Update README.md

README.md

@@ -1,1154 +1,179 @@
 ---
+language:
+- en
+- az
+license: cc-by-4.0
 tags:
-- sentence-transformers
+- sentence-embeddings
 - 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.
+- text-embeddings
+- bilingual
+- azerbaijani
+- english
+- all-minilm-l6-v2
+- bge-small-en-v1.5
+- distillation
 pipeline_tag: sentence-similarity
-library_name: sentence-transformers
+model-index:
+- name: Lroc/az-en-MiniLM-L6-v2-30M
+  results:
+  - task:
+      type: Semantic Textual Similarity
+      name: Semantic Textual Similarity (Azerbaijani)
+    dataset:
+      name: Azerbaijani STS Benchmarks (Average)
+      type: LocalDoc/Azerbaijani-STS-Average
+    metrics:
+    - type: Pearson Correlation
+      value: 0.7266
+      name: Average Pearson
+      verified: false
 ---
 
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+# Bilingual Azerbaijani-English Sentence Embedding Model (az-en-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.
+This is a sentence-transformer model that maps sentences & paragraphs in **Azerbaijani (az)** and **English (en)** to a 384-dimensional dense vector space. 
+It is designed for tasks like semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering for these two languages.
 
-## 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
+The model is based on `sentence-transformers/all-MiniLM-L6-v2` and was fine-tuned using knowledge distillation from the high-performance `BAAI/bge-small-en-v1.5` English embedding model. 
+A custom bilingual (Azerbaijani-English) SentencePiece Unigram tokenizer with a vocabulary of ~50k was trained from scratch and is used by this model.
 
-### Direct Usage (Sentence Transformers)
 
-First install the Sentence Transformers library:
+## Model Details
 
+*   **Base Architecture:** `sentence-transformers/all-MiniLM-L6-v2` (6 layers, 384 hidden dimension, 12 attention heads)
+*   **Parameters:** ~30.2 Million (after vocabulary expansion)
+*   **Tokenizer:** Custom bilingual (AZ-EN) SentencePiece Unigram, vocab size ~50k. Available at [LocalDoc/az-en-unigram-tokenizer-50k](https://huggingface.co/LocalDoc/az-en-unigram-tokenizer-50k).
+*   **Output Dimension:** 384
+*   **Max Sequence Length:** 512 tokens
+*   **Training:** Fine-tuned for 3 epochs on a parallel corpus of ~4.14 million Azerbaijani-English sentence pairs using MSELoss for knowledge distillation from `BAAI/bge-small-en-v1.5`.
+
+## Performance on Azerbaijani STS Benchmarks
+
+This model demonstrates strong performance on Azerbaijani Semantic Textual Similarity (STS) tasks  [LocalDoc-Azerbaijan/STS-Benchmark](https://github.com/LocalDoc-Azerbaijan/STS-Benchmark), achieving results competitive with, and in some cases surpassing, larger multilingual models.
+
+The following results were obtained after **3 epochs** of training :
+
+| Dataset | Pearson Correlation |
+| :-------------------------------------- | :------------------: |
+| LocalDoc/Azerbaijani-STSBenchmark | 0.7595 |
+| LocalDoc/Azerbaijani-biosses-sts | 0.7410 |
+| LocalDoc/Azerbaijani-sickr-sts | 0.7432 |
+| LocalDoc/Azerbaijani-sts12-sts | 0.7644 |
+| LocalDoc/Azerbaijani-sts13-sts | 0.6336 |
+| LocalDoc/Azerbaijani-sts15-sts | 0.7597 |
+| LocalDoc/Azerbaijani-sts16-sts | 0.6848 |
+| **Average Pearson** | **0.7266** |
+
+**Comparison with other models on (assumed) Azerbaijani STS Benchmarks (Average Pearson):**
+
+*   TEmA-small: `0.7959`
+*   Cohere/embed-multilingual-v3.0: `0.7823`
+*   BAAI/bge-m3: `0.7577`
+*   intfloat/multilingual-e5-large-instruct: `0.7377`
+*   Cohere/embed-multilingual-v2.0: `0.7318`
+*   intfloat/multilingual-e5-large: `0.7280`
+*   OpenAI/text-embedding-3-large: `0.7288`
+*   **LocalDoc/az-en-MiniLM-L6-v2: `0.7266`**
+*   sentence-transformers/LaBSE: `0.7250`
+*   intfloat/multilingual-e5-small: `0.7242`
+*   Cohere/embed-multilingual-light-v3.0: `0.7142`
+*   intfloat/multilingual-e5-base: `0.6960`
+
+
+## How to Use
+
+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.',
+model_id = "LocalDoc/az-en-MiniLM-L6-v2"
+
+try:
+    model = SentenceTransformer(model_id)
+    print(f"Model {model_id} loaded successfully!")
+except Exception as e:
+    print(f"Failed to load model. Ensure the tokenizer 'LocalDoc/az-en-unigram-tokenizer-50k' is accessible and its dependencies (protobuf, sentencepiece_model_pb2.py) are met if loading fails.")
+    print(f"Error: {e}")
+    # You might need to ensure the tokenizer can be loaded.
+    # If the tokenizer requires it (it shouldn't if it's correctly packaged on the Hub by your tokenizer repo):
+    # !pip install protobuf
+    # !wget -P ./az_en_tokenizer_hf/ https://raw.githubusercontent.com/google/sentencepiece/master/python/src/sentencepiece/sentencepiece_model_pb2.py
+    # model = SentenceTransformer(model_id)
+
+
+# Example Azerbaijani sentences
+sentences_az = [
+    "Azərbaycanın paytaxtı Bakı şəhəridir.",
+    "Bu gün hava çox istidir."
 ]
-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.*
--->
+# Example English sentences
+sentences_en = [
+    "The capital of Azerbaijan is the city of Baku.",
+    "The weather is very hot today.",
+    "I enjoy reading books."
+]
 
