Add new SentenceTransformer model

.gitattributes

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

1_Pooling/config.json

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

2_Dense/config.json

@@ -0,0 +1,6 @@
+{
+    "in_features": 768,
+    "out_features": 3072,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

2_Dense/model.safetensors

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+size 9437272

3_Dense/config.json

@@ -0,0 +1,6 @@
+{
+    "in_features": 3072,
+    "out_features": 768,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

3_Dense/model.safetensors

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+size 9437272

README.md

@@ -0,0 +1,1061 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:5424
+- loss:MultipleNegativesRankingLoss
+base_model: google/embeddinggemma-300m
+widget:
+- source_sentence: What does the Competition Bureau do?
+  sentences:
+  - What are the requirements for obtaining a Canadian passport?
+  - The Competition Bureau is an independent law enforcement agency that protects
+    and promotes competition for the benefit of Canadian consumers and businesses.
+  - Failure to file an annual or interim management’s discussion and analysis (MD&A)
+    or an annual or interim management report of fund performance (MRFP) is a common
+    failure.
+- source_sentence: What does this website provide information about?
+  sentences:
+  - What are the eligibility requirements for employment insurance benefits?
+  - Register yourself and/or your whole family with Health Care Connect and a care
+    connector will search for a doctor or nurse practitioner who is accepting new
+    patients in your community.
+  - This website provides information about pension plans under provincial and federal
+    pension standards legislation.
+- source_sentence: What impact did the Skills Canada competitions have on young people?
+  sentences:
+  - 'This includes records relating to: employee supervision, leave and time reporting,
+    job description preparation, job classification requests, staffing and recruitment,
+    employer-employee relations, ministry recognition programs, occupational safety
+    and health activities, and ministry training course development and delivery.'
+  - What are the eligibility requirements for the Canada Pension Plan?
+  - It meant a lot for the kids, especially those who had parents who were indifferent
+    to the trades.
+- source_sentence: What game animals can John Arseneault guide hunters for?
+  sentences:
+  - What are the eligibility requirements for the New Brunswick childcare benefit?
+  - Our $70 billion National Housing Strategy is helping build affordable housing
+    supply, including rental housing, across Canada.
+  - John Arseneault offers hunting services for Atlantic salmon, trout, and bass.
+- source_sentence: How can I find information about past Access to Information requests?
+  sentences:
+  - This house style was a popular design from 1890-1900.
+  - What are the eligibility requirements for the Canada Pension Plan?
+  - Search the summaries of completed Access to Information (ATI) requests to find
+    information about ATI requests made to the Government of Canada after January
+    2020.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on google/embeddinggemma-300m
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
+- **Maximum Sequence Length:** 2048 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (4): 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("Neelkumar/my-embedding-gemma-5424")
+# Run inference
+queries = [
+    "How can I find information about past Access to Information requests?",
+]
+documents = [
+    'Search the summaries of completed Access to Information (ATI) requests to find information about ATI requests made to the Government of Canada after January 2020.',
+    'What are the eligibility requirements for the Canada Pension Plan?',
+    'This house style was a popular design from 1890-1900.',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 768] [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.9569,  0.1398, -0.0558]])
