Upload fine-tuned AZ-EN sentence embedding model (checkpoint-388311)

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:8283932
+- loss:MSELoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: Through the Southern Gas Corridor pipeline, gas supply to the European
+    Union increased from 8.1 billion cubic meters in 2021 to 11.4 billion cubic meters
+    in 2022.
+  sentences:
+  - After this meeting, the monthly amount collected from prosecutors and investigators
+    for the building was increased from 460 manats to 480 manats.
+  - Məlik-Aslanov 1919-cu il fevralın 18-dək həm də müvəqqəti olaraq ticarət, sənaye
+    və ərzaq nazirinin səlahiyyətlərini də yerinə yetirmişdi.
+  - Üçüncü mərhələdə isə Şura hər bir layihə üzrə təqdim olunmuş ekspert rəyini, QHT-nin
+    Şuranın maliyyə dəstəyi hesabına əvvəlki illərdə həyata keçirdiyi layihənin icra
+    vəziyyətini və layihə idarəetmə təcrübəsini nəzərə alaraq yekun qərar qəbul edir.
+- source_sentence: '“Azərbaycan Uşaqlar Birliyi”nin sədri Kəmalə Ağazadə isə məsələnin
+    Elinanın deyil, digər şəxslərin üzərində fokuslanmasının doğru olmadığını bildirdi:
+    “Elinanın intiharı ilə bağlı məsələ bu gün də sosial şəbəkələrdə xeyli müzakirə
+    edilir, müxtəlif fikirlər bildirilir.'
+  sentences:
+  - 1952-ci ilin aprelindən başlayaraq, "Azərbaycan Kültür Dərnəyi" tərəfindən Ankarada
+    aylıq "Azərbaycan" jurnalı nəşr olunur.
+  - G. Məmmədovanın fikrincə abidənin konstruktiv həllinin analizi, kvadrat təməldən
+    dairəvi dacili və səkkizbucaqlı xarici barabana keçidin yelkənlərlə təmin edilməsinə
+    əsasən kilsəni təxminən VII-VIII əsrlərə aid etmək mümkündür.
+  - However, a signature campaign was conducted in the country to hold a referendum
+    on extending Nursultan Nazarbayev’s term, and nearly 5 million signatures were
+    collected.
+- source_sentence: Thus, we preserve our history, traditions, and culture, and we
+    do a lot to support each other.
+  sentences:
+  - Belə ki, ara yoldan Bakıxanov küçəsinə çıxan “Mercedes”in sürücü Özal Quliyevin
+    üstünlük nişanının tələbinə əməl etməməsi qəza ilə nəticələnib.
+  - Bundan başqa, onun sözlərinə görə, OPEK+ razılaşması neft bazarının məhsul artıqlığından
+    qurtulmasına kömək edib.
+  - Onun fikrincə, İranın Azərbaycan vilayətləri də “Cənubi Azərbaycan” olmalıdır.
+- source_sentence: It's true that, although Shahriyar, who is in the top four alongside
+    Aronyan in the rankings, couldn't win this match.
+  sentences:
+  - After spending a year in exile, his father Sultan Abdul Hamid sent him to Istanbul
+    along with his sisters Ayşe Sultan and Şadiye Sultan, and asked his brother Sultan
+    Reşad to arrange their marriages.
+  - Bu, ilk dəfədir ki ABŞ hərbi qüvvələri Rusiyanın keçən ay gizli olaraq raketlər
+    yerləşdirilməsini ictimai şəkildə təsdiq edir.
+  - He noted that the Supreme Court held seven sessions, thoroughly reviewed the lower
+    court’s investigation, and upheld the death sentence.
+- source_sentence: At the same time, it is no secret that Washington’s strategic plans
+    for the Middle East include changing the current Iranian regime, which opposes
+    Western interests in the region.
+  sentences:
+  - Sürücü Ə.Nəzərovla maşındakı digər sərnişinlər Rahim Mahmudov və Anar Bayramov
+    isə müxtəlif dərəcəli bədən xəsarətləri ilə Lənkəran Mərkəzi Rayon Xəstəxanasına
+    yerləşdirilib.