-<!--
-## Bias, Risks and Limitations
+print("\nEncoding Azerbaijani sentences...")
+embeddings_az = model.encode(sentences_az)
+for sent, emb in zip(sentences_az, embeddings_az):
+    print(f"Sentence: {sent}")
+    print(f"Embedding shape: {emb.shape}, first 3 dims: {emb[:3]}\n")
+
+print("Encoding English sentences...")
+embeddings_en = model.encode(sentences_en)
+for sent, emb in zip(sentences_en, embeddings_en):
+    print(f"Sentence: {sent}")
+    print(f"Embedding shape: {emb.shape}, first 3 dims: {emb[:3]}\n")
+```
 
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+# Example of calculating similarity
 
-<!--
-### Recommendations
+```python
+from sentence_transformers.util import cos_sim
 
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+similarity_matrix = cos_sim(embeddings_az[0], embeddings_en[0])
+print(f"Similarity between '{sentences_az[0]}' and '{sentences_en[0]}': {similarity_matrix.item():.4f}")
 
-## Training Details
+similarity_matrix_diff = cos_sim(embeddings_az[0], embeddings_en[2])
+print(f"Similarity between '{sentences_az[0]}' and '{sentences_en[2]}': {similarity_matrix_diff.item():.4f}")
+```
 
-### Training Dataset
+## Training
 
-#### Unnamed Dataset
+This model was fine-tuned from `sentence-transformers/all-MiniLM-L6-v2` using a **knowledge distillation** setup.
 
-* 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)
+- **Teacher Model:** [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5) (used to generate target embeddings for English sentences).
+- **Student Model:** Initialized from `sentence-transformers/all-MiniLM-L6-v2`.
+- **Tokenizer:** A custom bilingual (Azerbaijani-English) [SentencePiece Unigram tokenizer](https://huggingface.co/LocalDoc/az-en-unigram-tokenizer-50k) (`LocalDoc/az-en-unigram-tokenizer-50k`) was used.  
+  The student model's token embedding layer was resized to match the new vocabulary size (~50k).
+- **Training Data:** A parallel corpus of approximately **4.14 million Azerbaijani-English sentence pairs**.
+- **Loss Function:** `MSELoss` — the student model was trained to produce embeddings for both Azerbaijani and English sentences that are similar to the teacher model's embeddings for the corresponding **English** sentences.
 