+```
+
+<!--
+### 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: 5,424 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                           | negative                                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 15.8 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 32.04 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 15.01 tokens</li><li>max: 42 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                  | positive                                                                                                                                      | negative                                                                        |
+  |:--------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | <code>Quelles mesures les propriétaires peuvent-ils prendre pour éliminer les punaises de lit?</code>   | <code>Les propriétaires peuvent instaurer différentes mesures pour prévenir et éliminer les punaises des lits.</code>                         | <code>Quelles sont les conditions pour obtenir une assurance automobile?</code> |
+  | <code>Comment les pages web du gouvernement de la Saskatchewan sont-elles traduites en français?</code> | <code>Un certain nombre de pages sur le site web du gouvernement de la Saskatchewan ont été traduites professionnellement en français.</code> | <code>Quelles sont les exigences pour obtenir un permis de conduire?</code>     |
+  | <code>How long do plant breeders' rights last in Canada?</code>                                         | <code>Plant breeders receive legal protection for up to 25 years for trees and vines, and 20 years for other plant varieties.</code>          | <code>What are the requirements for importing a pet into Canada?</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
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 4
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `prompts`: task: sentence similarity | query: 
+
+#### 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`: 4
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `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}
+- `parallelism_config`: 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
+- `hub_revision`: None
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: task: sentence similarity | query: 
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0147 | 20    | 0.1138        |
+| 0.0295 | 40    | 0.0682        |
+| 0.0442 | 60    | 0.0099        |
+| 0.0590 | 80    | 0.0212        |
+| 0.0737 | 100   | 0.0447        |
+| 0.0885 | 120   | 0.0047        |
+| 0.1032 | 140   | 0.0057        |
+| 0.1180 | 160   | 0.0025        |
+| 0.1327 | 180   | 0.0036        |
+| 0.1475 | 200   | 0.0062        |
+| 0.1622 | 220   | 0.0285        |
+| 0.1770 | 240   | 0.0069        |
+| 0.1917 | 260   | 0.0008        |
+| 0.2065 | 280   | 0.0104        |
+| 0.2212 | 300   | 0.0019        |
+| 0.2360 | 320   | 0.0576        |
+| 0.2507 | 340   | 0.0088        |
+| 0.2655 | 360   | 0.0046        |
+| 0.2802 | 380   | 0.0014        |
+| 0.2950 | 400   | 0.001         |
+| 0.3097 | 420   | 0.0184        |
+| 0.3245 | 440   | 0.0016        |
+| 0.3392 | 460   | 0.0019        |
+| 0.3540 | 480   | 0.0192        |
+| 0.3687 | 500   | 0.0392        |
+| 0.3835 | 520   | 0.0051        |
+| 0.3982 | 540   | 0.0023        |
+| 0.4130 | 560   | 0.0119        |
+| 0.4277 | 580   | 0.0022        |
+| 0.4425 | 600   | 0.0046        |
+| 0.4572 | 620   | 0.0041        |
+| 0.4720 | 640   | 0.0066        |
+| 0.4867 | 660   | 0.0115        |
+| 0.5015 | 680   | 0.0112        |
+| 0.5162 | 700   | 0.0327        |
+| 0.5310 | 720   | 0.0009        |
+| 0.5457 | 740   | 0.0031        |
+| 0.5605 | 760   | 0.0007        |
+| 0.5752 | 780   | 0.0367        |
+| 0.5900 | 800   | 0.0344        |
+| 0.6047 | 820   | 0.0027        |
+| 0.6195 | 840   | 0.0105        |
+| 0.6342 | 860   | 0.0597        |
+| 0.6490 | 880   | 0.0594        |
+| 0.6637 | 900   | 0.0022        |
+| 0.6785 | 920   | 0.0177        |
+| 0.6932 | 940   | 0.0041        |
+| 0.7080 | 960   | 0.0123        |
