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 Type: Cross Encoder
- Base model: aubmindlab/bert-base-arabertv02
- Maximum Sequence Length: 512 tokens
- Number of Output Labels: 1 label
Model Sources
- Documentation: Sentence Transformers Documentation
- Documentation: Cross Encoder Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Cross Encoders on Hugging Face
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 ๐ค Hub
model = CrossEncoder("yoriis/checkpoint")
# Get scores for pairs of texts
pairs = [
['ูู
ุนุงู
ุงู ุญูู
ุงูุณูุทุงู ุณููู
ุงู ุงููุงูููู ุงูุฏููุฉ ุงูุนุซู
ุงููุฉุ', 'ุดุจู ุฌุฒูุฑุฉ ุชุงู
ุงู () ูู ุดุจู ุฌุฒูุฑุฉ ุชูุน ูู ุฑูุณูุง ูู ุฅูููู
ูุฑุงุณููุฏุงุฑ ูุฑุงู.'],
['ู
ู ูู ุนู
ุฑ ุงูุฎูุงู
ุ', 'ุงูุฑููุงุบูุจ ุงูุฃูุตูููููุงูู (ุชููู 502 ูู / 1108 ู
) ูู ุฃุฏูุจ ูุนุงูู
ุ ุฃุตูู ู
ู ุฃุตููุงูุ ูุนุงุด ุจุจุบุฏุงุฏ. ุฃูู ุนุฏุฉ ูุชุจ ูู ุงูุชูุณูุฑ ูุงูุฃุฏุจ ูุงูุจูุงุบุฉ.[1]'],
['ู
ุง ูู ุทุฑููุฉ ุชูุฒูุน ุงูุฐูู ุ', 'ูู ุงูุฅุญุตุงุก ูุงูุฃุนู
ุงู ุงูุชุฌุงุฑูุฉ ุ ูู
ุซู ุงูุฐูู ุงูุทููู ูุจุนุถ ุชูุฒูุนุงุช ุงูุฃุฑูุงู
ุฌุฒุกูุง ู
ู ุงูุชูุฒูุน ุจุนุฏุฏ ูุจูุฑ ู
ู ุงูุชูุงุฌุฏุงุช ุจุนูุฏูุง ุนู "ุงูุฑุฃุณ" ุฃู ุงูุฌุฒุก ุงูู
ุฑูุฒู ู
ู ุงูุชูุฒูุน. ูู
ูู ุฃู ูุชุถู
ู ุงูุชูุฒูุน ุดุนุจูุฉ ุ ูุฃุนุฏุงุฏูุง ุนุดูุงุฆูุฉ ูููุงุฆุน ุฃุญุฏุงุซ ุฐุงุช ุงุญุชู
ุงูุงุช ู
ุฎุชููุฉ ุ ุฅูุฎ. ุบุงูุจุงู ู
ุง ูุณุชุฎุฏู
ุงูู
ุตุทูุญ ุจุดูู ูุถูุงุถ ุ ุจุฏูู ุชุนุฑูู ุฃู ุชุนุฑูู ุชุนุณูู ุ ููู ุงูุชุนุงุฑูู ุงูุฏูููุฉ ู
ู
ููุฉ.'],
['ุฃูู ูุงูุช ุชูุงู
ุจุทููุฉ ูุฃุณ ุงูุนุงูู
ุงูู
ุตุบุฑุฉ ููุฃูุฏูุฉุ', 'ุฃูููู
ูุฃุณ ุงูุนุงูู
ููุฃูุฏูุฉ ูุฃูู ู
ุฑุฉ ูู 2000 ููู
ุชูู
ุจูู 2001 ู2004 ุจุณุจุจ ุงูููุงุฑ ุดุฑููุฉ ุงููููุง ุงูุชุณููููุฉ. ุชููุงู
ุงูุจุทููุฉ ูู ุณูุฉ ู
ูุฐ 2005. ุงุณุชุถุงู ุงูุจุทููุฉู ุงูุจุฑุงุฒูู ูุงููุงุจุงู ูุงูุฅู
ุงุฑุงุช ูุงูู
ุบุฑุจ.'],
['ูู
ู
ุฏููุฉ ุชุญุชูู ุฑูู
ุงููุงุ', 'ููุงูุฉ ู
ููุฉ ุชูุน ุจุงูุดู
ุงู ุงูุดุฑูู ุงูุฌุฒุงุฆุฑู ุชุญุฏูุง ุดุฑูุง ููุงูุฉ ูุณูุทููุฉ ูุบุฑุจุง ููุงูุฉ ุณุทูู ูููุงูุฉ ุฌูุฌู ูุฌููุจุง ููุงูุฉ ุฃู
ุงูุจูุงูู ูููุงูุฉ ุจุงุชูุฉ ูุดู