 ### 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
+- **Epochs:** 3  
+- **Batch Size:** 64  
+- **Max Sequence Length:** 512  
+- **Learning Rate:** 3e-4  
+- **Warmup Ratio:** 0.15
 
-</details>
 
-### Training Logs
-<details><summary>Click to expand</summary>
+## CC BY 4.0 License — What It Allows
 
-| 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        |
-| 0.6837 | 88500  | 0.0004        |
-| 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        |
-| 0.7108 | 92000  | 0.0004        |
-| 0.7146 | 92500  | 0.0004        |
-| 0.7185 | 93000  | 0.0004        |
-| 0.7224 | 93500  | 0.0004        |
-| 0.7262 | 94000  | 0.0004        |
-| 0.7301 | 94500  | 0.0004        |
-| 0.7339 | 95000  | 0.0004        |
-| 0.7378 | 95500  | 0.0004        |
-| 0.7417 | 96000  | 0.0004        |
-| 0.7455 | 96500  | 0.0004        |
-| 0.7494 | 97000  | 0.0004        |
-| 0.7533 | 97500  | 0.0004        |
-| 0.7571 | 98000  | 0.0004        |
-| 0.7610 | 98500  | 0.0004        |
-| 0.7649 | 99000  | 0.0004        |
-| 0.7687 | 99500  | 0.0004        |
-| 0.7726 | 100000 | 0.0004        |
-| 0.7764 | 100500 | 0.0004        |
-| 0.7803 | 101000 | 0.0004        |
-| 0.7842 | 101500 | 0.0004        |
-| 0.7880 | 102000 | 0.0004        |
-| 0.7919 | 102500 | 0.0004        |
-| 0.7958 | 103000 | 0.0004        |
-| 0.7996 | 103500 | 0.0004        |
-| 0.8035 | 104000 | 0.0004        |
-| 0.8073 | 104500 | 0.0004        |
-| 0.8112 | 105000 | 0.0004        |
-| 0.8151 | 105500 | 0.0004        |
-| 0.8189 | 106000 | 0.0004        |
-| 0.8228 | 106500 | 0.0004        |
-| 0.8267 | 107000 | 0.0004        |
-| 0.8305 | 107500 | 0.0004        |
-| 0.8344 | 108000 | 0.0004        |
-| 0.8382 | 108500 | 0.0004        |
-| 0.8421 | 109000 | 0.0004        |
-| 0.8460 | 109500 | 0.0004        |
-| 0.8498 | 110000 | 0.0004        |
-| 0.8537 | 110500 | 0.0004        |
-| 0.8576 | 111000 | 0.0004        |
-| 0.8614 | 111500 | 0.0004        |
-| 0.8653 | 112000 | 0.0004        |
-| 0.8691 | 112500 | 0.0004        |
-| 0.8730 | 113000 | 0.0004        |
-| 0.8769 | 113500 | 0.0004        |
-| 0.8807 | 114000 | 0.0004        |
-| 0.8846 | 114500 | 0.0004        |
-| 0.8885 | 115000 | 0.0004        |
-| 0.8923 | 115500 | 0.0004        |
-| 0.8962 | 116000 | 0.0004        |
-| 0.9001 | 116500 | 0.0004        |
-| 0.9039 | 117000 | 0.0004        |
-| 0.9078 | 117500 | 0.0004        |
-| 0.9116 | 118000 | 0.0004        |
-| 0.9155 | 118500 | 0.0004        |
-| 0.9194 | 119000 | 0.0004        |
-| 0.9232 | 119500 | 0.0004        |
-| 0.9271 | 120000 | 0.0004        |
-| 0.9310 | 120500 | 0.0004        |
-| 0.9348 | 121000 | 0.0004        |
-| 0.9387 | 121500 | 0.0004        |
-| 0.9425 | 122000 | 0.0004        |
-| 0.9464 | 122500 | 0.0004        |
-| 0.9503 | 123000 | 0.0004        |
-| 0.9541 | 123500 | 0.0004        |
-| 0.9580 | 124000 | 0.0004        |
-| 0.9619 | 124500 | 0.0004        |
-| 0.9657 | 125000 | 0.0004        |
-| 0.9696 | 125500 | 0.0004        |
-| 0.9734 | 126000 | 0.0004        |
-| 0.9773 | 126500 | 0.0004        |