+  - In addition, Turkey was demanding the territory that included the districts of
+    Akhaltsikhe, Akhalkalaki, Alexandropol (Gyumri), Surmali, and Nakhchivan.
+  - Bu vəziyyət kilsə meydanını düzəltdiyindən və qolları bərabər uzunluqda olan xaç
+    planı aydınlaşmadığı üçün bu plan növü qapalı yunan xaçı planı adlandırılır.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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 = [
+    'At the same time, it is no secret that Washington’s strategic plans for the Middle East include changing the current Iranian regime, which opposes Western interests in the region.',
+    'In addition, Turkey was demanding the territory that included the districts of Akhaltsikhe, Akhalkalaki, Alexandropol (Gyumri), Surmali, and Nakhchivan.',
+    'Sürücü Ə.Nəzərovla maşındakı digər sərnişinlər Rahim Mahmudov və Anar Bayramov isə müxtəlif dərəcəli bədən xəsarətləri ilə Lənkəran Mərkəzi Rayon Xəstəxanasına yerləşdirilib.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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: 8,283,932 training samples
+* Columns: <code>sentence_0</code> and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | label                                |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------|
+  | type    | string                                                                           | list                                 |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 29.8 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                           | label                                                                                                                            |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
+  | <code>“Biz “Hizbullah”a axan maliyyə dəstəyini dayandırmaq istəyirik və bu məqsədlə ABŞ hökuməti tutarlı məlumat qarşılığında 10 milyon dollaradək mükafat verməklə yanaşı digər tədbirlər də görəcək”, - Evanoff belə deyib.</code> | <code>[-0.022054675966501236, 0.0932646170258522, -0.01854480803012848, -0.025271562859416008, 0.028432276099920273, ...]</code> |
+  | <code>Bu dövləti bu gün müxalifətdə olanlar quranda Əli Həsənovun harada nə işlə məşğul olduğu bəlli deyildi.</code>                                                                                                                 | <code>[-0.012831359170377254, 0.022371841594576836, -0.0271938294172287, 0.09667906910181046, 0.009270057082176208, ...]</code>  |
+  | <code>APA-nın “Hürriyet” qəzetinə istinadən verdiyi məlumata görə, ABŞ Hərbi Hava Qüvvələrinn Komandanlığı ən son 1991-ci ildə Körfəz savaşında istifadə edilmiş B-52 təyyarələrinin Qətərə göndərildiyini açıqlayıb.</code>         | <code>[-0.01321476697921753, 0.06281372904777527, 0.005026344675570726, -0.004140781704336405, 0.04239720478653908, ...]</code>  |
+* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 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
+- `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`: 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
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step   | Training Loss |
+|:------:|:------:|:-------------:|
+| 0.0039 | 500    | 0.0035        |
+| 0.0077 | 1000   | 0.0024        |
+| 0.0116 | 1500   | 0.0022        |
+| 0.0155 | 2000   | 0.002         |
+| 0.0193 | 2500   | 0.0019        |
+| 0.0232 | 3000   | 0.0019        |