+| 0.7227 | 980   | 0.0988        |
+| 0.7375 | 1000  | 0.0248        |
+| 0.7522 | 1020  | 0.0021        |
+| 0.7670 | 1040  | 0.0376        |
+| 0.7817 | 1060  | 0.0216        |
+| 0.7965 | 1080  | 0.0779        |
+| 0.8112 | 1100  | 0.0317        |
+| 0.8260 | 1120  | 0.0233        |
+| 0.8407 | 1140  | 0.0201        |
+| 0.8555 | 1160  | 0.1391        |
+| 0.8702 | 1180  | 0.0846        |
+| 0.8850 | 1200  | 0.0064        |
+| 0.8997 | 1220  | 0.1509        |
+| 0.9145 | 1240  | 0.0196        |
+| 0.9292 | 1260  | 0.0198        |
+| 0.9440 | 1280  | 0.0174        |
+| 0.9587 | 1300  | 0.117         |
+| 0.9735 | 1320  | 0.0741        |
+| 0.9882 | 1340  | 0.3282        |
+| 1.0029 | 1360  | 0.0314        |
+| 1.0177 | 1380  | 0.1522        |
+| 1.0324 | 1400  | 0.0378        |
+| 1.0472 | 1420  | 0.025         |
+| 1.0619 | 1440  | 0.0442        |
+| 1.0767 | 1460  | 0.0314        |
+| 1.0914 | 1480  | 0.0745        |
+| 1.1062 | 1500  | 0.0272        |
+| 1.1209 | 1520  | 0.1248        |
+| 1.1357 | 1540  | 0.299         |
+| 1.1504 | 1560  | 0.0123        |
+| 1.1652 | 1580  | 0.0245        |
+| 1.1799 | 1600  | 0.0153        |
+| 1.1947 | 1620  | 0.0171        |
+| 1.2094 | 1640  | 0.0146        |
+| 1.2242 | 1660  | 0.0313        |
+| 1.2389 | 1680  | 0.0317        |
+| 1.2537 | 1700  | 0.084         |
+| 1.2684 | 1720  | 0.0569        |
+| 1.2832 | 1740  | 0.1958        |
+| 1.2979 | 1760  | 0.09          |
+| 1.3127 | 1780  | 0.0526        |
+| 1.3274 | 1800  | 0.0956        |
+| 1.3422 | 1820  | 0.1601        |
+| 1.3569 | 1840  | 0.156         |
+| 1.3717 | 1860  | 0.0296        |
+| 1.3864 | 1880  | 0.0391        |
+| 1.4012 | 1900  | 0.0816        |
+| 1.4159 | 1920  | 0.1262        |
+| 1.4307 | 1940  | 0.1375        |
+| 1.4454 | 1960  | 0.3373        |
+| 1.4602 | 1980  | 0.094         |
+| 1.4749 | 2000  | 0.0875        |
+| 1.4897 | 2020  | 0.1161        |
+| 1.5044 | 2040  | 0.1739        |
+| 1.5192 | 2060  | 0.0526        |
+| 1.5339 | 2080  | 0.1364        |
+| 1.5487 | 2100  | 0.0508        |
+| 1.5634 | 2120  | 0.0614        |
+| 1.5782 | 2140  | 0.0589        |
+| 1.5929 | 2160  | 0.0593        |
+| 1.6077 | 2180  | 0.0078        |
+| 1.6224 | 2200  | 0.2009        |
+| 1.6372 | 2220  | 0.1356        |
+| 1.6519 | 2240  | 0.1268        |
+| 1.6667 | 2260  | 0.0257        |
+| 1.6814 | 2280  | 0.0679        |
+| 1.6962 | 2300  | 0.0229        |
+| 1.7109 | 2320  | 0.1467        |
+| 1.7257 | 2340  | 0.1239        |
+| 1.7404 | 2360  | 0.0138        |
+| 1.7552 | 2380  | 0.0997        |
+| 1.7699 | 2400  | 0.0197        |
+| 1.7847 | 2420  | 0.0358        |
+| 1.7994 | 2440  | 0.0368        |
+| 1.8142 | 2460  | 0.0755        |
+| 1.8289 | 2480  | 0.1305        |
+| 1.8437 | 2500  | 0.0164        |
+| 1.8584 | 2520  | 0.1273        |
+| 1.8732 | 2540  | 0.0255        |
+| 1.8879 | 2560  | 0.0547        |
+| 1.9027 | 2580  | 0.0494        |
+| 1.9174 | 2600  | 0.1257        |
+| 1.9322 | 2620  | 0.0434        |
+| 1.9469 | 2640  | 0.0358        |
+| 1.9617 | 2660  | 0.1272        |
+| 1.9764 | 2680  | 0.022         |
+| 1.9912 | 2700  | 0.054         |
+| 2.0059 | 2720  | 0.0281        |
+| 2.0206 | 2740  | 0.0229        |
+| 2.0354 | 2760  | 0.0117        |
+| 2.0501 | 2780  | 0.0242        |
+| 2.0649 | 2800  | 0.0819        |
+| 2.0796 | 2820  | 0.0625        |
+| 2.0944 | 2840  | 0.0622        |
+| 2.1091 | 2860  | 0.0316        |
+| 2.1239 | 2880  | 0.2254        |
+| 2.1386 | 2900  | 0.0857        |
+| 2.1534 | 2920  | 0.026         |
+| 2.1681 | 2940  | 0.0023        |
+| 2.1829 | 2960  | 0.0053        |
+| 2.1976 | 2980  | 0.004         |
+| 2.2124 | 3000  | 0.0087        |
+| 2.2271 | 3020  | 0.0068        |
+| 2.2419 | 3040  | 0.0207        |
+| 2.2566 | 3060  | 0.0522        |
+| 2.2714 | 3080  | 0.005         |
+| 2.2861 | 3100  | 0.038         |
+| 2.3009 | 3120  | 0.0059        |
+| 2.3156 | 3140  | 0.035         |
+| 2.3304 | 3160  | 0.0603        |
+| 2.3451 | 3180  | 0.0209        |
+| 2.3599 | 3200  | 0.0103        |
+| 2.3746 | 3220  | 0.0109        |
+| 2.3894 | 3240  | 0.0755        |