-| 0.9812 | 127000 | 0.0004        |
-| 0.9850 | 127500 | 0.0004        |
-| 0.9889 | 128000 | 0.0004        |
-| 0.9928 | 128500 | 0.0004        |
-| 0.9966 | 129000 | 0.0004        |
-| 1.0005 | 129500 | 0.0004        |
-| 1.0043 | 130000 | 0.0004        |
-| 1.0082 | 130500 | 0.0004        |
-| 1.0121 | 131000 | 0.0004        |
-| 1.0159 | 131500 | 0.0004        |
-| 1.0198 | 132000 | 0.0004        |
-| 1.0237 | 132500 | 0.0004        |
-| 1.0275 | 133000 | 0.0004        |
-| 1.0314 | 133500 | 0.0004        |
-| 1.0353 | 134000 | 0.0004        |
-| 1.0391 | 134500 | 0.0004        |
-| 1.0430 | 135000 | 0.0004        |
-| 1.0468 | 135500 | 0.0004        |
-| 1.0507 | 136000 | 0.0004        |
-| 1.0546 | 136500 | 0.0004        |
-| 1.0584 | 137000 | 0.0004        |
-| 1.0623 | 137500 | 0.0004        |
-| 1.0662 | 138000 | 0.0004        |
-| 1.0700 | 138500 | 0.0004        |
-| 1.0739 | 139000 | 0.0004        |
-| 1.0777 | 139500 | 0.0004        |
-| 1.0816 | 140000 | 0.0004        |
-| 1.0855 | 140500 | 0.0004        |
-| 1.0893 | 141000 | 0.0004        |
-| 1.0932 | 141500 | 0.0004        |
-| 1.0971 | 142000 | 0.0004        |
-| 1.1009 | 142500 | 0.0004        |
-| 1.1048 | 143000 | 0.0004        |
-| 1.1086 | 143500 | 0.0004        |
-| 1.1125 | 144000 | 0.0004        |
-| 1.1164 | 144500 | 0.0004        |
-| 1.1202 | 145000 | 0.0004        |
-| 1.1241 | 145500 | 0.0004        |
-| 1.1280 | 146000 | 0.0004        |
-| 1.1318 | 146500 | 0.0004        |
-| 1.1357 | 147000 | 0.0004        |
-| 1.1396 | 147500 | 0.0004        |
-| 1.1434 | 148000 | 0.0004        |
-| 1.1473 | 148500 | 0.0004        |
-| 1.1511 | 149000 | 0.0004        |
-| 1.1550 | 149500 | 0.0004        |
-| 1.1589 | 150000 | 0.0004        |
-| 1.1627 | 150500 | 0.0004        |
-| 1.1666 | 151000 | 0.0004        |
-| 1.1705 | 151500 | 0.0004        |
-| 1.1743 | 152000 | 0.0004        |
-| 1.1782 | 152500 | 0.0004        |
-| 1.1820 | 153000 | 0.0004        |
-| 1.1859 | 153500 | 0.0004        |
-| 1.1898 | 154000 | 0.0004        |
-| 1.1936 | 154500 | 0.0004        |
-| 1.1975 | 155000 | 0.0004        |
-| 1.2014 | 155500 | 0.0003        |
-| 1.2052 | 156000 | 0.0003        |
-| 1.2091 | 156500 | 0.0004        |
-| 1.2129 | 157000 | 0.0003        |
-| 1.2168 | 157500 | 0.0004        |
-| 1.2207 | 158000 | 0.0003        |
-| 1.2245 | 158500 | 0.0003        |
-| 1.2284 | 159000 | 0.0003        |
-| 1.2323 | 159500 | 0.0003        |
-| 1.2361 | 160000 | 0.0003        |
-| 1.2400 | 160500 | 0.0003        |
-| 1.2438 | 161000 | 0.0003        |
-| 1.2477 | 161500 | 0.0003        |
-| 1.2516 | 162000 | 0.0003        |
-| 1.2554 | 162500 | 0.0003        |
-| 1.2593 | 163000 | 0.0003        |
-| 1.2632 | 163500 | 0.0003        |
-| 1.2670 | 164000 | 0.0003        |
-| 1.2709 | 164500 | 0.0003        |
-| 1.2748 | 165000 | 0.0003        |
-| 1.2786 | 165500 | 0.0003        |
-| 1.2825 | 166000 | 0.0003        |
-| 1.2863 | 166500 | 0.0003        |
-| 1.2902 | 167000 | 0.0003        |
-| 1.2941 | 167500 | 0.0003        |
-| 1.2979 | 168000 | 0.0003        |
-| 1.3018 | 168500 | 0.0003        |