+| 0.0270 | 3500   | 0.0018        |
+| 0.0309 | 4000   | 0.0018        |
+| 0.0348 | 4500   | 0.0017        |
+| 0.0386 | 5000   | 0.0017        |
+| 0.0425 | 5500   | 0.0016        |
+| 0.0464 | 6000   | 0.0016        |
+| 0.0502 | 6500   | 0.0016        |
+| 0.0541 | 7000   | 0.0016        |
+| 0.0579 | 7500   | 0.0015        |
+| 0.0618 | 8000   | 0.0015        |
+| 0.0657 | 8500   | 0.0015        |
+| 0.0695 | 9000   | 0.0014        |
+| 0.0734 | 9500   | 0.0014        |
+| 0.0773 | 10000  | 0.0014        |
+| 0.0811 | 10500  | 0.0013        |
+| 0.0850 | 11000  | 0.0013        |
+| 0.0888 | 11500  | 0.0013        |
+| 0.0927 | 12000  | 0.0012        |
+| 0.0966 | 12500  | 0.0012        |
+| 0.1004 | 13000  | 0.0012        |
+| 0.1043 | 13500  | 0.0012        |
+| 0.1082 | 14000  | 0.0011        |
+| 0.1120 | 14500  | 0.0011        |
+| 0.1159 | 15000  | 0.0011        |
+| 0.1197 | 15500  | 0.0011        |
+| 0.1236 | 16000  | 0.0011        |
+| 0.1275 | 16500  | 0.001         |
+| 0.1313 | 17000  | 0.001         |
+| 0.1352 | 17500  | 0.001         |
+| 0.1391 | 18000  | 0.001         |
+| 0.1429 | 18500  | 0.001         |
+| 0.1468 | 19000  | 0.0009        |
+| 0.1507 | 19500  | 0.0009        |
+| 0.1545 | 20000  | 0.0009        |
+| 0.1584 | 20500  | 0.0009        |
+| 0.1622 | 21000  | 0.0009        |
+| 0.1661 | 21500  | 0.0008        |
+| 0.1700 | 22000  | 0.0008        |
+| 0.1738 | 22500  | 0.0008        |
+| 0.1777 | 23000  | 0.0008        |
+| 0.1816 | 23500  | 0.0008        |
+| 0.1854 | 24000  | 0.0008        |
+| 0.1893 | 24500  | 0.0008        |
+| 0.1931 | 25000  | 0.0008        |
+| 0.1970 | 25500  | 0.0007        |
+| 0.2009 | 26000  | 0.0007        |
+| 0.2047 | 26500  | 0.0007        |
+| 0.2086 | 27000  | 0.0007        |
+| 0.2125 | 27500  | 0.0007        |
+| 0.2163 | 28000  | 0.0007        |
+| 0.2202 | 28500  | 0.0007        |
+| 0.2240 | 29000  | 0.0007        |
+| 0.2279 | 29500  | 0.0007        |
+| 0.2318 | 30000  | 0.0007        |
+| 0.2356 | 30500  | 0.0007        |
+| 0.2395 | 31000  | 0.0007        |
+| 0.2434 | 31500  | 0.0006        |
+| 0.2472 | 32000  | 0.0006        |
+| 0.2511 | 32500  | 0.0006        |
+| 0.2550 | 33000  | 0.0006        |
+| 0.2588 | 33500  | 0.0006        |
+| 0.2627 | 34000  | 0.0006        |
+| 0.2665 | 34500  | 0.0006        |
+| 0.2704 | 35000  | 0.0006        |
+| 0.2743 | 35500  | 0.0006        |
+| 0.2781 | 36000  | 0.0006        |
+| 0.2820 | 36500  | 0.0006        |
+| 0.2859 | 37000  | 0.0006        |
+| 0.2897 | 37500  | 0.0006        |
+| 0.2936 | 38000  | 0.0006        |
+| 0.2974 | 38500  | 0.0006        |
+| 0.3013 | 39000  | 0.0006        |
+| 0.3052 | 39500  | 0.0006        |
+| 0.3090 | 40000  | 0.0006        |
+| 0.3129 | 40500  | 0.0006        |
+| 0.3168 | 41000  | 0.0006        |
+| 0.3206 | 41500  | 0.0005        |
+| 0.3245 | 42000  | 0.0005        |
+| 0.3283 | 42500  | 0.0005        |
+| 0.3322 | 43000  | 0.0005        |
+| 0.3361 | 43500  | 0.0005        |
+| 0.3399 | 44000  | 0.0005        |
+| 0.3438 | 44500  | 0.0005        |
+| 0.3477 | 45000  | 0.0005        |
+| 0.3515 | 45500  | 0.0005        |
+| 0.3554 | 46000  | 0.0005        |
+| 0.3592 | 46500  | 0.0005        |
+| 0.3631 | 47000  | 0.0005        |
+| 0.3670 | 47500  | 0.0005        |
+| 0.3708 | 48000  | 0.0005        |
+| 0.3747 | 48500  | 0.0005        |
+| 0.3786 | 49000  | 0.0005        |
+| 0.3824 | 49500  | 0.0005        |
