Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:853827
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+datasets: []
+widget:
+- source_sentence: كيف يمكنني أن أخسر الوزن من خلال النظام الغذائي وتناول الطعام الصحي؟
+  sentences:
+  - هل يمكن أن نحصل على (آركتشيب) في (بيغ فور) بعد إزالة كلتا المجموعتين بشكل منفصل؟
+  - ما هي سلسلة المطاعم الأمريكية الموجودة في النرويج؟ ما هو رأي النرويجيين عنها؟
+  - كيف لي أن أخسر الوزن من خلال النظام الغذائي فقط؟
+- source_sentence: هل من المقبول أن تجيب على سؤالك؟
+  sentences:
+  - ما هو أفضل كتاب على الإطلاق؟
+  - أي مسحوق بروتيني بدون آثار جانبية؟
+  - إذا أجبت على سؤالك الخاص على Quora، هل تصنيف إجابتك ينخفض؟
+- source_sentence: كيف تحدد ما إذا كان البريد الإلكتروني قد تم فتحه من قبل المستلم؟
+  sentences:
+  - لقد حصلت على 160 علامة في الامتحان الرئيسي ما هي فرص CSE في LNMIIT Jaipur؟
+  - امرأة تعزف على آلة موسيقية
+  - كيف يمكن للمرء أن يتتبع ما إذا تم قراءة البريد الإلكتروني المرسل؟
+- source_sentence: رجل وامرأة يتنزهان مع كلابهما
+  sentences:
+  - الزوجان يتنزهان مع كلابهما.
+  - رجل وامرأة يتنزهون مع خنازيرهم
+  - هل يمكنك الحصول على جسد مثالي بدون جهد؟
+- source_sentence: يتم إنتاج أمثلة جميلة من المينا، والسيراميك، والفخار في وفرة كبيرة،
+    وغالبا ما تتبع موضوع سلتيكي.
+  sentences:
+  - عملائنا بالكاد يستطيعون تحمل تكاليف مساعدتنا القانونية
+  - يتم إنتاج الفخار الصغير الذي له موضوع سلتيكي.
+  - يتم إنتاج عدد كبير من العناصر ذات المواضيع السلتية.
+pipeline_tag: sentence-similarity
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained on the AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative dataset. 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### 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:
+
+```bash
+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("AbderrahmanSkiredj1/Arabic_text_embedding_for_sts")
+# Run inference
+sentences = [
+    'يتم إنتاج أمثلة جميلة من المينا، والسيراميك، والفخار في وفرة كبيرة، وغالبا ما تتبع موضوع سلتيكي.',
+    'يتم إنتاج عدد كبير من العناصر ذات المواضيع السلتية.',
+    'يتم إنتاج الفخار الصغير الذي له موضوع سلتيكي.',
+]
+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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative
+
+* Dataset: AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative
+* Size: 853,827 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 14.54 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.32 tokens</li><li>max: 35 tokens</li></ul> |
+* Samples:
+  | anchor                                                                            | positive                                               | negative                                               |
+  |:----------------------------------------------------------------------------------|:-------------------------------------------------------|:-------------------------------------------------------|
+  | <code>هل يمكنك أن تأكل نفس الشيء كل يوم وتحصل على كل التغذية التي تحتاجها؟</code> | <code>هل الأكل نفس الشيء كل يوم صحي؟</code>            | <code>ما هي القوة الخارقة التي تتمنى أن تملكها؟</code> |
+  | <code>ثلاثة لاعبي كرة قدم، رقم 16 يرمي الكرة، رقم 71 يمنع الخصم الآخر.</code>     | <code>لاعبي كرة القدم يرمون ويمنعون بعضهم البعض</code> | <code>الفريق يأكل البيتزا في مطعم</code>               |
+  | <code>كيف تحسن مهاراتك في الكتابة؟</code>                                         | <code>كيف أستمر في تحسين كتابتي؟</code>                | <code>كيف يتم تحديد أرقام الضمان الاجتماعي؟</code>     |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative
+
+* Dataset: AbderrahmanSkiredj1/arabic_quora_duplicates_stsb_alue_holyquran_aranli_900k_anchor_positive_negative
+* Size: 11,584 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                           | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 16.03 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.72 tokens</li><li>max: 221 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.59 tokens</li><li>max: 42 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                | positive                                            | negative                                                                   |
+  |:--------------------------------------------------------------------------------------|:----------------------------------------------------|:---------------------------------------------------------------------------|
+  | <code>ماذا سيحدث لو توقفت الأرض عن الدوران وتدور في نفس الوقت؟</code>                 | <code>ماذا سيحدث إذا توقفت الأرض عن الدوران؟</code> | <code>ما هو أفضل هاتف ذكي تحت 15000؟</code>                                |
