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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})
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("AbderrahmanSkiredj1/arabic_text_embedding_sts_arabertv02_arabicnlitriplet")
5# Run inference
6sentences = [
7 'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
8 'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
9 'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 768]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| anchor | positive | negative |
|---|---|---|
شخص على حصان يقفز فوق طائرة معطلة | شخص في الهواء الطلق، على حصان. | شخص في مطعم، يطلب عجة. |
أطفال يبتسمون و يلوحون للكاميرا | هناك أطفال حاضرون | الاطفال يتجهمون |
صبي يقفز على لوح التزلج في منتصف الجسر الأحمر. | الفتى يقوم بخدعة التزلج | الصبي يتزلج على الرصيف |
MatryoshkaLoss with these parameters:
1{
2 "loss": "MultipleNegativesRankingLoss",
3 "matryoshka_dims": [
4 768,
5 512,
6 256,
7 128,
8 64
9 ],
10 "matryoshka_weights": [
11 1,
12 1,
13 1,
14 1,
15 1
16 ],
17 "n_dims_per_step": -1
18}anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| anchor | positive | negative |
|---|---|---|
امرأتان يتعانقان بينما يحملان حزمة | إمرأتان يحملان حزمة | الرجال يتشاجرون خارج مطعم |
طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة. | طفلين يرتديان قميصاً مرقماً يغسلون أيديهم | طفلين يرتديان سترة يذهبان إلى المدرسة |
رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس | رجل يبيع الدونات لعميل | امرأة تشرب قهوتها في مقهى صغير |
MatryoshkaLoss with these parameters:
1{
2 "loss": "MultipleNegativesRankingLoss",
3 "matryoshka_dims": [
4 768,
5 512,
6 256,
7 128,
8 64
9 ],
10 "matryoshka_weights": [
11 1,
12 1,
13 1,
14 1,
15 1
16 ],
17 "n_dims_per_step": -1
18}per_device_train_batch_size: 64per_device_eval_batch_size: 64learning_rate: 1e-06num_train_epochs: 10warmup_ratio: 0.1fp16: Truebatch_sampler: no_duplicatesoverwrite_output_dir: Falsedo_predict: Falseprediction_loss_only: Trueper_device_train_batch_size: 64per_device_eval_batch_size: 64per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonelearning_rate: 1e-06weight_decay: 0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 10max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: 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}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: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falsefp16_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: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportional| Epoch | Step | Training Loss |
|---|---|---|
| 0.0183 | 40 | 10.8122 |
| 0.0367 | 80 | 10.8841 |
| 0.0550 | 120 | 10.0068 |
| 0.0734 | 160 | 9.925 |
| 0.0917 | 200 | 9.2169 |
| 0.1101 | 240 | 8.8431 |
| 0.1284 | 280 | 8.6185 |
| 0.1468 | 320 | 8.8141 |
| 0.1651 | 360 | 8.0517 |
| 0.1835 | 400 | 7.9315 |
| 0.2018 | 440 | 7.7502 |
| 0.2202 | 480 | 7.432 |
| 0.2385 | 520 | 7.4885 |
| 0.2569 | 560 | 6.8975 |
| 0.2752 | 600 | 6.9895 |
| 0.2936 | 640 | 6.5074 |
| 0.3119 | 680 | 6.3892 |
| 0.3303 | 720 | 6.6392 |
| 0.3486 | 760 | 6.235 |
| 0.3670 | 800 | 5.95 |
| 0.3853 | 840 | 5.7161 |
| 0.4037 | 880 | 5.6532 |
| 0.4220 | 920 | 5.5386 |
| 0.4404 | 960 | 5.7382 |
| 0.4587 | 1000 | 5.3164 |
| 0.4771 | 1040 | 5.1571 |
| 0.4954 | 1080 | 4.9178 |
| 0.5138 | 1120 | 5.0309 |
| 0.5321 | 1160 | 5.0867 |
| 0.5505 | 1200 | 5.1403 |
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{kusupati2024matryoshka,
2 title={Matryoshka Representation Learning},
3 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},
4 year={2024},
5 eprint={2205.13147},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG}
8}1@misc{henderson2017efficient,
2 title={Efficient Natural Language Response Suggestion for Smart Reply},
3 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},
4 year={2017},
5 eprint={1705.00652},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}