+| 2.4041 | 3260  | 0.0021        |
+| 2.4189 | 3280  | 0.1019        |
+| 2.4336 | 3300  | 0.1014        |
+| 2.4484 | 3320  | 0.0198        |
+| 2.4631 | 3340  | 0.0205        |
+| 2.4779 | 3360  | 0.0431        |
+| 2.4926 | 3380  | 0.1268        |
+| 2.5074 | 3400  | 0.0097        |
+| 2.5221 | 3420  | 0.0035        |
+| 2.5369 | 3440  | 0.0292        |
+| 2.5516 | 3460  | 0.0175        |
+| 2.5664 | 3480  | 0.0687        |
+| 2.5811 | 3500  | 0.021         |
+| 2.5959 | 3520  | 0.0438        |
+| 2.6106 | 3540  | 0.0024        |
+| 2.6254 | 3560  | 0.0029        |
+| 2.6401 | 3580  | 0.0267        |
+| 2.6549 | 3600  | 0.0288        |
+| 2.6696 | 3620  | 0.0058        |
+| 2.6844 | 3640  | 0.0634        |
+| 2.6991 | 3660  | 0.0404        |
+| 2.7139 | 3680  | 0.0253        |
+| 2.7286 | 3700  | 0.0127        |
+| 2.7434 | 3720  | 0.0786        |
+| 2.7581 | 3740  | 0.0739        |
+| 2.7729 | 3760  | 0.0348        |
+| 2.7876 | 3780  | 0.0697        |
+| 2.8024 | 3800  | 0.0143        |
+| 2.8171 | 3820  | 0.015         |
+| 2.8319 | 3840  | 0.0139        |
+| 2.8466 | 3860  | 0.023         |
+| 2.8614 | 3880  | 0.0625        |
+| 2.8761 | 3900  | 0.01          |
+| 2.8909 | 3920  | 0.0656        |
+| 2.9056 | 3940  | 0.0435        |
+| 2.9204 | 3960  | 0.0367        |
+| 2.9351 | 3980  | 0.0482        |
+| 2.9499 | 4000  | 0.0557        |
+| 2.9646 | 4020  | 0.1046        |
+| 2.9794 | 4040  | 0.0578        |
+| 2.9941 | 4060  | 0.0793        |
+| 3.0088 | 4080  | 0.0053        |
+| 3.0236 | 4100  | 0.0035        |
+| 3.0383 | 4120  | 0.0095        |
+| 3.0531 | 4140  | 0.001         |
+| 3.0678 | 4160  | 0.0368        |
+| 3.0826 | 4180  | 0.0251        |
+| 3.0973 | 4200  | 0.0084        |
+| 3.1121 | 4220  | 0.0224        |
+| 3.1268 | 4240  | 0.0407        |
+| 3.1416 | 4260  | 0.0611        |
+| 3.1563 | 4280  | 0.0226        |
+| 3.1711 | 4300  | 0.0092        |
+| 3.1858 | 4320  | 0.0052        |
+| 3.2006 | 4340  | 0.0578        |
+| 3.2153 | 4360  | 0.0259        |
+| 3.2301 | 4380  | 0.0002        |
+| 3.2448 | 4400  | 0.0787        |
+| 3.2596 | 4420  | 0.0194        |
+| 3.2743 | 4440  | 0.0002        |
+| 3.2891 | 4460  | 0.0006        |
+| 3.3038 | 4480  | 0.0188        |
+| 3.3186 | 4500  | 0.0722        |
+| 3.3333 | 4520  | 0.0621        |
+| 3.3481 | 4540  | 0.0017        |
+| 3.3628 | 4560  | 0.1242        |
+| 3.3776 | 4580  | 0.0057        |
+| 3.3923 | 4600  | 0.0064        |
+| 3.4071 | 4620  | 0.0016        |
+| 3.4218 | 4640  | 0.0007        |
+| 3.4366 | 4660  | 0.1187        |
+| 3.4513 | 4680  | 0.0529        |
+| 3.4661 | 4700  | 0.0294        |
+| 3.4808 | 4720  | 0.1213        |
+| 3.4956 | 4740  | 0.0221        |
+| 3.5103 | 4760  | 0.0234        |
+| 3.5251 | 4780  | 0.0034        |
+| 3.5398 | 4800  | 0.0107        |
+| 3.5546 | 4820  | 0.012         |
+| 3.5693 | 4840  | 0.0351        |
+| 3.5841 | 4860  | 0.0099        |
+| 3.5988 | 4880  | 0.002         |
+| 3.6136 | 4900  | 0.0024        |
+| 3.6283 | 4920  | 0.0321        |
+| 3.6431 | 4940  | 0.0008        |
+| 3.6578 | 4960  | 0.038         |
+| 3.6726 | 4980  | 0.0944        |
+| 3.6873 | 5000  | 0.0227        |
+| 3.7021 | 5020  | 0.0088        |
+| 3.7168 | 5040  | 0.0573        |
+| 3.7316 | 5060  | 0.2029        |
+| 3.7463 | 5080  | 0.0522        |
+| 3.7611 | 5100  | 0.012         |
+| 3.7758 | 5120  | 0.0044        |
+| 3.7906 | 5140  | 0.0178        |
+| 3.8053 | 5160  | 0.0032        |
+| 3.8201 | 5180  | 0.0375        |
+| 3.8348 | 5200  | 0.0322        |
+| 3.8496 | 5220  | 0.0066        |
+| 3.8643 | 5240  | 0.0108        |
+| 3.8791 | 5260  | 0.0143        |
+| 3.8938 | 5280  | 0.0271        |
+| 3.9086 | 5300  | 0.003         |
+| 3.9233 | 5320  | 0.0183        |
+| 3.9381 | 5340  | 0.0307        |
+| 3.9528 | 5360  | 0.0026        |
+| 3.9676 | 5380  | 0.0031        |
+| 3.9823 | 5400  | 0.0011        |
+| 3.9971 | 5420  | 0.0749        |
+| 4.0118 | 5440  | 0.0192        |
+| 4.0265 | 5460  | 0.037         |
+| 4.0413 | 5480  | 0.0017        |
+| 4.0560 | 5500  | 0.0013        |
+| 4.0708 | 5520  | 0.0246        |
+| 4.0855 | 5540  | 0.0007        |