-| 1.3057 | 169000 | 0.0003        |
-| 1.3095 | 169500 | 0.0003        |
-| 1.3134 | 170000 | 0.0003        |
-| 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        |
-| 1.3366 | 173000 | 0.0003        |
-| 1.3404 | 173500 | 0.0003        |
-| 1.3443 | 174000 | 0.0003        |
-| 1.3481 | 174500 | 0.0003        |
-| 1.3520 | 175000 | 0.0003        |
-| 1.3559 | 175500 | 0.0003        |
-| 1.3597 | 176000 | 0.0003        |
-| 1.3636 | 176500 | 0.0003        |
-| 1.3675 | 177000 | 0.0003        |
-| 1.3713 | 177500 | 0.0003        |
-| 1.3752 | 178000 | 0.0003        |
-| 1.3790 | 178500 | 0.0003        |
-| 1.3829 | 179000 | 0.0003        |
-| 1.3868 | 179500 | 0.0003        |
-| 1.3906 | 180000 | 0.0003        |
-| 1.3945 | 180500 | 0.0003        |
-| 1.3984 | 181000 | 0.0003        |
-| 1.4022 | 181500 | 0.0003        |
-| 1.4061 | 182000 | 0.0003        |
-| 1.4100 | 182500 | 0.0003        |
-| 1.4138 | 183000 | 0.0003        |
-| 1.4177 | 183500 | 0.0003        |
-| 1.4215 | 184000 | 0.0003        |
-| 1.4254 | 184500 | 0.0003        |
-| 1.4293 | 185000 | 0.0003        |
-| 1.4331 | 185500 | 0.0003        |
-| 1.4370 | 186000 | 0.0003        |
-| 1.4409 | 186500 | 0.0003        |
-| 1.4447 | 187000 | 0.0003        |
-| 1.4486 | 187500 | 0.0003        |
-| 1.4524 | 188000 | 0.0003        |
-| 1.4563 | 188500 | 0.0003        |
-| 1.4602 | 189000 | 0.0003        |
-| 1.4640 | 189500 | 0.0003        |
-| 1.4679 | 190000 | 0.0003        |
-| 1.4718 | 190500 | 0.0003        |
-| 1.4756 | 191000 | 0.0003        |
-| 1.4795 | 191500 | 0.0003        |
-| 1.4833 | 192000 | 0.0003        |
-| 1.4872 | 192500 | 0.0003        |
-| 1.4911 | 193000 | 0.0003        |
-| 1.4949 | 193500 | 0.0003        |
-| 1.4988 | 194000 | 0.0003        |
-| 1.5027 | 194500 | 0.0003        |
-| 1.5065 | 195000 | 0.0003        |
-| 1.5104 | 195500 | 0.0003        |
-| 1.5143 | 196000 | 0.0003        |
-| 1.5181 | 196500 | 0.0003        |
-| 1.5220 | 197000 | 0.0003        |
-| 1.5258 | 197500 | 0.0003        |
-| 1.5297 | 198000 | 0.0003        |
-| 1.5336 | 198500 | 0.0003        |
-| 1.5374 | 199000 | 0.0003        |
-| 1.5413 | 199500 | 0.0003        |
-| 1.5452 | 200000 | 0.0003        |
-| 1.5490 | 200500 | 0.0003        |
-| 1.5529 | 201000 | 0.0003        |
-| 1.5567 | 201500 | 0.0003        |
-| 1.5606 | 202000 | 0.0003        |
-| 1.5645 | 202500 | 0.0003        |
-| 1.5683 | 203000 | 0.0003        |
-| 1.5722 | 203500 | 0.0003        |
-| 1.5761 | 204000 | 0.0003        |
-| 1.5799 | 204500 | 0.0003        |
-| 1.5838 | 205000 | 0.0003        |
-| 1.5876 | 205500 | 0.0003        |
-| 1.5915 | 206000 | 0.0003        |
-| 1.5954 | 206500 | 0.0003        |
-| 1.5992 | 207000 | 0.0003        |
-| 1.6031 | 207500 | 0.0003        |
-| 1.6070 | 208000 | 0.0003        |
-| 1.6108 | 208500 | 0.0003        |
-| 1.6147 | 209000 | 0.0003        |
-| 1.6185 | 209500 | 0.0003        |
-| 1.6224 | 210000 | 0.0003        |
-| 1.6263 | 210500 | 0.0003        |
-| 1.6301 | 211000 | 0.0003        |
-| 1.6340 | 211500 | 0.0003        |
-| 1.6379 | 212000 | 0.0003        |
