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README.md

@@ -0,0 +1,413 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:132830
+- loss:MultipleNegativesRankingLoss
+base_model: intfloat/multilingual-e5-large
+widget:
+- source_sentence: 'query: 伤科方'
+  sentences:
+  - 'passage: title: 骨伤科效方集 author: Gengmin Tang category: Orthopedics, Medicine formulae,
+    receipts, prescriptions, 伤科方 description: '
+  - 'passage: title: พูดด้วยภาพ 2 : เทคนิคทำสไลด์เป็นภาพง่าย ๆ ใน 2 ขั้นตอน author:
+    สุธาพร ล้ำเลิศกุล. category: Microsoft PowerPoint (Computer file), Presentation
+    graphics software, Business presentations, การออกแบบกราฟิก description: จบปัญหา
+    "ไม่มีเวลา" และ "ไม่มีเทคนิค" ในการทำสไลด์ หนังสือ "พูดด้วยภาพ 2 : ทำสไลด์เป็นภาพง่าย
+    ๆ ใน 2 ขั้นตอน" เล่มนี้ จะสอนให้คุณคิดและทำสไลด์อย่างมีระบบใน 2 ขั้นตอน โดยคุณสามารถเลือกเรียนรู้เฉพาะบท
+    และลงมือทำได้แบบไม่จำเป็นต้องอ่านตั้งแต่ต้นจนจบ ย่อยข้อมูล "ยาก" ให้เป็น "ภาพ"
+    ที่เข้าใจง่าย พร้อม Link Youtube Video สอนในเล่ม ลด ขั้นตอน เพิ่ม ความแตกต่าง
+    ทำสไลด์ให้ สนุก สวยงาม และสื่อสารให้เกิดประโยชน์สูงสุดแก่ผู้ฟัง ตามแบบฉบับของ
+    "BetterPitch" สถาบันสอนการทำสไลด์ในองค์กรชั้นนำทั่วประเทศ!'
+  - 'passage: title: 福慧之道 author: Yinai Sun category: Happiness, Well-being, Conduct
+    of life, Human comfort, Bonheur, Bien-être, Morale pratique, ethics (philosophical
+    concept), comfort (sensation), Fo jiao Ren sheng zhe xue Tong su du wu description:
+    Ben shu shi dui zheng ge zhong hua wen hua de zong jie, jiang shu ji fu ji hui
+    de fang fa. nei rong bao gua : fu mai yu hui mai : ren sheng de xing fu er mai
+    ; ru he jie fu hui er mai ; cai fu fu tian ; zhi hui fu tian ; fu tian fa ze ;
+    ri xing yi shan ; fu hui ren sheng'
+- source_sentence: 'query: แนะนำหนังสือการจัดการธุรกิจ'
+  sentences:
+  - 'passage: title: กุญแจ 5 ดอก ขจัดข้อขัดแย้งในองค์งาน author: ปรีชา ทิวะหุต. category:
+    การจัดการธุรกิจ, การจัดองค์การ description: '
+  - 'passage: title: มานุษยวิทยากายภาพ : วิวัฒนาการทางกายภาพและวัฒนธรรม author: งามพิศ
+    สัตย์สงวน category: มานุษยวิทยา, มนุษย์กับวัฒนธรรม, มนุษยวิทยากายภาพ description: '
+  - 'passage: title: พีระพงศ์อนุสรณ์ author: N/A category: Birabhongse Kasemsri, M.L.,
+    1935-2000, Diplomats Thailand Biography, ชีวประวัติ, หนังสืออนุสรณ์งานศพ description:
+    ในวาระครบ 5 ปีแห่งการถึงแก่อนิจกรรมของหม่อมหลวงพีระพงศ์ เกษมศรี ครอบครัวของหม่อมหลวงพีระพงศ์ฯ
+    ได้จัดทำหนังสือ "พีระพงศ์อนุสรณ์" เป็นเครื่องสำนึกถึงชีวิตและงานของหม่อมหลวงพีระพงศ์ฯ
+    จุดมุ่งประสงค์เหนือสิ่งอื่นใดของหนังสือนี้ ก็เพื่อเป็นอนุสรณ์ถึงความจงรักภักดีอุทิศตนถวายของหม่อมหลวงพีระพงศ์ฯ
+    ต่อสถาบันพระมหากษัตริย์ ต่อพระบรมราชจักรีวงศ์ และต่อองคืพระบาทสมเด็จพระเจ้าอยู่หัวและสมเด็จพระนางเจ้าฯพระบรมราชินีนาถ
+    ตลอดจนพระบรมวงศานุวงศ์ทุกพระองค์'
+- source_sentence: 'query: เริ่มต้นManipulation, Orthopedicควรอ่านอะไร'
+  sentences:
+  - 'passage: title: 實用筋膜操作指引 = A practical guide to fascial manipulation author:
+    盧奧馬拉 (Luomala, Tuulia), 文字作者 category: Manipulation (Therapeutics), Fasciae (Anatomy),
+    Manipulation, Orthopedic, Fascia, Manipulation (Thérapeutique), Ji jin mo fang
+    song shu description: '
+  - 'passage: title: Opioid sensitivity of chronic noncancer pain author: Eija Kalso
+    category: Opioids Therapeutic use Congresses, Chronic pain Chemotherapy Congresses,
+    Opioids Receptors Congresses, Pain drug therapy, Analgesics, Opioid therapeutic
+    use, Chronic Disease drug therapy, Receptors, Opioid physiology, Douleur chronique
+    Chimiothérapie Congrès, Opioïdes Emploi en thérapeutique Congrès, Opioïdes
+    Récepteurs Congrès, Opioids Receptors, Opioids Therapeutic use, Analgésiques
+    morphiniques usage thérapeutique, Maladie chronique traitement médicamenteux,
+    Récepteur endorphine, Chronischer Schmerz, Opioide, Kongress, Opiatrezeptor,
+    Analgesie, Opiate, Congress, Conference papers and proceedings, Actes de congrès
+    description: Contains papers from the first international research symposium of
+    the International Association for the Study of Pain, held in Helsinki, Finland,
+    Fall 1998. Focus is on opioid responsiveness to neuropathic pain. Papers are arranged
