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BERT base (UBC V2) trained on 500k Arabic NLI triplets

Name: sbert_ar_nli_500k_ubc_norm
Availability: InStock

This is a sentence-transformers model finetuned from UBC-NLP/ARBERTv2. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: UBC-NLP/ARBERTv2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Language: ar
License: apache-2.0

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'لماذا رائحة الغسيل بعد الغسيل',
    'كيفية التخلص من الرائحة: إذا تم اكتشافها على الفور ، فعادة ما يكون غسل الملابس مرة أخرى هو كل ما يلزم لإزالة الروائح الكريهة. إذا لم ينجح ذلك وما زلت تواجه مشاكل ، جرب إحدى الطرق التالية: اغسل مرة أخرى ولكن هذه المرة أضف كوبًا واحدًا من الخل إلى الحمولة (جنبًا إلى جنب مع منظف الغسيل).',
    'حافظ على ملابسك منتعشة وجافة بالمجفف الكهربائي من سيرز. عندما يدور يوم الغسيل ، يمكنك الاعتماد على الأداء الفعال للمجفف الكهربائي. يحتوي سيرز على مجففات تناسب ديكور أي غرفة غسيل. من الفولاذ المقاوم للصدأ إلى تشطيبات الأونيكس ، يسهل تنسيق هذا الجهاز الأنيق مع الغسالة التي تختارها.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.016	250	2.4152	-
0.032	500	1.24	-
0.048	750	1.0238	-
0.064	1000	0.929	-
0.08	1250	0.8268	-
0.096	1500	0.8117	-
0.112	1750	0.7486	-
0.128	2000	0.7053	-
0.144	2250	0.7131	-
0.16	2500	0.7003	-
0.176	2750	0.6735	-
0.192	3000	0.6548	-
0.208	3250	0.63	-
0.224	3500	0.6037	-
0.24	3750	0.6149	-
0.256	4000	0.5545	-
0.272	4250	0.5385	-
0.288	4500	0.5413	-
0.304	4750	0.5217	-
0.32	5000	0.4884	0.4664
0.336	5250	0.5052	-
0.352	5500	0.5239	-
0.368	5750	0.5145	-
0.384	6000	0.4707	-
0.4	6250	0.4514	-
0.416	6500	0.42	-
0.432	6750	0.4747	-
0.448	7000	0.4798	-
0.464	7250	0.4443	-
0.48	7500	0.4402	-
0.496	7750	0.411	-
0.512	8000	0.4546	-
0.528	8250	0.4428	-
0.544	8500	0.4293	-
0.56	8750	0.4052	-
0.576	9000	0.3993	-
0.592	9250	0.3971	-
0.608	9500	0.4246	-
0.624	9750	0.3995	-
0.64	10000	0.4087	0.3428
0.656	10250	0.3955	-
0.672	10500	0.3878	-
0.688	10750	0.3896	-
0.704	11000	0.3535	-
0.72	11250	0.3809	-
0.736	11500	0.3502	-
0.752	11750	0.3558	-
0.768	12000	0.3626	-
0.784	12250	0.3607	-
0.8	12500	0.3775	-
0.816	12750	0.3458	-
0.832	13000	0.3498	-
0.848	13250	0.3618	-
0.864	13500	0.3617	-
0.88	13750	0.3529	-
0.896	14000	0.3285	-
0.912	14250	0.3379	-
0.928	14500	0.336	-
0.944	14750	0.3402	-
0.96	15000	0.3391	0.2951
0.976	15250	0.3663	-
0.992	15500	0.3461	-

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.2.1
Transformers: 4.44.2
PyTorch: 2.4.0
Accelerate: 0.34.2
Datasets: 3.0.0
Tokenizers: 0.19.1

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",
}

Matryoshka2dLoss

bibtex

@misc{li20242d,
    title={2D Matryoshka Sentence Embeddings},
    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
    year={2024},
    eprint={2402.14776},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

akhooli/sbert_ar_nli_500k_ubc_norm

BERT base (UBC V2) trained on 500k Arabic NLI triplets

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

Matryoshka2dLoss

MatryoshkaLoss

MultipleNegativesRankingLoss

akhooli/
sbert_ar_nli_500k_ubc_norm