ุงูุง ููุงูุฉ ุฌูุฌู ูููุงูุฉ ุณูููุฏุฉ ุชุจูุบ ู
ุณุงุญุชูุง 3,407\xa0ูู
ยฒ ุจุชุนุฏุงุฏ ุณูุงูู ูุฏูุฑ(ุณูุฉ 2008) ุจู: 766,886 ูุณู
ุฉ...ุฃู
ุง ุงููุซุงูุฉ ุงูุณูุงููุฉ ูุจูุบุช 225 ูุณู
ุฉ/ูู
ยฒ ูู ููุณ ุงูุณูุฉ.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'ูู
ุนุงู
ุงู ุญูู
ุงูุณูุทุงู ุณููู
ุงู ุงููุงูููู ุงูุฏููุฉ ุงูุนุซู
ุงููุฉุ',
[
'ุดุจู ุฌุฒูุฑุฉ ุชุงู
ุงู () ูู ุดุจู ุฌุฒูุฑุฉ ุชูุน ูู ุฑูุณูุง ูู ุฅูููู
ูุฑุงุณููุฏุงุฑ ูุฑุงู.',
'ุงูุฑููุงุบูุจ ุงูุฃูุตูููููุงูู (ุชููู 502 ูู / 1108 ู
) ูู ุฃุฏูุจ ูุนุงูู
ุ ุฃุตูู ู
ู ุฃุตููุงูุ ูุนุงุด ุจุจุบุฏุงุฏ. ุฃูู ุนุฏุฉ ูุชุจ ูู ุงูุชูุณูุฑ ูุงูุฃุฏุจ ูุงูุจูุงุบุฉ.[1]',
'ูู ุงูุฅุญุตุงุก ูุงูุฃุนู
ุงู ุงูุชุฌุงุฑูุฉ ุ ูู
ุซู ุงูุฐูู ุงูุทููู ูุจุนุถ ุชูุฒูุนุงุช ุงูุฃุฑูุงู
ุฌุฒุกูุง ู
ู ุงูุชูุฒูุน ุจุนุฏุฏ ูุจูุฑ ู
ู ุงูุชูุงุฌุฏุงุช ุจุนูุฏูุง ุนู "ุงูุฑุฃุณ" ุฃู ุงูุฌุฒุก ุงูู
ุฑูุฒู ู
ู ุงูุชูุฒูุน. ูู
ูู ุฃู ูุชุถู
ู ุงูุชูุฒูุน ุดุนุจูุฉ ุ ูุฃุนุฏุงุฏูุง ุนุดูุงุฆูุฉ ูููุงุฆุน ุฃุญุฏุงุซ ุฐุงุช ุงุญุชู
ุงูุงุช ู
ุฎุชููุฉ ุ ุฅูุฎ. ุบุงูุจุงู ู
ุง ูุณุชุฎุฏู
ุงูู
ุตุทูุญ ุจุดูู ูุถูุงุถ ุ ุจุฏูู ุชุนุฑูู ุฃู ุชุนุฑูู ุชุนุณูู ุ ููู ุงูุชุนุงุฑูู ุงูุฏูููุฉ ู
ู
ููุฉ.',
'ุฃูููู
ูุฃุณ ุงูุนุงูู
ููุฃูุฏูุฉ ูุฃูู ู
ุฑุฉ ูู 2000 ููู
ุชูู
ุจูู 2001 ู2004 ุจุณุจุจ ุงูููุงุฑ ุดุฑููุฉ ุงููููุง ุงูุชุณููููุฉ. ุชููุงู
ุงูุจุทููุฉ ูู ุณูุฉ ู
ูุฐ 2005. ุงุณุชุถุงู ุงูุจุทููุฉู ุงูุจุฑุงุฒูู ูุงููุงุจุงู ูุงูุฅู
ุงุฑุงุช ูุงูู
ุบุฑุจ.',
'ููุงูุฉ ู
ููุฉ ุชูุน ุจุงูุดู
ุงู ุงูุดุฑูู ุงูุฌุฒุงุฆุฑู ุชุญุฏูุง ุดุฑูุง ููุงูุฉ ูุณูุทููุฉ ูุบุฑุจุง ููุงูุฉ ุณุทูู ูููุงูุฉ ุฌูุฌู ูุฌููุจุง ููุงูุฉ ุฃู
ุงูุจูุงูู ูููุงูุฉ ุจุงุชูุฉ ูุดู
ุงูุง ููุงูุฉ ุฌูุฌู ูููุงูุฉ ุณูููุฏุฉ ุชุจูุบ ู
ุณุงุญุชูุง 3,407\xa0ูู
ยฒ ุจุชุนุฏุงุฏ ุณูุงูู ูุฏูุฑ(ุณูุฉ 2008) ุจู: 766,886 ูุณู
ุฉ...ุฃู
ุง ุงููุซุงูุฉ ุงูุณูุงููุฉ ูุจูุบุช 225 ูุณู
ุฉ/ูู
ยฒ ูู ููุณ ุงูุณูุฉ.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
Evaluation
Metrics
Cross Encoder Classification
- Dataset:
eval - Evaluated with
CrossEncoderClassificationEvaluator