+    in sections on function and dysfunction of opioid receptors, clinical pharmacology
+    of opioids, understanding and improving opioid sensitivity, and opioid sensitivity
+    of different chronic pain states. Specific topics include targeting of opioid
+    receptors to presynaptic sites, route of opioid administration, phenotypic changes
+    induced in dorsal root ganglion neurons by nerve injury, and opioids in headache.
+    Kalso is currently affiliated with the Karolinska Institute in Sweden. IASP member
+    price, $44.85. Annotation copyrighted by Book News, Inc., Portland, OR'
+  - 'passage: title: พลิกคัมภีร์ตีแตกเศรษฐกิจไทย = Thailand''s economic outlook 2009
+    author: วีระศักดิ์ พงศ์อักษร. category: ปัญหาเศรษฐกิจ ไทย, ไทย ภาวะเศรษฐกิจ, ไทย
+    ภาวะสังคม description: '
+- source_sentence: 'query: เริ่มต้นทางรถไฟ ไทย กาญจนบุรีควรอ่านอะไร'
+  sentences:
+  - 'passage: title: คู่มือคำศัพท์ช่วยเหลือนักท่องเที่ยวเบื้องต้น (ภาษาจีน) พร้อมภาพประกอบ
+    author: ชัยพันธุ์ สิทธิสุวรรณกุล category: คำศัพท์, ภาษาจีน คู่มือ, นักท่องเที่ยว
+    description: '
+  - 'passage: title: ทางรถไฟสายมรณะ author: N/A category: ทางรถไฟ ไทย กาญจนบุรี description: '
+  - 'passage: title: ยุทธศาสตร์ชาติว่าด้วยการป้องกันและปราบปรามการทุจริต ระยะที่ 3
+    (พ.ศ. 2560-2564) author: คณะกรรมการป้องกันและปราบปรามการทุจริตแห่งชาติ category:
+    การทุจริตและประพฤติมิชอบ ไทย, การทุจริตและประพฤติมิชอบในวงราชการ ไทย ยุทธศาสตร์,
+    ยุทธศาสตร์, การฉ้อราษฎร์บังหลวง ไทย การป้องกัน description: '
+- source_sentence: 'query: หนังสือนิทาน'
+  sentences:
+  - 'passage: title: เด็กหญิงข้าวเปลือก author: หยาดฝน ธัญโชติกานต์. category: นิทาน
+    description: '
+  - 'passage: title: Current drug discovery technologies author: N/A category: Drugs
+    Design Periodicals, Pharmaceutical technology Periodicals, Drug Design, Technology,
+    Pharmaceutical, Drugs Design, Pharmaceutical technology, Periodicals description: '
+  - 'passage: title: 汉语词汇・句法・语音的相互关联 : 第二届肯特岗国际汉语语言学圆桌会议论文集 = Interface in Chinese
+    : morphology, syntax and phonetics author: Kent Ridge International Roundtable
+    Conference on Chinese linguistics category: Chinese language Grammar Congresses,
+    Chinese language Congresses, Chinois (Langue) Grammaire Congrès, Chinois (Langue)
+    Congrès, Han yu yu yan xue guo ji xue shu hui yi hui yi lu, Chinese language,
+    Chinese language Grammar, Conference papers and proceedings, Conversation and
+    phrase books description: '
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-large
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). It maps sentences & paragraphs to a 1024-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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision 3d7cfbdacd47fdda877c5cd8a79fbcc4f2a574f3 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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/huggingface/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, 'architecture': 'XLMRobertaModel'})
+  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'query: หนังสือนิทาน',
+    'passage: title: เด็กหญิงข้าวเปลือก author: หยาดฝน ธัญโชติกานต์. category: นิทาน description: ',
+    'passage: title: Current drug discovery technologies author: N/A category: Drugs Design Periodicals, Pharmaceutical technology Periodicals, Drug Design, Technology, Pharmaceutical, Drugs Design, Pharmaceutical technology, Periodicals description: ',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000,  0.7672, -0.0610],
+#         [ 0.7672,  1.0000,  0.0661],
+#         [-0.0610,  0.0661,  1.0000]])
+```
+
+<!--
+### 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: 132,830 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                          | sentence_2                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              | string                                                                              |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 14.35 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 90.98 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 87.53 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                  | sentence_1                                                                                                                                                                                                                                                         | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  |:------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>query: ไสยศาสตร์ สำหรับมือใหม่</code>                 | <code>passage: title: สถานการณ์พระพุทธศาสนา : กระแสไสยศาสตร์ author: พระธรรมปิฎก (ป.