Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

2_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b16fa5e38a7796584b44de28285e849288d488c36e70d76eace2adbd20826b8
+size 2362528

README.md

@@ -0,0 +1,428 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:149098
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/LaBSE
+widget:
+- source_sentence: چگونه می توانید واقعاً بدانید که کسی یک جامعه شناسی/روانی است؟
+    (علاوه بر این که آنها اسکن مغزی دارند)
+  sentences:
+  - تفاوت بین وکیل و وکیل چیست؟
+  - چگونه می توانم برای آزمون ادبیات انگلیسی خالص UGC آماده شوم؟
+  - از کجا می دانید کسی روانپزشکی است یا یک جامعه شناسی؟
+- source_sentence: ایده شما از ازدواج چیست؟
+  sentences:
+  - کدام برنامه برای C و C ++ مهمترین است؟
+  - How will the ban on Rs. 1000 and Rs. 500 notes impact Indian economy?
+  - ایده ازدواج چیست؟
+- source_sentence: کدام یک بهترین لپ تاپ برای خرید زیر 30k است؟
+  sentences:
+  - چگونه قیمت املاک و مستغلات تحت تأثیر تصمیم دولت هند برای از بین بردن 500 و 1000
+    یادداشت قرار می گیرد؟
+  - کدام بهترین لپ تاپ برای خرید بالاتر از 25000 پوند و زیر/تا 30000 پوند است؟
+  - چگونه استرس در ذهن را کاهش می دهیم؟
+- source_sentence: چگونه می توانم به طور جامع برای ادبیات انگلیسی خالص UGC آماده شوم؟
+  sentences:
+  - چگونه می توانم یک حساب پس انداز تعقیب را بصورت آنلاین ببندم؟
+  - چگونه می توانم برای NET JRF در ادبیات انگلیسی آماده شوم؟
+  - تفاوت بین گربه و علاقه مندان به GMAT چیست؟
+- source_sentence: آیا با دختری که باکره نیست ازدواج خواهید کرد؟
+  sentences:
+  - زنی با شلوار جین کنار اسبی با زین ایستاده است
+  - آیا تا به حال چیزی ماوراء الطبیعه یا فوق طبیعی را تجربه کرده اید؟
+  - آیا با کسی که باکره نیست ازدواج می کنید؟
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/LaBSE
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision b7f947194ceae0ddf90bafe213722569e274ad28 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  (3): Normalize()
+)
+```
+
+## 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("codersan/FaLaBSE-v5")
+# Run inference
+sentences = [
+    'آیا با دختری که باکره نیست ازدواج خواهید کرد؟',
+    'آیا با کسی که باکره نیست ازدواج می کنید؟',
+    'زنی با شلوار جین کنار اسبی با زین ایستاده است',
+]
+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
+
+#### Unnamed Dataset
+
+
+* Size: 149,098 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.54 tokens</li><li>max: 57 tokens</li></ul> |
+* Samples:
+  | anchor                                                               | positive                                                           |
+  |:---------------------------------------------------------------------|:-------------------------------------------------------------------|
+  | <code>اگر هند تقسیم نشده بود ، هند امروز چگونه به نظر می رسد؟</code> | <code>اگر پارتیشن اتفاق نیفتاد ، هند امروز چگونه خواهد بود؟</code> |
+  | <code>چگونه می توانم وارد امنیت اینترنت شوم؟</code>                  | <code>چگونه می توانم شروع به یادگیری امنیت اطلاعات کنم؟</code>     |
+  | <code>برخی از بهترین مؤسسات مربیگری GMAT در دهلی/NCR چیست؟</code>    | <code>بهترین مؤسسات مربیگری برای GMAT در NCR چیست؟</code>          |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `batch_sampler`: no_duplicates
+
+#### 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`: 32
+- `per_device_eval_batch_size`: 8