| 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, andlabel - 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 ูู ุนุงู ุงู ุญูู ุงูุณูุทุงู ุณููู ุงู ุงููุงูููู ุงูุฏููุฉ ุงูุนุซู ุงููุฉุุดุจู ุฌุฒูุฑุฉ ุชุงู ุงู () ูู ุดุจู ุฌุฒูุฑุฉ ุชูุน ูู ุฑูุณูุง ูู ุฅูููู ูุฑุงุณููุฏุงุฑ ูุฑุงู.0.0ู ู ูู ุนู ุฑ ุงูุฎูุงู ุุงูุฑููุงุบูุจ ุงูุฃูุตูููููุงูู (ุชููู 502 ูู / 1108 ู ) ูู ุฃุฏูุจ ูุนุงูู ุ ุฃุตูู ู ู ุฃุตููุงูุ ูุนุงุด ุจุจุบุฏุงุฏ. ุฃูู ุนุฏุฉ ูุชุจ ูู ุงูุชูุณูุฑ ูุงูุฃุฏุจ ูุงูุจูุงุบุฉ.[1]0.0ู ุง ูู ุทุฑููุฉ ุชูุฒูุน ุงูุฐูู ุูู ุงูุฅุญุตุงุก ูุงูุฃุนู ุงู ุงูุชุฌุงุฑูุฉ ุ ูู ุซู ุงูุฐูู ุงูุทููู ูุจุนุถ ุชูุฒูุนุงุช ุงูุฃุฑูุงู ุฌุฒุกูุง ู ู ุงูุชูุฒูุน ุจุนุฏุฏ ูุจูุฑ ู ู ุงูุชูุงุฌุฏุงุช ุจุนูุฏูุง ุนู "ุงูุฑุฃุณ" ุฃู ุงูุฌุฒุก ุงูู ุฑูุฒู ู ู ุงูุชูุฒูุน. ูู ูู ุฃู ูุชุถู ู ุงูุชูุฒูุน ุดุนุจูุฉ ุ ูุฃุนุฏุงุฏูุง ุนุดูุงุฆูุฉ ูููุงุฆุน ุฃุญุฏุงุซ ุฐุงุช ุงุญุชู ุงูุงุช ู ุฎุชููุฉ ุ ุฅูุฎ. ุบุงูุจุงู ู ุง ูุณุชุฎุฏู ุงูู ุตุทูุญ ุจุดูู ูุถูุงุถ ุ ุจุฏูู ุชุนุฑูู ุฃู ุชุนุฑูู ุชุนุณูู ุ ููู ุงูุชุนุงุฑูู ุงูุฏูููุฉ ู ู ููุฉ.1.0 - Loss:
BinaryCrossEntropyLosswith these parameters:{ "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": null }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 16per_device_eval_batch_size: 16num_train_epochs: 4fp16: True
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 4max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Truefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsehub_revision: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseliger_kernel_config: Noneeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_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",
}
- Downloads last month
- 1
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
๐
Ask for provider support
Model tree for yoriis/checkpoint
Base model
aubmindlab/bert-base-arabertv02Evaluation results
- Accuracy on evalself-reported0.995
- Accuracy Threshold on evalself-reported0.940
- F1 on evalself-reported0.993
- F1 Threshold on evalself-reported0.925
- Precision on evalself-reported0.995
- Recall on evalself-reported0.991
- Average Precision on evalself-reported0.999