อ. ปยุตฺโต) category: ไสยศาสตร์, พุทธศาสนากับไสยศาสตร์ description: </code>                                                                                                    | <code>passage: title: Hospitality marketing management author: Robert D. Reid category: Hospitality industry Marketing, Food service Marketing, Restaurants Marketing, Accueil (Tourisme) Marketing, Services alimentaires Marketing, Marketing, Tiếp thị, Hospitality industry, Khách sạn, Dịch vụ ăn uống, Restaurants, Quán ăn description: </code>                                                                                                                                                                                   |
+  | <code>query: 伤科方</code>                                     | <code>passage: title: 骨伤科效方集 author: Gengmin Tang category: Orthopedics, Medicine formulae, receipts, prescriptions, 伤科方 description: </code>                                                                                                                      | <code>passage: title: 福慧之道 author: Yinai Sun category: Happiness, Well-being, Conduct of life, Human comfort, Bonheur, Bien-être, Morale pratique, ethics (philosophical concept), comfort (sensation), Fo jiao Ren sheng zhe xue Tong su du wu description: Ben shu shi dui zheng ge zhong hua wen hua de zong jie, jiang shu ji fu ji hui de fang fa. nei rong bao gua : fu mai yu hui mai : ren sheng de xing fu er mai ; ru he jie fu hui er mai ; cai fu fu tian ; zhi hui fu tian ; fu tian fa ze ; ri xing yi shan ; fu hui ren sheng</code> |
+  | <code>query: basic Acid-Base Imbalance problems book</code> | <code>passage: title: Acid-base, fluids, and electrolytes made ridiculously simple author: Richard A. Preston category: Acid-Base Imbalance problems, Body Fluids problems, Water-Electrolyte Imbalance problems, Water-electrolyte imbalance description: </code> | <code>passage: title: Fetal and neonatal neurology and neurosurgery author: Malcolm I. Levene category: Brain Diseases, Newborn infants, Nervous system Surgery, Nervous system Diseases, Brain embryology, Fetal Diseases therapy, Infant, Newborn, Neurosurgery, Prenatal Diagnosis methods, Ultrasonography methods, Neurosurgical Procedures, Cerveau Maladies, Nouveau-nés, Neurochirurgie, Système nerveux Maladies description: </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",
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 1
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_ratio`: None
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `enable_jit_checkpoint`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `use_cpu`: False
+- `seed`: 42
+- `data_seed`: None
+- `bf16`: False
+- `fp16`: True
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: -1
+- `ddp_backend`: None
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `use_cache`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.2408 | 500  | 0.4763        |
+| 0.4817 | 1000 | 0.1799        |
+| 0.7225 | 1500 | 0.1731        |
+| 0.9634 | 2000 | 0.1628        |
+
+
+### Framework Versions
+- Python: 3.12.13
+- Sentence Transformers: 5.3.0
+- Transformers: 5.0.0
+- PyTorch: 2.10.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+<!--
+## 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 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "5.0.0",
+    "pytorch": "2.10.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model-002.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ebd2ec3ab0d42110d44ce5907bb602811983ee0a939551b8877552b5fd8dfe7
+size 2239607120

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fe715a86a37cd2b20e5eaeee8b22815bce65de676d1e0cd856114b59dab67fc
+size 16766387

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}