-| 1.6417 | 212500 | 0.0003        |
-| 1.6456 | 213000 | 0.0003        |
-| 1.6495 | 213500 | 0.0003        |
-| 1.6533 | 214000 | 0.0003        |
-| 1.6572 | 214500 | 0.0003        |
-| 1.6610 | 215000 | 0.0003        |
-| 1.6649 | 215500 | 0.0003        |
-| 1.6688 | 216000 | 0.0003        |
-| 1.6726 | 216500 | 0.0003        |
-| 1.6765 | 217000 | 0.0003        |
-| 1.6804 | 217500 | 0.0003        |
-| 1.6842 | 218000 | 0.0003        |
-| 1.6881 | 218500 | 0.0003        |
-| 1.6919 | 219000 | 0.0003        |
-| 1.6958 | 219500 | 0.0003        |
-| 1.6997 | 220000 | 0.0003        |
-| 1.7035 | 220500 | 0.0003        |
-| 1.7074 | 221000 | 0.0003        |
-| 1.7113 | 221500 | 0.0003        |
-| 1.7151 | 222000 | 0.0003        |
-| 1.7190 | 222500 | 0.0003        |
-| 1.7228 | 223000 | 0.0003        |
-| 1.7267 | 223500 | 0.0003        |
-| 1.7306 | 224000 | 0.0003        |
-| 1.7344 | 224500 | 0.0003        |
-| 1.7383 | 225000 | 0.0003        |
-| 1.7422 | 225500 | 0.0003        |
-| 1.7460 | 226000 | 0.0003        |
-| 1.7499 | 226500 | 0.0003        |
-| 1.7537 | 227000 | 0.0003        |
-| 1.7576 | 227500 | 0.0003        |
-| 1.7615 | 228000 | 0.0003        |
-| 1.7653 | 228500 | 0.0003        |
-| 1.7692 | 229000 | 0.0003        |
-| 1.7731 | 229500 | 0.0003        |
-| 1.7769 | 230000 | 0.0003        |
-| 1.7808 | 230500 | 0.0003        |
-| 1.7847 | 231000 | 0.0003        |
-| 1.7885 | 231500 | 0.0003        |
-| 1.7924 | 232000 | 0.0003        |
-| 1.7962 | 232500 | 0.0003        |
-| 1.8001 | 233000 | 0.0003        |
-| 1.8040 | 233500 | 0.0003        |
-| 1.8078 | 234000 | 0.0003        |
-| 1.8117 | 234500 | 0.0003        |
-| 1.8156 | 235000 | 0.0003        |
-| 1.8194 | 235500 | 0.0003        |
-| 1.8233 | 236000 | 0.0003        |
-| 1.8271 | 236500 | 0.0003        |
-| 1.8310 | 237000 | 0.0003        |
-| 1.8349 | 237500 | 0.0003        |
-| 1.8387 | 238000 | 0.0003        |
-| 1.8426 | 238500 | 0.0003        |
-| 1.8465 | 239000 | 0.0003        |
-| 1.8503 | 239500 | 0.0003        |
-| 1.8542 | 240000 | 0.0003        |
-| 1.8580 | 240500 | 0.0003        |
-| 1.8619 | 241000 | 0.0003        |
-| 1.8658 | 241500 | 0.0003        |
-| 1.8696 | 242000 | 0.0003        |
-| 1.8735 | 242500 | 0.0003        |
-| 1.8774 | 243000 | 0.0003        |
-| 1.8812 | 243500 | 0.0003        |
-| 1.8851 | 244000 | 0.0003        |
-| 1.8889 | 244500 | 0.0003        |
-| 1.8928 | 245000 | 0.0003        |
-| 1.8967 | 245500 | 0.0003        |
-| 1.9005 | 246000 | 0.0003        |
-| 1.9044 | 246500 | 0.0003        |
-| 1.9083 | 247000 | 0.0003        |
-| 1.9121 | 247500 | 0.0003        |
-| 1.9160 | 248000 | 0.0003        |
-| 1.9199 | 248500 | 0.0003        |
-| 1.9237 | 249000 | 0.0003        |
-| 1.9276 | 249500 | 0.0003        |
-| 1.9314 | 250000 | 0.0003        |
-| 1.9353 | 250500 | 0.0003        |
-| 1.9392 | 251000 | 0.0003        |
-| 1.9430 | 251500 | 0.0003        |
-| 1.9469 | 252000 | 0.0003        |
-| 1.9508 | 252500 | 0.0003        |
-| 1.9546 | 253000 | 0.0003        |
-| 1.9585 | 253500 | 0.0003        |
