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": 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

@@ -0,0 +1,412 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1535
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/nomic-embed-text-v2-moe
+widget:
+- source_sentence: İhale Yönetmeliği Madde 19'a göre doğrudan temin için mal/hizmet/tarife/seyahat
+    alımlarının bedel sınırı nedir?
+  sentences:
+  - 'Week9 (Nov 25): Condition Variables, Deadlocks / Project Phase II: Scheduler
+    - @Nov 28, Preliminary Report Due: Dec 23, How to write Project Report, Phase
+    1- DEMO'
+  - ç) Üniversitenin tahmini bedeli bir önceki hesap dönemi toplam giderlerinin TÜFE
+    oranına göre güncellenecek üçyüzsekizbinüçyüzyetmişdört TL’ye karşılık gelen tutardaki
+    bedeli aşmayacak olan mal ve hizmet alımları, tarifeli alımlar ile seyahat alımları.
+  - MADDE 9 – (1) Yabancı uyruklu öğrenciler hakkında ilgili mevzuat hükümleri ile
+    Senato tarafından belirlenen esaslar uygulanır.
+- source_sentence: Madde 8'e göre Komisyon Başkanı kime karşı sorumludur?
+  sentences:
+  - Komisyon Başkanı, Rektöre karşı sorumludur.
+  - Komisyon Başkanı, çalışma birimlerinin faaliyetlerini izler ve denetler.
+  - Alınacak pedagojik formasyon dersleri öğrencinin dönemlik ders yükünün üzerinde
+    olması halinde genel not ortalaması 3.00 ve üzeri olan öğrencilerden ücret alınmayacak...
+- source_sentence: What is the grading breakdown for CSE 462?
+  sentences:
+  - 'Grading Breakdown: 30% Midterm
+
+    45% Final
+
+    25% Assignments'
+  - Teklifler iadeli taahhütlü olarak da gönderilebilir.
+  - Any form of cheating will be reported to the faculty's relevant administrative
+    body for further action.
+- source_sentence: Resmi Yazışma Yönergesi Madde 16'ya göre paragraflar nasıl başlar
+    ve hizalanır?
+  sentences:
+  - (8)Yazışma birim kodu olmayan veya verilmeyen hiçbir birim yazışma yapamaz.
+  - (3) Zorunlu hâllerde veya olağanüstü durumlarda hazırlanan olur yazılarında “OLUR”
+    ibaresinden sonra tarih ve imza için uygun boş satır bırakılarak ilk satırda imzalayanın
+    adı ve soyadına, ikinci satırda ise unvan bilgilerine yer verilir (Örnek 18).
+  - (4) Paragrafa 1,25 cm içeriden başlanır ve metin iki yana hizalanır.
+- source_sentence: CSE 447 (Ozkaya) sınavında kopya çekme girişimi nasıl değerlendirilir?
+  sentences:
+  - 'Week-10 AVL tree
+
+    Week-11 IPR tree
+
+    Week-12 B tree
+
+    Week-13 B+ tree'
+  - 'Alan Eğitimcisi : Doktorasını ve/veya doçentliğini, ilgili alan eğitiminde (fizik
+    eğitimi, kimya eğitimi, biyoloji eğitimi, matematik eğitimi, tarih eğitimi, din
+    eğitimi, Türkçe eğitimi vb.) almış öğretim üyesini,'
+  - 'Exam Cheating Policy: Any attempt at cheating during the midterm and final exams
+    will be treated seriously.'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on nomic-ai/nomic-embed-text-v2-moe
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) on the json 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:** [nomic-ai/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) <!-- at revision 1066b6599d099fbb93dfcb64f9c37a7c9e503e85 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **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: NomicBertModel 
+  (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})
+  (2): 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("Demircan12/nomic-embed-text-v2-moe-YeditepeFT")
+# Run inference
+sentences = [
+    'CSE 447 (Ozkaya) sınavında kopya çekme girişimi nasıl değerlendirilir?',
+    'Exam Cheating Policy: Any attempt at cheating during the midterm and final exams will be treated seriously.',
+    'Week-10 AVL tree\nWeek-11 IPR tree\nWeek-12 B tree\nWeek-13 B+ tree',
+]
+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
+
+#### json
+
+* Dataset: json
+* Size: 1,535 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: 11 tokens</li><li>mean: 22.23 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 38.09 tokens</li><li>max: 247 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                    | positive                                                                                                                                                                                                              |
+  |:--------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Are the Fall (Regular) and Spring (Irregular) programs in the Faculty of Law different according to MADDE 5?</code> | <code>Güz (Regular) ve Bahar (Irregular) programları başlangıç zamanı dışında her açıdan birbirine denktir.</code>                                                                                                    |
+  | <code>According to MADDE 6, who can take the Postgraduate Proficiency Exam?</code>                                        | <code>(2) Yeterlik Sınavı’na, yeni kayıtlı öğrencilerle birlikte halen hazırlık programında öğrenimine devam eden öğrenciler de girebilirler.</code>                                                                  |
+  | <code>What is the purpose of the Horizontal/Vertical Transfer Adaptation Principles (Madde 1)?</code>                     | <code>Madde 1- (1) Yatay/Dikey Geçiş İntibak Esaslarının amacı, Yeditepe Üniversitesine yatay geçiş veya dikey geçiş ile kabul edilen öğrencilerin intibak işlemlerine ilişkin esas ve usulleri belirlemektir.</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"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### json
+
+* Dataset: json
+* Size: 220 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 220 samples:
