CrossEncoder based on NAMAA-Space/GATE-Reranker-V1
This is a Cross Encoder model finetuned from NAMAA-Space/GATE-Reranker-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: NAMAA-Space/GATE-Reranker-V1
- 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/GTE-quqa-haqa")
# Get scores for pairs of texts
pairs = [
['ูู ุชุฑู ุงูุตูุงุฉ ุชูุงููุง ููุณูุง ูุจูุฑุฉ ู
ู ุงููุจุงุฆุฑุ ูู
ู ุงูุนูู
ุงุก ู
ู ูุงู ุจููุฑูุ ูู ูุฐุง ุงูุญูู
ูู ุชูุฌููู ู
ู ุงูุณูุฉ ุงููุจููุฉุ', 'ุญุฏูุซ ุทูุงุฑููู ุจููู ุดูููุงุจู ุฑุถู ุงููู ุนููุ ุนููู ุงููููุจูููู ๏ทบ ููุงูู: ยซุงูุฌูู
ูุนูุฉู ุญูููู ููุงุฌูุจู ุนูููู ููููู ู
ูุณูููู
ู ููู ุฌูู
ูุงุนูุฉู ุฅููููุง ุฃูุฑูุจูุนูุฉู: ุนูุจูุฏู ู
ูู
ููููููุ ุฃููู ุงู
ูุฑูุฃูุฉูุ ุฃููู ุตูุจููููุ ุฃููู ู
ูุฑููุถูยป. ุฑูุงู ุฃุจู ุฏุงูุฏ (1067)ุ ูุตุญุญู ุงูุฃูุจุงูู ูู ุฅุฑูุงุก ุงูุบููู (592)ุ ูุงููุงุฏุนู ูู ุงูุตุญูุญ ุงูู
ุณูุฏ (517) .'],
['ู
ู ูู ุงููุจู ุงูุฐู ูุงู ูุนู
ู ูุฌุงุฑุง ุ', 'ุนู ุฃุจู ุจู ูุนุจ ุฑุถู ุงููู ุนูู ูุงู: ยซุฅู ุฑุณูู ุงููู ๏ทบ ูุงู ููุชุฑ ููููุช ูุจู ุงูุฑููุนยป. ุฃุฎุฑุฌู ุงุจู ู
ุงุฌู.'],
['ู
ุง ุณุจุจ ูุฑุงููุฉ ุงูุตูุงุฉ ุนูู ุงูุณุฌูุงุฏ ุงูู
ุฒุฎุฑูุ', 'ุงุจู ุนุจุงุณ ุฑุถู ุงููู ุนูู ุนู ุงููุจู ๏ทบ ุฃูู ูุงู: (ู
ู ุณู
ุน ุงููุฏุงุก ููู
ูุฃุชูุ ููุง ุตูุงุฉ ูู ุฅูุง ู
ู ุนุฐุฑ). ุฃุฎุฑุฌู ุงุจู ู
ุงุฌู'],
['ู
ู ูู ุงูุตุญุงุจู ุงูุฐู ูุงู ููู ุงููุจู ๏ทบ: ยซู
ู ุฎูุฑ ุฐู ูู
ู ูุนูู ูุฌูู ู
ุณุญุฉ ู
ููยป ุ', 'ุญุฏูุซ ุฌูุฑููุฑ ุจูู ุนูุจูุฏู ุงููู ุงูุจูุฌูููููู ุฑุถู ุงููู ุนููุ ู
ูุง ุฑูุขููู ุฑูุณูููู ุงููู ๏ทบ ููุทูู ุฅููููุง ุชูุจูุณููู
ู ููู ููุฌูููู ููุงูู: ููููุงูู ุฑูุณูููู ุงููู ๏ทบ: ยซููุทูููุนู ุนูููููููู
ู ู
ููู ููุฐูุง ุงูุจูุงุจู ุฑูุฌููู ู
ููู ุฎูููุฑู ุฐูู ููู
ูููุ ุนูููู ููุฌููููู ู
ูุณูุญูุฉู ู
ูููููุ ููุทูููุนู ุฌูุฑููุฑู ุจููู ุนูุจูุฏู ุงูููยป. ููู ูู ู