+| 4.1003 | 5560  | 0.045         |
+| 4.1150 | 5580  | 0.038         |
+| 4.1298 | 5600  | 0.0179        |
+| 4.1445 | 5620  | 0.021         |
+| 4.1593 | 5640  | 0.0012        |
+| 4.1740 | 5660  | 0.0001        |
+| 4.1888 | 5680  | 0.0004        |
+| 4.2035 | 5700  | 0.0001        |
+| 4.2183 | 5720  | 0.0021        |
+| 4.2330 | 5740  | 0.0279        |
+| 4.2478 | 5760  | 0.0044        |
+| 4.2625 | 5780  | 0.0063        |
+| 4.2773 | 5800  | 0.0046        |
+| 4.2920 | 5820  | 0.0692        |
+| 4.3068 | 5840  | 0.0007        |
+| 4.3215 | 5860  | 0.0053        |
+| 4.3363 | 5880  | 0.0288        |
+| 4.3510 | 5900  | 0.0197        |
+| 4.3658 | 5920  | 0.0007        |
+| 4.3805 | 5940  | 0.002         |
+| 4.3953 | 5960  | 0.0059        |
+| 4.4100 | 5980  | 0.0258        |
+| 4.4248 | 6000  | 0.001         |
+| 4.4395 | 6020  | 0.0017        |
+| 4.4543 | 6040  | 0.0024        |
+| 4.4690 | 6060  | 0.0748        |
+| 4.4838 | 6080  | 0.002         |
+| 4.4985 | 6100  | 0.0498        |
+| 4.5133 | 6120  | 0.0016        |
+| 4.5280 | 6140  | 0.0018        |
+| 4.5428 | 6160  | 0.0022        |
+| 4.5575 | 6180  | 0.0012        |
+| 4.5723 | 6200  | 0.009         |
+| 4.5870 | 6220  | 0.0659        |
+| 4.6018 | 6240  | 0.0121        |
+| 4.6165 | 6260  | 0.0294        |
+| 4.6313 | 6280  | 0.0002        |
+| 4.6460 | 6300  | 0.0184        |
+| 4.6608 | 6320  | 0.0158        |
+| 4.6755 | 6340  | 0.0104        |
+| 4.6903 | 6360  | 0.0498        |
+| 4.7050 | 6380  | 0.0061        |
+| 4.7198 | 6400  | 0.0305        |
+| 4.7345 | 6420  | 0.0427        |
+| 4.7493 | 6440  | 0.0004        |
+| 4.7640 | 6460  | 0.0009        |
+| 4.7788 | 6480  | 0.0001        |
+| 4.7935 | 6500  | 0.0261        |
+| 4.8083 | 6520  | 0.0019        |
+| 4.8230 | 6540  | 0.0024        |
+| 4.8378 | 6560  | 0.0228        |
+| 4.8525 | 6580  | 0.0002        |
+| 4.8673 | 6600  | 0.002         |
+| 4.8820 | 6620  | 0.0005        |
+| 4.8968 | 6640  | 0.0082        |
+| 4.9115 | 6660  | 0.0119        |
+| 4.9263 | 6680  | 0.0175        |
+| 4.9410 | 6700  | 0.0011        |
+| 4.9558 | 6720  | 0.0021        |
+| 4.9705 | 6740  | 0.0106        |
+| 4.9853 | 6760  | 0.018         |
+| 5.0    | 6780  | 0.019         |
+| 5.0147 | 6800  | 0.0629        |
+| 5.0295 | 6820  | 0.0076        |
+| 5.0442 | 6840  | 0.0004        |
+| 5.0590 | 6860  | 0.0014        |
+| 5.0737 | 6880  | 0.0012        |
+| 5.0885 | 6900  | 0.0021        |
+| 5.1032 | 6920  | 0.0032        |
+| 5.1180 | 6940  | 0.0275        |
+| 5.1327 | 6960  | 0.019         |
+| 5.1475 | 6980  | 0.0006        |
+| 5.1622 | 7000  | 0.0006        |
+| 5.1770 | 7020  | 0.0049        |
+| 5.1917 | 7040  | 0.0359        |
+| 5.2065 | 7060  | 0.0028        |
+| 5.2212 | 7080  | 0.0012        |
+| 5.2360 | 7100  | 0.0138        |
+| 5.2507 | 7120  | 0.0042        |
+| 5.2655 | 7140  | 0.0003        |
+| 5.2802 | 7160  | 0.0056        |
+| 5.2950 | 7180  | 0.0329        |
+| 5.3097 | 7200  | 0.0016        |
+| 5.3245 | 7220  | 0.0092        |
+| 5.3392 | 7240  | 0.0002        |
+| 5.3540 | 7260  | 0.0211        |
+| 5.3687 | 7280  | 0.019         |
+| 5.3835 | 7300  | 0.0012        |
+| 5.3982 | 7320  | 0.0002        |
+| 5.4130 | 7340  | 0.0002        |
+| 5.4277 | 7360  | 0.0143        |
+| 5.4425 | 7380  | 0.0004        |
+| 5.4572 | 7400  | 0.0004        |
+| 5.4720 | 7420  | 0.0068        |
+| 5.4867 | 7440  | 0.0201        |
+| 5.5015 | 7460  | 0.0003        |
+| 5.5162 | 7480  | 0.0042        |
+| 5.5310 | 7500  | 0.0007        |
+| 5.5457 | 7520  | 0.0664        |
+| 5.5605 | 7540  | 0.0014        |
+| 5.5752 | 7560  | 0.0175        |
+| 5.5900 | 7580  | 0.0362        |
+| 5.6047 | 7600  | 0.0225        |
+| 5.6195 | 7620  | 0.0003        |
+| 5.6342 | 7640  | 0.0025        |
+| 5.6490 | 7660  | 0.0128        |
+| 5.6637 | 7680  | 0.0013        |
+| 5.6785 | 7700  | 0.0042        |
+| 5.6932 | 7720  | 0.0012        |
+| 5.7080 | 7740  | 0.0017        |
+| 5.7227 | 7760  | 0.0039        |
+| 5.7375 | 7780  | 0.0013        |
+| 5.7522 | 7800  | 0.0008        |
+| 5.7670 | 7820  | 0.006         |
+| 5.7817 | 7840  | 0.0177        |