+| 0.3863 | 50000  | 0.0005        |
+| 0.3902 | 50500  | 0.0005        |
+| 0.3940 | 51000  | 0.0005        |
+| 0.3979 | 51500  | 0.0005        |
+| 0.4017 | 52000  | 0.0005        |
+| 0.4056 | 52500  | 0.0005        |
+| 0.4095 | 53000  | 0.0005        |
+| 0.4133 | 53500  | 0.0005        |
+| 0.4172 | 54000  | 0.0005        |
+| 0.4211 | 54500  | 0.0005        |
+| 0.4249 | 55000  | 0.0005        |
+| 0.4288 | 55500  | 0.0005        |
+| 0.4326 | 56000  | 0.0005        |
+| 0.4365 | 56500  | 0.0005        |
+| 0.4404 | 57000  | 0.0005        |
+| 0.4442 | 57500  | 0.0005        |
+| 0.4481 | 58000  | 0.0005        |
+| 0.4520 | 58500  | 0.0005        |
+| 0.4558 | 59000  | 0.0005        |
+| 0.4597 | 59500  | 0.0005        |
+| 0.4635 | 60000  | 0.0005        |
+| 0.4674 | 60500  | 0.0005        |
+| 0.4713 | 61000  | 0.0005        |
+| 0.4751 | 61500  | 0.0005        |
+| 0.4790 | 62000  | 0.0005        |
+| 0.4829 | 62500  | 0.0005        |
+| 0.4867 | 63000  | 0.0005        |
+| 0.4906 | 63500  | 0.0005        |
+| 0.4944 | 64000  | 0.0005        |
+| 0.4983 | 64500  | 0.0005        |
+| 0.5022 | 65000  | 0.0005        |
+| 0.5060 | 65500  | 0.0004        |
+| 0.5099 | 66000  | 0.0004        |
+| 0.5138 | 66500  | 0.0004        |
+| 0.5176 | 67000  | 0.0004        |
+| 0.5215 | 67500  | 0.0004        |
+| 0.5254 | 68000  | 0.0004        |
+| 0.5292 | 68500  | 0.0004        |
+| 0.5331 | 69000  | 0.0004        |
+| 0.5369 | 69500  | 0.0004        |
+| 0.5408 | 70000  | 0.0004        |
+| 0.5447 | 70500  | 0.0004        |
+| 0.5485 | 71000  | 0.0004        |
+| 0.5524 | 71500  | 0.0004        |
+| 0.5563 | 72000  | 0.0004        |
+| 0.5601 | 72500  | 0.0004        |
+| 0.5640 | 73000  | 0.0004        |
+| 0.5678 | 73500  | 0.0004        |
+| 0.5717 | 74000  | 0.0004        |
+| 0.5756 | 74500  | 0.0004        |
+| 0.5794 | 75000  | 0.0004        |
+| 0.5833 | 75500  | 0.0004        |
+| 0.5872 | 76000  | 0.0004        |
+| 0.5910 | 76500  | 0.0004        |
+| 0.5949 | 77000  | 0.0004        |
+| 0.5987 | 77500  | 0.0004        |
+| 0.6026 | 78000  | 0.0004        |
+| 0.6065 | 78500  | 0.0004        |
+| 0.6103 | 79000  | 0.0004        |
+| 0.6142 | 79500  | 0.0004        |
+| 0.6181 | 80000  | 0.0004        |
+| 0.6219 | 80500  | 0.0004        |
+| 0.6258 | 81000  | 0.0004        |
+| 0.6296 | 81500  | 0.0004        |
+| 0.6335 | 82000  | 0.0004        |
+| 0.6374 | 82500  | 0.0004        |
+| 0.6412 | 83000  | 0.0004        |
+| 0.6451 | 83500  | 0.0004        |
+| 0.6490 | 84000  | 0.0004        |
+| 0.6528 | 84500  | 0.0004        |
+| 0.6567 | 85000  | 0.0004        |
+| 0.6606 | 85500  | 0.0004        |
+| 0.6644 | 86000  | 0.0004        |
+| 0.6683 | 86500  | 0.0004        |
+| 0.6721 | 87000  | 0.0004        |
+| 0.6760 | 87500  | 0.0004        |
+| 0.6799 | 88000  | 0.0004        |
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+| 0.6876 | 89000  | 0.0004        |
+| 0.6915 | 89500  | 0.0004        |
+| 0.6953 | 90000  | 0.0004        |
+| 0.6992 | 90500  | 0.0004        |
+| 0.7030 | 91000  | 0.0004        |
+| 0.7069 | 91500  | 0.0004        |
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+| 0.7146 | 92500  | 0.0004        |
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+| 0.7224 | 93500  | 0.0004        |
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+| 1.2014 | 155500 | 0.0003        |
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+| 1.2941 | 167500 | 0.0003        |