+  | <code>ثلاثة متفرجين بالغين وطفل واحد ينظرون إلى السماء بينما يقفون على الرصيف.</code> | <code>أربعة أشخاص ينظرون إلى السماء.</code>         | <code>رجل وثلاثة أطفال يشاهدون بالونات الهيليوم تطفو أعلى في الهواء</code> |
+  | <code>ماذا تفعل الدول لمنع الحرب؟</code>                                              | <code>كيف يجب على الدول أن تمنع الحرب؟</code>       | <code>كيف يمكنني كسب المال من بدء مدونة؟</code>                            |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 1e-06
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 1e-06
+- `weight_decay`: 0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `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
+- `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}
+- `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
+- `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_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0120 | 40   | 3.1459        |
+| 0.0240 | 80   | 3.2058        |
+| 0.0360 | 120  | 3.0837        |
+| 0.0480 | 160  | 3.1024        |
+| 0.0600 | 200  | 3.015         |
+| 0.0719 | 240  | 3.1311        |
+| 0.0839 | 280  | 3.1101        |
+| 0.0959 | 320  | 3.1288        |
+| 0.1079 | 360  | 3.045         |
+| 0.1199 | 400  | 3.0488        |
+| 0.1319 | 440  | 3.1001        |
+| 0.1439 | 480  | 3.2334        |
+| 0.1559 | 520  | 3.0581        |
+| 0.1679 | 560  | 2.9821        |
+| 0.1799 | 600  | 3.1733        |
+| 0.1918 | 640  | 3.0658        |
+| 0.2038 | 680  | 3.0721        |
+| 0.2158 | 720  | 3.1647        |
+| 0.2278 | 760  | 3.0326        |
+| 0.2398 | 800  | 3.1014        |
+| 0.2518 | 840  | 2.9365        |
+| 0.2638 | 880  | 3.0642        |
+| 0.2758 | 920  | 2.9864        |
+| 0.2878 | 960  | 3.0939        |
+| 0.2998 | 1000 | 3.0676        |
+| 0.3118 | 1040 | 2.9717        |
+| 0.3237 | 1080 | 2.9908        |
+| 0.3357 | 1120 | 2.9506        |
+| 0.3477 | 1160 | 2.907         |
+| 0.3597 | 1200 | 3.0451        |
+| 0.3717 | 1240 | 3.0002        |
+| 0.3837 | 1280 | 2.8842        |
+| 0.3957 | 1320 | 3.0697        |
+| 0.4077 | 1360 | 2.8967        |
+| 0.4197 | 1400 | 3.0008        |
+| 0.4317 | 1440 | 3.0027        |
+| 0.4436 | 1480 | 2.9229        |
+| 0.4556 | 1520 | 2.9539        |
+| 0.4676 | 1560 | 2.9415        |
+| 0.4796 | 1600 | 2.9401        |
+| 0.4916 | 1640 | 2.8498        |
+| 0.5036 | 1680 | 2.9646        |
+| 0.5156 | 1720 | 2.9231        |
+| 0.5276 | 1760 | 2.942         |
+| 0.5396 | 1800 | 2.8521        |
+| 0.5516 | 1840 | 2.8362        |
+| 0.5635 | 1880 | 2.8497        |
+| 0.5755 | 1920 | 2.8867        |
+| 0.5875 | 1960 | 2.9148        |
+| 0.5995 | 2000 | 2.9343        |
+| 0.6115 | 2040 | 2.8537        |
+| 0.6235 | 2080 | 2.7989        |
+| 0.6355 | 2120 | 2.8508        |
+| 0.6475 | 2160 | 2.916         |
+| 0.6595 | 2200 | 2.926         |
+| 0.6715 | 2240 | 2.752         |
+| 0.6835 | 2280 | 2.7792        |
+| 0.6954 | 2320 | 2.8381        |
+| 0.7074 | 2360 | 2.7455        |
+| 0.7194 | 2400 | 2.8953        |
+| 0.7314 | 2440 | 2.8179        |
+| 0.7434 | 2480 | 2.8471        |
+| 0.7554 | 2520 | 2.7538        |
+| 0.7674 | 2560 | 2.8271        |
+| 0.7794 | 2600 | 2.8401        |
+| 0.7914 | 2640 | 2.7402        |
+| 0.8034 | 2680 | 2.6439        |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.3
+- PyTorch: 2.2.2+cu121
+- Accelerate: 0.29.1
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## 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",
+}
+```
+
+#### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "output/matryoshka_nli_v4fromv3_output-matryoshka_nli_v3fromv1_output-matryoshkav1_11000-2024-07-06_22-30-55-checkpoint-1200-2024-07-07_12-56-26/checkpoint-2700",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 64000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.3",
+    "pytorch": "2.2.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af205c762996d872f6edee7c9ac4ea72cac082d6f8f9d1372a5206b807b129a3
+size 540795752

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,93 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "[رابط]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": true,
+      "special": true
+    },
+    "6": {
+      "content": "[بريد]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": true,
+      "special": true
+    },
+    "7": {
+      "content": "[مستخدم]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": true,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": [
+    "[بريد]",
+    "[مستخدم]",
+    "[رابط]"
+  ],
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}