+| 5.7965 | 7860  | 0.0189        |
+| 5.8112 | 7880  | 0.0015        |
+| 5.8260 | 7900  | 0.0003        |
+| 5.8407 | 7920  | 0.001         |
+| 5.8555 | 7940  | 0.0269        |
+| 5.8702 | 7960  | 0.0006        |
+| 5.8850 | 7980  | 0.0176        |
+| 5.8997 | 8000  | 0.0048        |
+| 5.9145 | 8020  | 0.0031        |
+| 5.9292 | 8040  | 0.0056        |
+| 5.9440 | 8060  | 0.0015        |
+| 5.9587 | 8080  | 0.0102        |
+| 5.9735 | 8100  | 0.0047        |
+| 5.9882 | 8120  | 0.0339        |
+| 6.0029 | 8140  | 0.0027        |
+| 6.0177 | 8160  | 0.0008        |
+| 6.0324 | 8180  | 0.0014        |
+| 6.0472 | 8200  | 0.0001        |
+| 6.0619 | 8220  | 0.0183        |
+| 6.0767 | 8240  | 0.0142        |
+| 6.0914 | 8260  | 0.0004        |
+| 6.1062 | 8280  | 0.0392        |
+| 6.1209 | 8300  | 0.0016        |
+| 6.1357 | 8320  | 0.0025        |
+| 6.1504 | 8340  | 0.0017        |
+| 6.1652 | 8360  | 0.018         |
+| 6.1799 | 8380  | 0.0031        |
+| 6.1947 | 8400  | 0.0021        |
+| 6.2094 | 8420  | 0.0244        |
+| 6.2242 | 8440  | 0.0263        |
+| 6.2389 | 8460  | 0.0183        |
+| 6.2537 | 8480  | 0.0367        |
+| 6.2684 | 8500  | 0.0009        |
+| 6.2832 | 8520  | 0.0           |
+| 6.2979 | 8540  | 0.0001        |
+| 6.3127 | 8560  | 0.0011        |
+| 6.3274 | 8580  | 0.0007        |
+| 6.3422 | 8600  | 0.0004        |
+| 6.3569 | 8620  | 0.0044        |
+| 6.3717 | 8640  | 0.0174        |
+| 6.3864 | 8660  | 0.0002        |
+| 6.4012 | 8680  | 0.0176        |
+| 6.4159 | 8700  | 0.0341        |
+| 6.4307 | 8720  | 0.0015        |
+| 6.4454 | 8740  | 0.0002        |
+| 6.4602 | 8760  | 0.0043        |
+| 6.4749 | 8780  | 0.0036        |
+| 6.4897 | 8800  | 0.0001        |
+| 6.5044 | 8820  | 0.0004        |
+| 6.5192 | 8840  | 0.0474        |
+| 6.5339 | 8860  | 0.0001        |
+| 6.5487 | 8880  | 0.0003        |
+| 6.5634 | 8900  | 0.0021        |
+| 6.5782 | 8920  | 0.0014        |
+| 6.5929 | 8940  | 0.0004        |
+| 6.6077 | 8960  | 0.0176        |
+| 6.6224 | 8980  | 0.0001        |
+| 6.6372 | 9000  | 0.0009        |
+| 6.6519 | 9020  | 0.0015        |
+| 6.6667 | 9040  | 0.0003        |
+| 6.6814 | 9060  | 0.0001        |
+| 6.6962 | 9080  | 0.0016        |
+| 6.7109 | 9100  | 0.0182        |
+| 6.7257 | 9120  | 0.0002        |
+| 6.7404 | 9140  | 0.0009        |
+| 6.7552 | 9160  | 0.0018        |
+| 6.7699 | 9180  | 0.0182        |
+| 6.7847 | 9200  | 0.0           |
+| 6.7994 | 9220  | 0.0206        |
+| 6.8142 | 9240  | 0.0001        |
+| 6.8289 | 9260  | 0.0002        |
+| 6.8437 | 9280  | 0.0037        |
+| 6.8584 | 9300  | 0.0238        |
+| 6.8732 | 9320  | 0.0002        |
+| 6.8879 | 9340  | 0.0           |
+| 6.9027 | 9360  | 0.0002        |
+| 6.9174 | 9380  | 0.019         |
+| 6.9322 | 9400  | 0.0059        |
+| 6.9469 | 9420  | 0.0002        |
+| 6.9617 | 9440  | 0.0001        |
+| 6.9764 | 9460  | 0.0004        |
+| 6.9912 | 9480  | 0.0023        |
+| 7.0059 | 9500  | 0.0006        |
+| 7.0206 | 9520  | 0.0019        |
+| 7.0354 | 9540  | 0.0176        |
+| 7.0501 | 9560  | 0.0026        |
+| 7.0649 | 9580  | 0.0014        |
+| 7.0796 | 9600  | 0.0003        |
+| 7.0944 | 9620  | 0.0001        |
+| 7.1091 | 9640  | 0.0002        |
+| 7.1239 | 9660  | 0.0362        |
+| 7.1386 | 9680  | 0.001         |
+| 7.1534 | 9700  | 0.0001        |
+| 7.1681 | 9720  | 0.0002        |
+| 7.1829 | 9740  | 0.0029        |
+| 7.1976 | 9760  | 0.0002        |
+| 7.2124 | 9780  | 0.0003        |
+| 7.2271 | 9800  | 0.0027        |
+| 7.2419 | 9820  | 0.0001        |
+| 7.2566 | 9840  | 0.0001        |
+| 7.2714 | 9860  | 0.0002        |
+| 7.2861 | 9880  | 0.0124        |
+| 7.3009 | 9900  | 0.0361        |
+| 7.3156 | 9920  | 0.0039        |
+| 7.3304 | 9940  | 0.0           |
+| 7.3451 | 9960  | 0.0           |
+| 7.3599 | 9980  | 0.0008        |
+| 7.3746 | 10000 | 0.0002        |
+| 7.3894 | 10020 | 0.0003        |
+| 7.4041 | 10040 | 0.0001        |
+| 7.4189 | 10060 | 0.0174        |
+| 7.4336 | 10080 | 0.0015        |
+| 7.4484 | 10100 | 0.0152        |
+| 7.4631 | 10120 | 0.0351        |