ุณูุฏ ุงูุฅู
ุงู
ุฃุญู
ุฏ (19179)ุ ููู ูู ุงูุตุญูุญุฉ (3193)ุ ููู ุงูุตุญูุญ ุงูู
ุณูุฏ (262).'],
['ู
ุง ูุถู ุตูุงุฉ ุงููููุ', 'ุนููู ุฃูุจูู ููุฑูููุฑูุฉู ุฑุถู ุงููู ุนููุ ุฃูููู ุฑูุณูููู ุงููู ๏ทบ ููุงูู: ยซููููุณู ุงูุดููุฏููุฏู ุจูุงูุตููุฑูุนูุฉู ุฅููููู
ูุง ุงูุดููุฏููุฏู ุงูููุฐูู ููู
ููููู ููููุณููู ุนูููุฏู ุงูุบูุถูุจูยป. ุฑูุงู ุงูุจุฎุงุฑู (6114)ุ ูู
ุณูู
(2609).'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'ูู ุชุฑู ุงูุตูุงุฉ ุชูุงููุง ููุณูุง ูุจูุฑุฉ ู
ู ุงููุจุงุฆุฑุ ูู
ู ุงูุนูู
ุงุก ู
ู ูุงู ุจููุฑูุ ูู ูุฐุง ุงูุญูู
ูู ุชูุฌููู ู
ู ุงูุณูุฉ ุงููุจููุฉุ',
[
'ุญุฏูุซ ุทูุงุฑููู ุจููู ุดูููุงุจู ุฑุถู ุงููู ุนููุ ุนููู ุงููููุจูููู ๏ทบ ููุงูู: ยซุงูุฌูู
ูุนูุฉู ุญูููู ููุงุฌูุจู ุนูููู ููููู ู
ูุณูููู
ู ููู ุฌูู
ูุงุนูุฉู ุฅููููุง ุฃูุฑูุจูุนูุฉู: ุนูุจูุฏู ู
ูู
ููููููุ ุฃููู ุงู
ูุฑูุฃูุฉูุ ุฃููู ุตูุจููููุ ุฃููู ู
ูุฑููุถูยป. ุฑูุงู ุฃุจู ุฏุงูุฏ (1067)ุ ูุตุญุญู ุงูุฃูุจุงูู ูู ุฅุฑูุงุก ุงูุบููู (592)ุ ูุงููุงุฏุนู ูู ุงูุตุญูุญ ุงูู
ุณูุฏ (517) .',
'ุนู ุฃุจู ุจู ูุนุจ ุฑุถู ุงููู ุนูู ูุงู: ยซุฅู ุฑุณูู ุงููู ๏ทบ ูุงู ููุชุฑ ููููุช ูุจู ุงูุฑููุนยป. ุฃุฎุฑุฌู ุงุจู ู
ุงุฌู.',
'ุงุจู ุนุจุงุณ ุฑุถู ุงููู ุนูู ุนู ุงููุจู ๏ทบ ุฃูู ูุงู: (ู
ู ุณู
ุน ุงููุฏุงุก ููู
ูุฃุชูุ ููุง ุตูุงุฉ ูู ุฅูุง ู
ู ุนุฐุฑ). ุฃุฎุฑุฌู ุงุจู ู
ุงุฌู',
'ุญุฏูุซ ุฌูุฑููุฑ ุจูู ุนูุจูุฏู ุงููู ุงูุจูุฌูููููู ุฑุถู ุงููู ุนููุ ู
ูุง ุฑูุขููู ุฑูุณูููู ุงููู ๏ทบ ููุทูู ุฅููููุง ุชูุจูุณููู
ู ููู ููุฌูููู ููุงูู: ููููุงูู ุฑูุณูููู ุงููู ๏ทบ: ยซููุทูููุนู ุนูููููููู
ู ู
ููู ููุฐูุง ุงูุจูุงุจู ุฑูุฌููู ู
ููู ุฎูููุฑู ุฐูู ููู
ูููุ ุนูููู ููุฌููููู ู
ูุณูุญูุฉู ู
ูููููุ ููุทูููุนู ุฌูุฑููุฑู ุจููู ุนูุจูุฏู ุงูููยป. ููู ูู ู
ุณูุฏ ุงูุฅู
ุงู
ุฃุญู
ุฏ (19179)ุ ููู ูู ุงูุตุญูุญุฉ (3193)ุ ููู ุงูุตุญูุญ ุงูู
ุณูุฏ (262).',