-| 1.9623 | 254000 | 0.0003        |
-| 1.9662 | 254500 | 0.0003        |
-| 1.9701 | 255000 | 0.0003        |
-| 1.9739 | 255500 | 0.0003        |
-| 1.9778 | 256000 | 0.0003        |
-| 1.9817 | 256500 | 0.0003        |
-| 1.9855 | 257000 | 0.0003        |
-| 1.9894 | 257500 | 0.0003        |
-| 1.9932 | 258000 | 0.0003        |
-| 1.9971 | 258500 | 0.0003        |
-| 2.0010 | 259000 | 0.0003        |
-| 2.0048 | 259500 | 0.0003        |
-| 2.0087 | 260000 | 0.0003        |
-| 2.0126 | 260500 | 0.0003        |
-| 2.0164 | 261000 | 0.0003        |
-| 2.0203 | 261500 | 0.0003        |
-| 2.0242 | 262000 | 0.0003        |
-| 2.0280 | 262500 | 0.0003        |
-| 2.0319 | 263000 | 0.0003        |
-| 2.0357 | 263500 | 0.0003        |
-| 2.0396 | 264000 | 0.0003        |
-| 2.0435 | 264500 | 0.0003        |
-| 2.0473 | 265000 | 0.0003        |
-| 2.0512 | 265500 | 0.0003        |
-| 2.0551 | 266000 | 0.0003        |
-| 2.0589 | 266500 | 0.0003        |
-| 2.0628 | 267000 | 0.0003        |
-| 2.0666 | 267500 | 0.0003        |
-| 2.0705 | 268000 | 0.0003        |
-| 2.0744 | 268500 | 0.0003        |
-| 2.0782 | 269000 | 0.0003        |
-| 2.0821 | 269500 | 0.0003        |
-| 2.0860 | 270000 | 0.0003        |
-| 2.0898 | 270500 | 0.0003        |
-| 2.0937 | 271000 | 0.0003        |
-| 2.0975 | 271500 | 0.0003        |
-| 2.1014 | 272000 | 0.0003        |
-| 2.1053 | 272500 | 0.0003        |
-| 2.1091 | 273000 | 0.0003        |
-| 2.1130 | 273500 | 0.0003        |
-| 2.1169 | 274000 | 0.0003        |
-| 2.1207 | 274500 | 0.0003        |
-| 2.1246 | 275000 | 0.0003        |
-| 2.1284 | 275500 | 0.0003        |
-| 2.1323 | 276000 | 0.0003        |
-| 2.1362 | 276500 | 0.0003        |
-| 2.1400 | 277000 | 0.0003        |
-| 2.1439 | 277500 | 0.0003        |
-| 2.1478 | 278000 | 0.0003        |
-| 2.1516 | 278500 | 0.0003        |
-| 2.1555 | 279000 | 0.0003        |
-| 2.1594 | 279500 | 0.0003        |
-| 2.1632 | 280000 | 0.0003        |
-| 2.1671 | 280500 | 0.0003        |
-| 2.1709 | 281000 | 0.0003        |
-| 2.1748 | 281500 | 0.0003        |
-| 2.1787 | 282000 | 0.0003        |
-| 2.1825 | 282500 | 0.0003        |
-| 2.1864 | 283000 | 0.0003        |
-| 2.1903 | 283500 | 0.0003        |
-| 2.1941 | 284000 | 0.0003        |
-| 2.1980 | 284500 | 0.0003        |
-| 2.2018 | 285000 | 0.0003        |
-| 2.2057 | 285500 | 0.0003        |
-| 2.2096 | 286000 | 0.0003        |
-| 2.2134 | 286500 | 0.0003        |
-| 2.2173 | 287000 | 0.0003        |
-| 2.2212 | 287500 | 0.0003        |
-| 2.2250 | 288000 | 0.0003        |
-| 2.2289 | 288500 | 0.0003        |
-| 2.2327 | 289000 | 0.0003        |
-| 2.2366 | 289500 | 0.0003        |
-| 2.2405 | 290000 | 0.0003        |
-| 2.2443 | 290500 | 0.0003        |
-| 2.2482 | 291000 | 0.0003        |
-| 2.2521 | 291500 | 0.0003        |
-| 2.2559 | 292000 | 0.0003        |
-| 2.2598 | 292500 | 0.0003        |
-| 2.2636 | 293000 | 0.0003        |
-| 2.2675 | 293500 | 0.0003        |
-| 2.2714 | 294000 | 0.0003        |
-| 2.2752 | 294500 | 0.0003        |
-| 2.2791 | 295000 | 0.0003        |
-| 2.2830 | 295500 | 0.0003        |