+| 7.4779 | 10140 | 0.0007        |
+| 7.4926 | 10160 | 0.0005        |
+| 7.5074 | 10180 | 0.0005        |
+| 7.5221 | 10200 | 0.0001        |
+| 7.5369 | 10220 | 0.0002        |
+| 7.5516 | 10240 | 0.0001        |
+| 7.5664 | 10260 | 0.001         |
+| 7.5811 | 10280 | 0.0057        |
+| 7.5959 | 10300 | 0.0012        |
+| 7.6106 | 10320 | 0.0001        |
+| 7.6254 | 10340 | 0.0005        |
+| 7.6401 | 10360 | 0.0016        |
+| 7.6549 | 10380 | 0.0072        |
+| 7.6696 | 10400 | 0.0007        |
+| 7.6844 | 10420 | 0.0001        |
+| 7.6991 | 10440 | 0.0002        |
+| 7.7139 | 10460 | 0.0036        |
+| 7.7286 | 10480 | 0.0001        |
+| 7.7434 | 10500 | 0.0002        |
+| 7.7581 | 10520 | 0.0001        |
+| 7.7729 | 10540 | 0.0001        |
+| 7.7876 | 10560 | 0.0007        |
+| 7.8024 | 10580 | 0.0002        |
+| 7.8171 | 10600 | 0.0001        |
+| 7.8319 | 10620 | 0.018         |
+| 7.8466 | 10640 | 0.0882        |
+| 7.8614 | 10660 | 0.0006        |
+| 7.8761 | 10680 | 0.0001        |
+| 7.8909 | 10700 | 0.0001        |
+| 7.9056 | 10720 | 0.0001        |
+| 7.9204 | 10740 | 0.0176        |
+| 7.9351 | 10760 | 0.0002        |
+| 7.9499 | 10780 | 0.0231        |
+| 7.9646 | 10800 | 0.0002        |
+| 7.9794 | 10820 | 0.0002        |
+| 7.9941 | 10840 | 0.0           |
+| 8.0088 | 10860 | 0.0001        |
+| 8.0236 | 10880 | 0.0001        |
+| 8.0383 | 10900 | 0.0003        |
+| 8.0531 | 10920 | 0.0172        |
+| 8.0678 | 10940 | 0.0002        |
+| 8.0826 | 10960 | 0.018         |
+| 8.0973 | 10980 | 0.0174        |
+| 8.1121 | 11000 | 0.0001        |
+| 8.1268 | 11020 | 0.0174        |
+| 8.1416 | 11040 | 0.0           |
+| 8.1563 | 11060 | 0.0039        |
+| 8.1711 | 11080 | 0.0001        |
+| 8.1858 | 11100 | 0.0           |
+| 8.2006 | 11120 | 0.002         |
+| 8.2153 | 11140 | 0.0176        |
+| 8.2301 | 11160 | 0.0022        |
+| 8.2448 | 11180 | 0.0001        |
+| 8.2596 | 11200 | 0.0           |
+| 8.2743 | 11220 | 0.0027        |
+| 8.2891 | 11240 | 0.0198        |
+| 8.3038 | 11260 | 0.0           |
+| 8.3186 | 11280 | 0.0003        |
+| 8.3333 | 11300 | 0.0223        |
+| 8.3481 | 11320 | 0.0092        |
+| 8.3628 | 11340 | 0.0001        |
+| 8.3776 | 11360 | 0.0009        |
+| 8.3923 | 11380 | 0.0014        |
+| 8.4071 | 11400 | 0.0006        |
+| 8.4218 | 11420 | 0.0006        |
+| 8.4366 | 11440 | 0.0006        |
+| 8.4513 | 11460 | 0.0005        |
+| 8.4661 | 11480 | 0.0192        |
+| 8.4808 | 11500 | 0.0347        |
+| 8.4956 | 11520 | 0.0009        |
+| 8.5103 | 11540 | 0.0002        |
+| 8.5251 | 11560 | 0.0           |
+| 8.5398 | 11580 | 0.0           |
+| 8.5546 | 11600 | 0.0002        |
+| 8.5693 | 11620 | 0.0174        |
+| 8.5841 | 11640 | 0.0001        |
+| 8.5988 | 11660 | 0.0171        |
+| 8.6136 | 11680 | 0.0001        |
+| 8.6283 | 11700 | 0.0001        |
+| 8.6431 | 11720 | 0.0428        |
+| 8.6578 | 11740 | 0.0003        |
+| 8.6726 | 11760 | 0.0           |
+| 8.6873 | 11780 | 0.0001        |
+| 8.7021 | 11800 | 0.0176        |
+| 8.7168 | 11820 | 0.0358        |
+| 8.7316 | 11840 | 0.0002        |
+| 8.7463 | 11860 | 0.0002        |
+| 8.7611 | 11880 | 0.0001        |
+| 8.7758 | 11900 | 0.0002        |
+| 8.7906 | 11920 | 0.0015        |
+| 8.8053 | 11940 | 0.0001        |
+| 8.8201 | 11960 | 0.0001        |
+| 8.8348 | 11980 | 0.0112        |
+| 8.8496 | 12000 | 0.0033        |
+| 8.8643 | 12020 | 0.0001        |
+| 8.8791 | 12040 | 0.001         |
+| 8.8938 | 12060 | 0.0174        |
+| 8.9086 | 12080 | 0.0001        |
+| 8.9233 | 12100 | 0.0002        |
+| 8.9381 | 12120 | 0.0001        |
+| 8.9528 | 12140 | 0.0001        |
+| 8.9676 | 12160 | 0.0222        |
+| 8.9823 | 12180 | 0.0003        |
+| 8.9971 | 12200 | 0.0001        |
+| 9.0118 | 12220 | 0.0           |
+| 9.0265 | 12240 | 0.0001        |
+| 9.0413 | 12260 | 0.0182        |
+| 9.0560 | 12280 | 0.0174        |
+| 9.0708 | 12300 | 0.0           |
+| 9.0855 | 12320 | 0.0           |
+| 9.1003 | 12340 | 0.0023        |
+| 9.1150 | 12360 | 0.0001        |
+| 9.1298 | 12380 | 0.0248        |
+| 9.1445 | 12400 | 0.0           |
+| 9.1593 | 12420 | 0.0           |