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+| 1.3018 | 168500 | 0.0003        |
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+| 1.3172 | 170500 | 0.0003        |
+| 1.3211 | 171000 | 0.0003        |
+| 1.3250 | 171500 | 0.0003        |
+| 1.3288 | 172000 | 0.0003        |
+| 1.3327 | 172500 | 0.0003        |
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+| 1.3404 | 173500 | 0.0003        |
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+| 1.3481 | 174500 | 0.0003        |
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+| 1.4988 | 194000 | 0.0003        |
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+| 2.0203 | 261500 | 0.0003        |
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+| 2.1091 | 273000 | 0.0003        |
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+| 2.1169 | 274000 | 0.0003        |
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+| 2.5341 | 328000 | 0.0003        |
+| 2.5379 | 328500 | 0.0003        |
+| 2.5418 | 329000 | 0.0003        |
+| 2.5456 | 329500 | 0.0003        |
+| 2.5495 | 330000 | 0.0003        |
+| 2.5534 | 330500 | 0.0003        |
+| 2.5572 | 331000 | 0.0003        |
+| 2.5611 | 331500 | 0.0003        |
+| 2.5650 | 332000 | 0.0003        |
+| 2.5688 | 332500 | 0.0003        |
+| 2.5727 | 333000 | 0.0003        |
+| 2.5765 | 333500 | 0.0003        |
+| 2.5804 | 334000 | 0.0003        |
+| 2.5843 | 334500 | 0.0003        |
+| 2.5881 | 335000 | 0.0003        |
+| 2.5920 | 335500 | 0.0003        |
+| 2.5959 | 336000 | 0.0003        |
+| 2.5997 | 336500 | 0.0003        |
+| 2.6036 | 337000 | 0.0003        |
+| 2.6074 | 337500 | 0.0003        |
+| 2.6113 | 338000 | 0.0003        |
+| 2.6152 | 338500 | 0.0003        |
+| 2.6190 | 339000 | 0.0003        |
+| 2.6229 | 339500 | 0.0003        |
+| 2.6268 | 340000 | 0.0003        |
+| 2.6306 | 340500 | 0.0003        |
+| 2.6345 | 341000 | 0.0003        |
+| 2.6383 | 341500 | 0.0003        |
+| 2.6422 | 342000 | 0.0003        |
+| 2.6461 | 342500 | 0.0003        |
+| 2.6499 | 343000 | 0.0003        |
+| 2.6538 | 343500 | 0.0003        |
+| 2.6577 | 344000 | 0.0003        |
+| 2.6615 | 344500 | 0.0003        |
+| 2.6654 | 345000 | 0.0003        |
+| 2.6693 | 345500 | 0.0003        |
+| 2.6731 | 346000 | 0.0003        |
+| 2.6770 | 346500 | 0.0003        |
+| 2.6808 | 347000 | 0.0003        |
+| 2.6847 | 347500 | 0.0003        |
+| 2.6886 | 348000 | 0.0003        |
+| 2.6924 | 348500 | 0.0003        |
+| 2.6963 | 349000 | 0.0003        |
+| 2.7002 | 349500 | 0.0003        |
+| 2.7040 | 350000 | 0.0003        |
+| 2.7079 | 350500 | 0.0003        |
+| 2.7117 | 351000 | 0.0003        |
+| 2.7156 | 351500 | 0.0003        |
+| 2.7195 | 352000 | 0.0003        |
+| 2.7233 | 352500 | 0.0003        |
+| 2.7272 | 353000 | 0.0003        |
+| 2.7311 | 353500 | 0.0003        |
+| 2.7349 | 354000 | 0.0003        |
+| 2.7388 | 354500 | 0.0003        |
+| 2.7426 | 355000 | 0.0003        |
+| 2.7465 | 355500 | 0.0003        |
+| 2.7504 | 356000 | 0.0003        |
+| 2.7542 | 356500 | 0.0003        |
+| 2.7581 | 357000 | 0.0003        |
+| 2.7620 | 357500 | 0.0003        |
+| 2.7658 | 358000 | 0.0003        |
+| 2.7697 | 358500 | 0.0003        |
+| 2.7736 | 359000 | 0.0003        |
+| 2.7774 | 359500 | 0.0003        |
+| 2.7813 | 360000 | 0.0003        |
+| 2.7851 | 360500 | 0.0003        |
+| 2.7890 | 361000 | 0.0003        |