+  |         | anchor                                                                             | positive                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 12 tokens</li><li>mean: 21.82 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 36.3 tokens</li><li>max: 160 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                            | positive                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>CSE 439 dersinin notlandırma dağılımı nasıldır?</code>                                                                      | <code>Grading Breakdown: Midterm: 30%\nFinal: 35%\nHomeworks and Quizzes: 15%\nTerm Project: 20%</code>                                                                                                                                                                                                                                                                                                 |
+  | <code>Hukuk Fakültesi Yönetmeliği Madde 13'e göre dersler öğrencilerin hangi yeteneklerini geliştirmeye yönelik yürütülür?</code> | <code>(3) Dersler öğrencilerin muhakeme ve sözlü-yazılı anlatım yeteneklerinin geliştirilmesine katkı sağlayacak şekilde yürütülür.</code>                                                                                                                                                                                                                                                              |
+  | <code>Yönetmelik Madde 18'e göre hangi sınav Yönetmeliğine göre başarılı olanlar dil sınavından muaf tutulur?</code>              | <code>b) Öğretim dilinin anadil olarak konuşulduğu ülkelerde yabancıların yükseköğrenim görebilmeleri için aranan asgari yabancı dil seviyesinin tespiti amacına yönelik olarak yapılan sınavlarda ve 25/9/2013 tarihli ve 28776 sayılı Resmî Gazete’de yayımlanan Yeditepe Üniversitesi Yabancı Diller Hazırlık Programı Eğitim-Öğretim ve Sınav Yönetmeliği hükümlerine göre başarılı olanlar.</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
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `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`: 1.0
+- `num_train_epochs`: 5
+- `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`: 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}
+- `tp_size`: 0
+- `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
+- `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 | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 1.0417 | 100  | 0.1199        | 0.0659          |
+| 2.0833 | 200  | 0.0236        | 0.0524          |
+| 3.125  | 300  | 0.0145        | 0.0578          |
+| 4.1667 | 400  | 0.0102        | 0.0617          |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+#### 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 +1,73 @@
+{
+  "activation_function": "gelu",
+  "add_pooling_layer": false,
+  "architectures": [
+    "NomicBertModel"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "nomic-ai/nomic-bert-2048--configuration_hf_nomic_bert.NomicBertConfig",
+    "AutoModel": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel",
+    "AutoModelForMaskedLM": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForPreTraining",
+    "AutoModelForMultipleChoice": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForMultipleChoice",
+    "AutoModelForQuestionAnswering": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForQuestionAnswering",
+    "AutoModelForSequenceClassification": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForSequenceClassification",
+    "AutoModelForTokenClassification": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForTokenClassification"
+  },
+  "bos_token_id": null,
+  "causal": false,
+  "dense_seq_output": true,
+  "embd_pdrop": 0.1,
+  "eos_token_id": null,
+  "expert_choice_router": false,
+  "ffn_div": 1,
+  "fused_bias_fc": true,
+  "fused_dropout_add_ln": true,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_trained_positions": 2048,
+  "mlp_fc1_bias": true,
+  "mlp_fc2_bias": true,
+  "model_type": "nomic_bert",
+  "moe_every_n_layers": 2,
+  "moe_impl": "megablocks",
+  "moe_normalize_expert_weights": false,
+  "moe_resid_pdrop": 0.0,
+  "moe_top_k": 2,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 2048,
+  "num_experts": 8,
+  "num_shared_experts": 0,
+  "pad_token_id": 1,
+  "pad_vocab_size_multiple": 64,
+  "parallel_block": false,
+  "parallel_block_tied_norm": false,
+  "prenorm": false,
+  "qkv_proj_bias": true,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "rotary_emb_base": 10000,
+  "rotary_emb_fraction": 1.0,
+  "rotary_emb_interleaved": false,
+  "rotary_emb_scale_base": null,
+  "rotary_scaling_factor": null,
+  "router_aux_loss_coef": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "use_flash_attn": true,
+  "use_rms_norm": null,
+  "use_xentropy": true,
+  "vocab_size": 250048
+}

config_sentence_transformers.json

@@ -0,0 +1,20 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "search_query: ",
+    "passage": "search_document: ",
+    "Classification": "classification: ",
+    "MultilabelClassification": "classification: ",
+    "Clustering": "clustering: ",
+    "PairClassification": "classification: ",
+    "STS": "classification: ",
+    "Summarization": "classification: ",
+    "Speed": "search_document: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c72a21fd4c5bdd24e9f095ecc125bf8d4154cd557cf7ba62fdc75b5a4a74c06
+size 1901187232

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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+    "single_word": false
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+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

tokenizer_config.json

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+  "unk_token": "<unk>"
+}