงู„ุณู„ุทุงู† ุณู„ูŠู…ุงู† ุงู„ู‚ุงู†ูˆู†ูŠ ุงู„ุฏูˆู„ุฉ ุงู„ุนุซู…ุงู†ูŠุฉุŸ ุดุจู‡ ุฌุฒูŠุฑุฉ ุชุงู…ุงู† () ู‡ูŠ ุดุจู‡ ุฌุฒูŠุฑุฉ ุชู‚ุน ููŠ ุฑูˆุณูŠุง ููŠ ุฅู‚ู„ูŠู… ูƒุฑุงุณู†ูˆุฏุงุฑ ูƒุฑุงูŠ. 0.0
    ู…ู† ู‡ูˆ ุนู…ุฑ ุงู„ุฎูŠุงู…ุŸ ุงู„ุฑูŽู‘ุงุบูุจ ุงู„ุฃูŽุตู’ููŽู‡ูŽุงู†ูŠ (ุชูˆููŠ 502 ู‡ู€ / 1108 ู…) ู‡ูˆ ุฃุฏูŠุจ ูˆุนุงู„ู…ุŒ ุฃุตู„ู‡ ู…ู† ุฃุตูู‡ุงู†ุŒ ูˆุนุงุด ุจุจุบุฏุงุฏ. ุฃู„ู ุนุฏุฉ ูƒุชุจ ููŠ ุงู„ุชูุณูŠุฑ ูˆุงู„ุฃุฏุจ ูˆุงู„ุจู„ุงุบุฉ.[1] 0.0
    ู…ุง ู‡ู‰ ุทุฑูŠู‚ุฉ ุชูˆุฒูŠุน ุงู„ุฐูŠู„ ุŸ ููŠ ุงู„ุฅุญุตุงุก ูˆุงู„ุฃุนู…ุงู„ ุงู„ุชุฌุงุฑูŠุฉ ุŒ ูŠู…ุซู„ ุงู„ุฐูŠู„ ุงู„ุทูˆูŠู„ ู„ุจุนุถ ุชูˆุฒูŠุนุงุช ุงู„ุฃุฑู‚ุงู… ุฌุฒุกู‹ุง ู…ู† ุงู„ุชูˆุฒูŠุน ุจุนุฏุฏ ูƒุจูŠุฑ ู…ู† ุงู„ุชูˆุงุฌุฏุงุช ุจุนูŠุฏู‹ุง ุนู† "ุงู„ุฑุฃุณ" ุฃูˆ ุงู„ุฌุฒุก ุงู„ู…ุฑูƒุฒูŠ ู…ู† ุงู„ุชูˆุฒูŠุน. ูŠู…ูƒู† ุฃู† ูŠุชุถู…ู† ุงู„ุชูˆุฒูŠุน ุดุนุจูŠุฉ ุŒ ูˆุฃุนุฏุงุฏู‹ุง ุนุดูˆุงุฆูŠุฉ ู„ูˆู‚ุงุฆุน ุฃุญุฏุงุซ ุฐุงุช ุงุญุชู…ุงู„ุงุช ู…ุฎุชู„ูุฉ ุŒ ุฅู„ุฎ. ุบุงู„ุจุงู‹ ู…ุง ูŠุณุชุฎุฏู… ุงู„ู…ุตุทู„ุญ ุจุดูƒู„ ูุถูุงุถ ุŒ ุจุฏูˆู† ุชุนุฑูŠู ุฃูˆ ุชุนุฑูŠู ุชุนุณููŠ ุŒ ู„ูƒู† ุงู„ุชุนุงุฑูŠู ุงู„ุฏู‚ูŠู‚ุฉ ู…ู…ูƒู†ุฉ. 1.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": null
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • num_train_epochs: 4
  • fp16: True

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
  • 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: 4
  • 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: 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: None
  • hub_always_push: False
  • hub_revision: None
  • 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
  • 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
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss eval_average_precision
0.1884 500 0.3474 0.9981
0.3768 1000 0.1324 0.9988
0.5652 1500 0.0712 0.9984
0.7536 2000 0.058 0.9981
0.9420 2500 0.0466 0.9989
1.0 2654 - 0.9988
1.1304 3000 0.0426 0.9989
1.3188 3500 0.0357 0.9989
1.5072 4000 0.0362 0.9988
1.6956 4500 0.0314 0.9992
1.8839 5000 0.0273 0.9990
2.0 5308 - 0.9991
2.0723 5500 0.0302 0.9991
2.2607 6000 0.0265 0.9990
2.4491 6500 0.0262 0.9991
2.6375 7000 0.0249 0.9991
2.8259 7500 0.0284 0.9991
3.0 7962 - 0.9991
3.0143 8000 0.0252 0.9991
3.2027 8500 0.023 0.9991
3.3911 9000 0.022 0.9991
3.5795 9500 0.0244 0.9991
3.7679 10000 0.0219 0.9991
3.9563 10500 0.021 0.9991
4.0 10616 - 0.9991

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 4.1.0
  • Transformers: 4.54.0
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.9.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.2

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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