+| 2.7929 | 361500 | 0.0003        |
+| 2.7967 | 362000 | 0.0003        |
+| 2.8006 | 362500 | 0.0003        |
+| 2.8045 | 363000 | 0.0003        |
+| 2.8083 | 363500 | 0.0003        |
+| 2.8122 | 364000 | 0.0003        |
+| 2.8160 | 364500 | 0.0003        |
+| 2.8199 | 365000 | 0.0003        |
+| 2.8238 | 365500 | 0.0003        |
+| 2.8276 | 366000 | 0.0003        |
+| 2.8315 | 366500 | 0.0003        |
+| 2.8354 | 367000 | 0.0003        |
+| 2.8392 | 367500 | 0.0003        |
+| 2.8431 | 368000 | 0.0003        |
+| 2.8469 | 368500 | 0.0003        |
+| 2.8508 | 369000 | 0.0003        |
+| 2.8547 | 369500 | 0.0003        |
+| 2.8585 | 370000 | 0.0003        |
+| 2.8624 | 370500 | 0.0003        |
+| 2.8663 | 371000 | 0.0003        |
+| 2.8701 | 371500 | 0.0003        |
+| 2.8740 | 372000 | 0.0003        |
+| 2.8778 | 372500 | 0.0003        |
+| 2.8817 | 373000 | 0.0003        |
+| 2.8856 | 373500 | 0.0003        |
+| 2.8894 | 374000 | 0.0003        |
+| 2.8933 | 374500 | 0.0003        |
+| 2.8972 | 375000 | 0.0003        |
+| 2.9010 | 375500 | 0.0003        |
+| 2.9049 | 376000 | 0.0003        |
+| 2.9088 | 376500 | 0.0003        |
+| 2.9126 | 377000 | 0.0003        |
+| 2.9165 | 377500 | 0.0003        |
+| 2.9203 | 378000 | 0.0003        |
+| 2.9242 | 378500 | 0.0003        |
+| 2.9281 | 379000 | 0.0003        |
+| 2.9319 | 379500 | 0.0003        |
+| 2.9358 | 380000 | 0.0003        |
+| 2.9397 | 380500 | 0.0003        |
+| 2.9435 | 381000 | 0.0003        |
+| 2.9474 | 381500 | 0.0003        |
+| 2.9512 | 382000 | 0.0003        |
+| 2.9551 | 382500 | 0.0003        |
+| 2.9590 | 383000 | 0.0003        |
+| 2.9628 | 383500 | 0.0003        |
+| 2.9667 | 384000 | 0.0003        |
+| 2.9706 | 384500 | 0.0003        |
+| 2.9744 | 385000 | 0.0003        |
+| 2.9783 | 385500 | 0.0003        |
+| 2.9821 | 386000 | 0.0003        |
+| 2.9860 | 386500 | 0.0003        |
+| 2.9899 | 387000 | 0.0003        |
+| 2.9937 | 387500 | 0.0003        |
+| 2.9976 | 388000 | 0.0003        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.4
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.7.0
+- 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",
+}
+```
+
+#### MSELoss
+```bibtex
+@inproceedings{reimers-2020-multilingual-sentence-bert,
+    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2004.09813",
+}
+```
+
+<!--
+## 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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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50001
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.4",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 120784024

modules.json

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

optimizer.pt

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+size 240447226

rng_state.pth

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scheduler.pt

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+size 1064

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "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": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "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

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
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+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "PreTrainedTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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