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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 256,
+  "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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+---
+base_model: omarelsayeed/QA_Search
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:40597
+- loss:LoggingBAS
+widget:
+- source_sentence: 'فوري اليومي : عند إضافة أموال الي فوري اليومي يكون هناك اختيارين
+    بين إضافة مبلغ أو اختيار عدد وثائق'
+  sentences:
+  - انا حولت مبلغ ٢٠٠٠٠ج يوم ٢٠/١٠/٢٠٢٤ ولم تصل الي حسابي فوري وقدمت شكوي ولم يرد
+    عليا نهاءيا
+  - الرجاء إضافة الحجز لتذاكر القطار .
+  - الرجاء إضافة الحجز لتذاكر القطار .
+- source_sentence: تحويل من فودافون كاش الي البطاقه البنكيه
+  sentences:
+  - شحن رصيد عملات على تطبيق التيك توك
+  - توفير الاجهزه لمعارض الموتسكلات والدفع بالتقسيط عن طريق فورى وتغطية مناطق منشأة
+    البكارى وكرداسه و ناهيه لعدم توافر مناديب اومشرف. فى المناطق غير متوفر الخدمات
+    من فورى بها
+  - اضافه حجز القطارات على البرنامج
+- source_sentence: ارجو التواصل معي حيث انكم لا تردون علي رقمي ولا استطيع ايجاد حل
+    لمشكلتي 01080179030
+  sentences:
+  - زياده افراد العمل لدي خدمة العملاء
+  - هّلَ يَمًکْنِ تٌحًوٌيَلَ نِقُوٌدٍ مًنِ آلَآنِسِتٌآ بًآيَ بًنِفُسِ رقُمً آلَفُوٌنِ
+    لَلَکْآرتٌ آلَآصّفُر بًنِفُسِ رقُمً آلَمًوٌبًآيَلَ
+  - هذا الابلكيشن سئ
+- source_sentence: 1- عايز رسايل SMS بكل سحب وايداع 2- عايز اقدر احول إلى محفظة مثل
+    فودافون كاش من التطبيق علطول
+  sentences:
+  - تحويل الفلوس للمحافظ الالكترونيه والبطاقات البنكيه الاخري لتحسين جودة الخدمه
+  - هل من الممكن وضع الوضع المظلم في البرنامج
+  - تحويل المبلغ من الادخار الي حسابي الشخصي في البنك مباشرة
+- source_sentence: يوجد مشكله من شهر ولم يتم الانتهاء من الحل حتي الان عند عمل تحويل
+    من كارت الائتمان يتم رفض العمليه.....
+  sentences:
+  - تحويل الأموال للحسابات البنكية
+  - في خدمه غير متوجده علي الابلكيشان ارجو المساعده واظافه الخدمه 77178 تحصيلات العربي
+  - ممكن اقدم على طلب تقسيط ليه طلب اترفض في اول مرا
+---
+
+# SentenceTransformer based on omarelsayeed/QA_Search
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [omarelsayeed/QA_Search](https://huggingface.co/omarelsayeed/QA_Search). It maps sentences & paragraphs to a 256-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:** [omarelsayeed/QA_Search](https://huggingface.co/omarelsayeed/QA_Search) <!-- at revision 1714c8f70fa4550f723c8345fc222bdd06b8e137 -->
+- **Maximum Sequence Length:** 30 tokens
+- **Output Dimensionality:** 256 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 30, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 256, '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'يوجد مشكله من شهر ولم يتم الانتهاء من الحل حتي الان عند عمل تحويل من كارت الائتمان يتم رفض العمليه.....',
+    'في خدمه غير متوجده علي الابلكيشان ارجو المساعده واظافه الخدمه 77178 تحصيلات العربي',
+    'ممكن اقدم على طلب تقسيط ليه طلب اترفض في اول مرا',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 256]
+
+# 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
+
+#### Unnamed Dataset
+
+
+* Size: 40,597 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 13.93 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.83 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: -1.0</li><li>mean: -0.48</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                  | sentence_1                                                         | label             |
+  |:--------------------------------------------|:-------------------------------------------------------------------|:------------------|
+  | <code>تحويل للمحافظ الإلكترونية</code>      | <code>تحويل إلي المحفظة الإلكترونية مثل فودافون كاش و خلافه</code> | <code>1.0</code>  |
+  | <code>تحويل نقود على فودافون كاش</code>     | <code>محفظه الموبايل</code>                                        | <code>-1.0</code> |
+  | <code>تحويل علي المحافظه الالكترونيه</code> | <code>تحويل الاموال من فوري لمحافظ فودافون كاش رجاء</code>         | <code>1.0</code>  |
+* Loss: <code>__main__.LoggingBAS</code> with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 2
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.1970 | 500  | 1.0946        |
+| 0.3940 | 1000 | 0.894         |
+| 0.5910 | 1500 | 0.8248        |
+| 0.7880 | 2000 | 0.8007        |
+| 0.9850 | 2500 | 0.7938        |
+| 1.1820 | 3000 | 0.7666        |
+| 1.3790 | 3500 | 0.7409        |
+| 1.5760 | 4000 | 0.7377        |
+| 1.7730 | 4500 | 0.7262        |
+| 1.9701 | 5000 | 0.7302        |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.3.1
+- Transformers: 4.45.1
+- PyTorch: 2.4.0
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.20.0
+
+## 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",
+}
+```
+
+<!--
+## 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

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+{
+  "_name_or_path": "omarelsayeed/QA_Search",
+  "_num_labels": 2,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "id2label": {
+    "0": "\u0627\u0645\u0627\u0643\u0646 \u0641\u0631\u0648\u0639 \u0641\u0648\u0631\u064a",
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+    "22": "\u0645\u064a\u0627\u0647",
+    "23": "\u063a\u0627\u0632",
+    "24": "\u0627\u0644\u0643\u0647\u0631\u0628\u0627\u0621",
+    "25": "\u0641\u0648\u0631\u064a \u0628\u0627\u064a",
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+    "29": "\u0627\u0633\u062a\u0631\u0627\u062c\u0639 \u0642\u064a\u0645\u0629 \u0645\u0627\u0644\u064a\u0629",
+    "30": "\u0645\u0639\u0627\u0645\u0644\u0627\u062a \u062f\u0648\u0644\u064a\u0629",
+    "31": "\u062a\u0630\u0627\u0643\u0631",
+    "32": "\u0627\u0644\u062a\u0623\u0645\u064a\u0646",
+    "33": "\u0627\u0644\u0646\u0642\u0627\u0628\u0627\u062a",
+    "34": "\u062a\u0639\u0644\u064a\u0645",
+    "35": "\u062e\u062f\u0645\u0629 \u0627\u0644\u0639\u0645\u0644\u0627\u0621",
+    "36": "\u0627\u0644\u0639\u0627\u0628 \u0627\u0648\u0646\u0644\u0627\u064a\u0646",
+    "37": "\u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0645\u0627\u0644\u064a\u0629 \u0648 \u0628\u0646\u0648\u0643",
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+    "42": "Yellow Card",
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+  "model_type": "bert",
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+  "num_hidden_layers": 4,
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+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
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+  "use_cache": true,
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+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.45.1",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d31eea292c5946c145366a915555ac435b1086051a30c1fb6e3496de18f63d3c
+size 46203496

modules.json

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+[
+  {
+    "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": 30,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
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+    "normalized": false,
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+    "single_word": false
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+  "sep_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "do_lower_case": true,
+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 150,
+  "never_split": null,
+  "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]"
+}