+- `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`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0429 | 100  | 0.0474        |
+| 0.0858 | 200  | 0.0364        |
+| 0.1288 | 300  | 0.0345        |
+| 0.1717 | 400  | 0.0309        |
+| 0.2146 | 500  | 0.0347        |
+| 0.2575 | 600  | 0.0365        |
+| 0.3004 | 700  | 0.0303        |
+| 0.3433 | 800  | 0.0288        |
+| 0.3863 | 900  | 0.029         |
+| 0.4292 | 1000 | 0.0329        |
+| 0.4721 | 1100 | 0.0351        |
+| 0.5150 | 1200 | 0.0282        |
+| 0.5579 | 1300 | 0.029         |
+| 0.6009 | 1400 | 0.029         |
+| 0.6438 | 1500 | 0.0278        |
+| 0.6867 | 1600 | 0.028         |
+| 0.7296 | 1700 | 0.0276        |
+| 0.7725 | 1800 | 0.0306        |
+| 0.8155 | 1900 | 0.0242        |
+| 0.8584 | 2000 | 0.0254        |
+| 0.9013 | 2100 | 0.0226        |
+| 0.9442 | 2200 | 0.0261        |
+| 0.9871 | 2300 | 0.0258        |
+| 1.0300 | 2400 | 0.0245        |
+| 1.0730 | 2500 | 0.0194        |
+| 1.1159 | 2600 | 0.021         |
+| 1.1588 | 2700 | 0.018         |
+| 1.2017 | 2800 | 0.0201        |
+| 1.2446 | 2900 | 0.0204        |
+| 1.2876 | 3000 | 0.0178        |
+| 1.3305 | 3100 | 0.0159        |
+| 1.3734 | 3200 | 0.0184        |
+| 1.4163 | 3300 | 0.0189        |
+| 1.4592 | 3400 | 0.0194        |
+| 1.5021 | 3500 | 0.0201        |
+| 1.5451 | 3600 | 0.0164        |
+| 1.5880 | 3700 | 0.0187        |
+| 1.6309 | 3800 | 0.0181        |
+| 1.6738 | 3900 | 0.0161        |
+| 1.7167 | 4000 | 0.0195        |
+| 1.7597 | 4100 | 0.0165        |
+| 1.8026 | 4200 | 0.0175        |
+| 1.8455 | 4300 | 0.016         |
+| 1.8884 | 4400 | 0.0142        |
+| 1.9313 | 4500 | 0.0187        |
+| 1.9742 | 4600 | 0.0137        |
+| 2.0172 | 4700 | 0.0173        |
+| 2.0601 | 4800 | 0.015         |
+| 2.1030 | 4900 | 0.0158        |
+| 2.1459 | 5000 | 0.0135        |
+| 2.1888 | 5100 | 0.0144        |
+| 2.2318 | 5200 | 0.0135        |
+| 2.2747 | 5300 | 0.0142        |
+| 2.3176 | 5400 | 0.0129        |
+| 2.3605 | 5500 | 0.0142        |
+| 2.4034 | 5600 | 0.0141        |
+| 2.4464 | 5700 | 0.0142        |
+| 2.4893 | 5800 | 0.0141        |
+| 2.5322 | 5900 | 0.0118        |
+| 2.5751 | 6000 | 0.0142        |
+| 2.6180 | 6100 | 0.0125        |
+| 2.6609 | 6200 | 0.0107        |
+| 2.7039 | 6300 | 0.0129        |
+| 2.7468 | 6400 | 0.0114        |
+| 2.7897 | 6500 | 0.0137        |
+| 2.8326 | 6600 | 0.0108        |
+| 2.8755 | 6700 | 0.0131        |
+| 2.9185 | 6800 | 0.0114        |
+| 2.9614 | 6900 | 0.0137        |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.0
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### 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 +1,32 @@
+{
+  "_name_or_path": "sentence-transformers/LaBSE",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "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,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 501153
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:474aecc76ef904f550d7b43b328bb77b98272e52f3549f1911372f76c9ac7c20
+size 1883730160

modules.json

@@ -0,0 +1,26 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "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.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
+size 13632172

tokenizer_config.json

@@ -0,0 +1,59 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+}