-| 2.2868 | 296000 | 0.0003        |
-| 2.2907 | 296500 | 0.0003        |
-| 2.2946 | 297000 | 0.0003        |
-| 2.2984 | 297500 | 0.0003        |
-| 2.3023 | 298000 | 0.0003        |
-| 2.3061 | 298500 | 0.0003        |
-| 2.3100 | 299000 | 0.0003        |
-| 2.3139 | 299500 | 0.0003        |
-| 2.3177 | 300000 | 0.0003        |
-| 2.3216 | 300500 | 0.0003        |
-| 2.3255 | 301000 | 0.0003        |
-| 2.3293 | 301500 | 0.0003        |
-| 2.3332 | 302000 | 0.0003        |
-| 2.3370 | 302500 | 0.0003        |
-| 2.3409 | 303000 | 0.0003        |
-| 2.3448 | 303500 | 0.0003        |
-| 2.3486 | 304000 | 0.0003        |
-| 2.3525 | 304500 | 0.0003        |
-| 2.3564 | 305000 | 0.0003        |
-| 2.3602 | 305500 | 0.0003        |
-| 2.3641 | 306000 | 0.0003        |
-| 2.3679 | 306500 | 0.0003        |
-| 2.3718 | 307000 | 0.0003        |
-| 2.3757 | 307500 | 0.0003        |
-| 2.3795 | 308000 | 0.0003        |
-| 2.3834 | 308500 | 0.0003        |
-| 2.3873 | 309000 | 0.0003        |
-| 2.3911 | 309500 | 0.0003        |
-| 2.3950 | 310000 | 0.0003        |
-| 2.3989 | 310500 | 0.0003        |
-| 2.4027 | 311000 | 0.0003        |
-| 2.4066 | 311500 | 0.0003        |
-| 2.4104 | 312000 | 0.0003        |
-| 2.4143 | 312500 | 0.0003        |
-| 2.4182 | 313000 | 0.0003        |
-| 2.4220 | 313500 | 0.0003        |
-| 2.4259 | 314000 | 0.0003        |
-| 2.4298 | 314500 | 0.0003        |
-| 2.4336 | 315000 | 0.0003        |
-| 2.4375 | 315500 | 0.0003        |
-| 2.4413 | 316000 | 0.0003        |
-| 2.4452 | 316500 | 0.0003        |
-| 2.4491 | 317000 | 0.0003        |
-| 2.4529 | 317500 | 0.0003        |
-| 2.4568 | 318000 | 0.0003        |
-| 2.4607 | 318500 | 0.0003        |
-| 2.4645 | 319000 | 0.0003        |
-| 2.4684 | 319500 | 0.0003        |
-| 2.4722 | 320000 | 0.0003        |
-| 2.4761 | 320500 | 0.0003        |
-| 2.4800 | 321000 | 0.0003        |
-| 2.4838 | 321500 | 0.0003        |
-| 2.4877 | 322000 | 0.0003        |
-| 2.4916 | 322500 | 0.0003        |
-| 2.4954 | 323000 | 0.0003        |
-| 2.4993 | 323500 | 0.0003        |
-| 2.5031 | 324000 | 0.0003        |
-| 2.5070 | 324500 | 0.0003        |
-| 2.5109 | 325000 | 0.0003        |
-| 2.5147 | 325500 | 0.0003        |
-| 2.5186 | 326000 | 0.0003        |
-| 2.5225 | 326500 | 0.0003        |
-| 2.5263 | 327000 | 0.0003        |
-| 2.5302 | 327500 | 0.0003        |
-| 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        |
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-| 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",
-}
-```
+The **Creative Commons Attribution 4.0 International (CC BY 4.0)** license allows:
 
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--->
+For more information, please refer to the <a target="_blank" href="https://creativecommons.org/licenses/by/4.0/deed.en">CC BY 4.0 license</a>.
 
-<!--
-## Model Card Authors
 
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--->
+## Contact
 
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-## Model Card Contact
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