+| 9.1740 | 12440 | 0.0           |
+| 9.1888 | 12460 | 0.0001        |
+| 9.2035 | 12480 | 0.0087        |
+| 9.2183 | 12500 | 0.0           |
+| 9.2330 | 12520 | 0.0003        |
+| 9.2478 | 12540 | 0.0174        |
+| 9.2625 | 12560 | 0.0           |
+| 9.2773 | 12580 | 0.0006        |
+| 9.2920 | 12600 | 0.0001        |
+| 9.3068 | 12620 | 0.0053        |
+| 9.3215 | 12640 | 0.0           |
+| 9.3363 | 12660 | 0.0174        |
+| 9.3510 | 12680 | 0.0001        |
+| 9.3658 | 12700 | 0.0002        |
+| 9.3805 | 12720 | 0.0001        |
+| 9.3953 | 12740 | 0.0001        |
+| 9.4100 | 12760 | 0.0001        |
+| 9.4248 | 12780 | 0.0002        |
+| 9.4395 | 12800 | 0.0002        |
+| 9.4543 | 12820 | 0.0023        |
+| 9.4690 | 12840 | 0.0           |
+| 9.4838 | 12860 | 0.0018        |
+| 9.4985 | 12880 | 0.0028        |
+| 9.5133 | 12900 | 0.0174        |
+| 9.5280 | 12920 | 0.0001        |
+| 9.5428 | 12940 | 0.0001        |
+| 9.5575 | 12960 | 0.0174        |
+| 9.5723 | 12980 | 0.0003        |
+| 9.5870 | 13000 | 0.0           |
+| 9.6018 | 13020 | 0.0174        |
+| 9.6165 | 13040 | 0.0001        |
+| 9.6313 | 13060 | 0.0           |
+| 9.6460 | 13080 | 0.0001        |
+| 9.6608 | 13100 | 0.0174        |
+| 9.6755 | 13120 | 0.0173        |
+| 9.6903 | 13140 | 0.0           |
+| 9.7050 | 13160 | 0.0005        |
+| 9.7198 | 13180 | 0.0001        |
+| 9.7345 | 13200 | 0.0002        |
+| 9.7493 | 13220 | 0.0           |
+| 9.7640 | 13240 | 0.0001        |
+| 9.7788 | 13260 | 0.0           |
+| 9.7935 | 13280 | 0.0026        |
+| 9.8083 | 13300 | 0.0003        |
+| 9.8230 | 13320 | 0.0001        |
+| 9.8378 | 13340 | 0.0174        |
+| 9.8525 | 13360 | 0.0099        |
+| 9.8673 | 13380 | 0.0002        |
+| 9.8820 | 13400 | 0.0           |
+| 9.8968 | 13420 | 0.0032        |
+| 9.9115 | 13440 | 0.0177        |
+| 9.9263 | 13460 | 0.0175        |
+| 9.9410 | 13480 | 0.0176        |
+| 9.9558 | 13500 | 0.0001        |
+| 9.9705 | 13520 | 0.0           |
+| 9.9853 | 13540 | 0.0011        |
+| 10.0   | 13560 | 0.0174        |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 5.1.1
+- Transformers: 4.57.0.dev0
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.8.1
+- Datasets: 3.6.0
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+{
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+}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3TextModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 2048,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 3,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 257,
+  "transformers_version": "4.57.0.dev0",
+  "use_bidirectional_attention": true,
+  "use_cache": true,
+  "vocab_size": 262144
+}

config_sentence_transformers.json

@@ -0,0 +1,26 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.57.0.dev0",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "task: search result | query: ",
+    "document": "title: none | text: ",
+    "BitextMining": "task: search result | query: ",
+    "Clustering": "task: clustering | query: ",
+    "Classification": "task: classification | query: ",
+    "InstructionRetrieval": "task: code retrieval | query: ",
+    "MultilabelClassification": "task: classification | query: ",
+    "PairClassification": "task: sentence similarity | query: ",
+    "Reranking": "task: search result | query: ",
+    "Retrieval": "task: search result | query: ",
+    "Retrieval-query": "task: search result | query: ",
+    "Retrieval-document": "title: none | text: ",
+    "STS": "task: sentence similarity | query: ",
+    "Summarization": "task: summarization | query: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+oid sha256:4b54d41b5529c7c852d8958e354acd80db0818ed0772c58a4808135bcdcd0027
+size 1211486072

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 4,
+    "name": "4",
+    "path": "4_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 2048,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+oid sha256:216e2a79606fe879c9f17c529c71cd241338407fd5646b595ffd3c4b9ea1d503
+size 33385262

tokenizer.model

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