'ุนููู ุฃูุจูู ููุฑูููุฑูุฉู ุฑุถู ุงููู ุนููุ ุฃูููู ุฑูุณูููู ุงููู ๏ทบ ููุงูู: ยซููููุณู ุงูุดููุฏููุฏู ุจูุงูุตููุฑูุนูุฉู ุฅููููู
ูุง ุงูุดููุฏููุฏู ุงูููุฐูู ููู
ููููู ููููุณููู ุนูููุฏู ุงูุบูุถูุจูยป. ุฑูุงู ุงูุจุฎุงุฑู (6114)ุ ูู
ุณูู
(2609).',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
Evaluation
Metrics
Cross Encoder Classification
- Dataset:
eval
- Evaluated with
CrossEncoderClassificationEvaluator
Metric | Value |
---|---|
accuracy | 0.9347 |
accuracy_threshold | 0.5419 |
f1 | 0.8599 |
f1_threshold | 0.5419 |
precision | 0.9278 |
recall | 0.8012 |
average_precision | 0.9188 |
Cross Encoder Classification
- Dataset:
eval
- Evaluated with
CrossEncoderClassificationEvaluator
Metric | Value |
---|---|
accuracy | 0.8665 |
accuracy_threshold | 0.602 |
f1 | 0.4424 |
f1_threshold | 0.113 |
precision | 0.4294 |
recall | 0.4562 |
average_precision | 0.4908 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 8,623 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: 9 characters
- mean: 35.96 characters
- max: 132 characters
- min: 39 characters
- mean: 286.62 characters
- max: 12356 characters
- min: 0.0
- mean: 0.16
- max: 1.0
- Samples:
sentence_0 sentence_1 label ูู ุชุฑู ุงูุตูุงุฉ ุชูุงููุง ููุณูุง ูุจูุฑุฉ ู ู ุงููุจุงุฆุฑุ ูู ู ุงูุนูู ุงุก ู ู ูุงู ุจููุฑูุ ูู ูุฐุง ุงูุญูู ูู ุชูุฌููู ู ู ุงูุณูุฉ ุงููุจููุฉุ
ุญุฏูุซ ุทูุงุฑููู ุจููู ุดูููุงุจู ุฑุถู ุงููู ุนููุ ุนููู ุงููููุจูููู ๏ทบ ููุงูู: ยซุงูุฌูู ูุนูุฉู ุญูููู ููุงุฌูุจู ุนูููู ููููู ู ูุณูููู ู ููู ุฌูู ูุงุนูุฉู ุฅููููุง ุฃูุฑูุจูุนูุฉู: ุนูุจูุฏู ู ูู ููููููุ ุฃููู ุงู ูุฑูุฃูุฉูุ ุฃููู ุตูุจููููุ ุฃููู ู ูุฑููุถูยป. ุฑูุงู ุฃุจู ุฏุงูุฏ (1067)ุ ูุตุญุญู ุงูุฃูุจุงูู ูู ุฅุฑูุงุก ุงูุบููู (592)ุ ูุงููุงุฏุนู ูู ุงูุตุญูุญ ุงูู ุณูุฏ (517) .
0.0
ู ู ูู ุงููุจู ุงูุฐู ูุงู ูุนู ู ูุฌุงุฑุง ุ
ุนู ุฃุจู ุจู ูุนุจ ุฑุถู ุงููู ุนูู ูุงู: ยซุฅู ุฑุณูู ุงููู ๏ทบ ูุงู ููุชุฑ ููููุช ูุจู ุงูุฑููุนยป. ุฃุฎุฑุฌู ุงุจู ู ุงุฌู.
0.0
ู ุง ุณุจุจ ูุฑุงููุฉ ุงูุตูุงุฉ ุนูู ุงูุณุฌูุงุฏ ุงูู ุฒุฎุฑูุ
ุงุจู ุนุจุงุณ ุฑุถู ุงููู ุนูู ุนู ุงููุจู ๏ทบ ุฃูู ูุงู: (ู ู ุณู ุน ุงููุฏุงุก ููู ูุฃุชูุ ููุง ุตูุงุฉ ูู ุฅูุง ู ู ุนุฐุฑ). ุฃุฎุฑุฌู ุงุจู ู ุงุฌู
0.0
- Loss:
BinaryCrossEntropyLoss
with these parameters:{ "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": null }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsnum_train_epochs
: 4fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 8per_device_eval_batch_size
: 8per_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
: proportionalrouter_mapping
: {}learning_rate_mapping
: {}
Training Logs
Epoch | Step | Training Loss | eval_average_precision |
---|---|---|---|
0.3298 | 500 | 0.4083 | 0.8871 |
0.6596 | 1000 | 0.2958 | 0.9043 |
0.9894 | 1500 | 0.2839 | 0.9092 |
1.0 | 1516 | - | 0.9091 |
1.3193 | 2000 | 0.2698 | 0.9129 |
1.6491 | 2500 | 0.2617 | 0.9152 |
1.9789 | 3000 | 0.2791 | 0.9163 |
2.0 | 3032 | - | 0.9160 |
2.3087 | 3500 | 0.2651 | 0.9159 |
2.6385 | 4000 | 0.2475 | 0.9172 |
2.9683 | 4500 | 0.264 | 0.9186 |
3.0 | 4548 | - | 0.9187 |
3.2982 | 5000 | 0.225 | 0.9180 |
3.6280 | 5500 | 0.2706 | 0.9186 |
3.9578 | 6000 | 0.2242 | 0.9188 |
4.0 | 6064 | - | 0.9188 |
0.4638 | 500 | 0.5074 | 0.4693 |
0.9276 | 1000 | 0.3909 | 0.4817 |
1.0 | 1078 | - | 0.4858 |
1.3915 | 1500 | 0.3806 | 0.4802 |
1.8553 | 2000 | 0.3638 | 0.4828 |
2.0 | 2156 | - | 0.4843 |
2.3191 | 2500 | 0.395 | 0.4828 |
2.7829 | 3000 | 0.347 | 0.4840 |
3.0 | 3234 | - | 0.4850 |
3.2468 | 3500 | 0.3614 | 0.4866 |
3.7106 | 4000 | 0.3483 | 0.4906 |
4.0 | 4312 | - | 0.4908 |
Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.0.0
- Transformers: 4.55.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.9.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
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
- 14
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
๐
Ask for provider support
Model tree for yoriis/GTE-quqa-haqa
Base model
aubmindlab/bert-base-arabertv02
Finetuned
NAMAA-Space/GATE-Reranker-V1
Evaluation results
- Accuracy on evalself-reported0.935
- Accuracy Threshold on evalself-reported0.542
- F1 on evalself-reported0.860
- F1 Threshold on evalself-reported0.542
- Precision on evalself-reported0.928
- Recall on evalself-reported0.801
- Average Precision on evalself-reported0.919
- Accuracy on evalself-reported0.867
- Accuracy Threshold on evalself-reported0.602
- F1 on evalself-reported0.442