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1_Pooling/config.json

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+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:12582766
+- loss:CoSENTLoss
+base_model: KhaledReda/all-MiniLM-L6-v23-pair_score
+widget:
+- source_sentence: police pyramid north
+  sentences:
+  - silver sparkle category fashion footwear shoe heels tags silver heels women heels
+    thin straps heels rhinestones heels heels sparkle heels keywords heels sparkle
+    heels attrs gender women brand unidentical generic name heels size 37 features
+    thin straps sparkle material rhinestones color silver description thin straps
+    with rhinestones heels. height 7 cm.
+  - colorful printed long cardigan category fashion casual wear outerwear outerwear
+    tags cardigan colorful cardigan long cardigan printed cardigan keywords cardigan
+    colorful cardigan long cardigan printed cardigan attrs gender women brand u modest
+    generic name cardigan features long types of fashion styles casual everyday wear
+    color colorful pattern printed
+  - courtly black category fashion footwear shoe flats tags flat shoe black shoe black
+    flat shoe courtly keywords courtly attrs color black
+- source_sentence: pre play fanny pack
+  sentences:
+  - streptoquin - antidiarrheal antispasmodic - 20 tablets category pharmacies medicine
+    stomach medicine stomach medicine tags antidiarrheal streptoquin antispasmodic
+    streptoquin streptoquin streptoquin tablets keywords antidiarrheal streptoquin
+    antispasmodic streptoquin streptoquin streptoquin tablets attrs pharmacies form
+    tablets
+  - sante - gold brownie cherry granola - 300 gr category groceries supermarkets grain
+    granola tags sante palm oil free granola wheat free granola brownie granola cherry
+    granola gold granola granola sante granola keywords brownie granola cherry granola
+    gold granola granola sante granola description palm oil free no added wheat
+  - kataflam henedy edition category kids toys and games game card game tags women
+    cards game men cards game unisex cards game card games henedy card games kataflam
+    kataflam card games kataflam henedy card games keywords card games henedy card
+    games kataflam kataflam card games kataflam henedy card games
+- source_sentence: yellow
+  sentences:
+  - koi category fashion accessories neckwear and scarf scarf tags youkai scarf fabrics
+    scarf polyfibre fabric scarf satin scarf chiffon scarf lightweight scarf semi
+    transparent scarf soft scarf blue scarf white scarf headscarf koi scarf scarf
+    keywords koi scarf scarf attrs gender women brand pavo generic name scarf product
+    name koi measurements 200 70 cm features lightweight material polyfibre color
+    blue and white pattern abstract description japanese heritage harnessed to be
+    a piece of art for youkoi comes in two fabrics chiffon and silk to suit your look
+    material polyfibre fabric finish satin and chiffon characteristic light weight
+    semi transparent shiny smooth very soft size kindly note that the scarf may vary
+    by a couple of centimeters 200 x 70 color blue white style textured abstract border
+    care instructions put a cloth on top while ironing on low setting wash under running
+    water not in a bucket or full sink. do not soak.hand wash with care exchange policy
+    exchange is only offered within 48 hours of receiving a wrong scarf other than
+    the one you ordererd or one that has a defect. plea
+  - maalox plus lemon 180 ml susp category pharmacies medicine stomach medicine stomach
+    medicine tags maalox plus maalox plus lemon keywords maalox plus maalox plus lemon
+    attrs units 180 millilitre
+  - white in green leaves category fashion accessories neckwear and scarf scarf tags
+    crepe scarf chiffon scarf white scarf leaves keywords leaves attrs gender women
+    brand souple generic name scarf measurements 200 75 cm material crepe chiffon
+    color white green pattern leaves description imported crepe chiffon. size 200
+    x 75. colors included white different shades of green.
+- source_sentence: ankh scarab royal ring
+  sentences:
+  - masterpiece ethereal ring mother of pearl category fashion jewelry ring ring tags
+    silver ring men ring ethereal ring masterpiece ring mother pearl ring ring keywords
+    ethereal ring masterpiece ring mother pearl ring ring attrs gender men brand nora
+    el batran jewellery generic name ring product name ethereal features masterpiece
+    material silver mother of pearl color silver
+  - omegapress 0.1 mg 30/tab new category health and nutrition dietary supplements
+    omega omega tags omegapress keywords omegapress attrs pharmacies form tab units
+    0.0001 gram
+  - avene physiolift smooth.night balm 30 m category beauty skincare anti-aging anti-aging
+    tags avene night balm avene physiolift avene smoothing night balm physiolift smoothing
+    night balm smoothing night balm keywords avene night balm avene physiolift avene
+    smoothing night balm physiolift smoothing night balm smoothing night balm attrs
+    units 30 m
+- source_sentence: laces boot
+  sentences:
+  - baked savory tart crust 3.5 cm - 12 pieces only cairo giza category groceries
+    specialty foods bakery pastry tags savory tart shells mini quiches shells canapés
+    shells pre made tart shells pre cooked tart shells ready to bake baked tart crust
+    crust tart savory tart crust tart keywords baked tart crust crust tart savory
+    tart crust tart attrs units 12 pieces size numeric 3.5 cm description 12 pieces
+    in each pack. each shell has a diameter of 3.5 cm. also known as savory tart shells.
+    suitable for savory creations such as mini quiches and canapés. save time with
+    these pre made tart shells. just prep your favorite filling and it s ready to
+    be served fill them with cheese smoked salmon eggs vegetables cooked beef or chicken.
+    they re not frozen. tart crusts are pre cooked and stored at room temperature.
+    perishable. available for delivery only to cairo and giza residents.
+  - raw african coffee soap category beauty skincare face soap face soap tags shea
+    butter soap coconut oil soap antioxidant soap firming skin soap dark spots soap
+    coffee soap raw african raw african soap soap ahwa soap kahwa soap kahwah soap
+    qahwa soap raw african ahwa soap raw african kahwa soap raw african kahwah soap
+    raw african qahwa soap keywords coffee soap raw african raw african soap soap
+    ahwa soap kahwa soap kahwah soap qahwa soap raw african ahwa soap raw african
+    kahwa soap raw african kahwah soap raw african qahwa soap description our coffee-based
+    soap bar gives you a boosting and energizing sensation this soap is rich in antioxidants
+    and nutrients that fight age signs firms and tighten the skin and gives you a
+    youthful look. it helps in reducing dark spots and acne scars. for all skin types.
+    this product is free of harsh chemicals like parabens sulphates or mineral oils.
+    we never test our products on animals and we don t deal with suppliers who test
+    their products on animals. ingredients shea butter coconut oil olive oil coffee.
+  - nursing covers mustard flowers category kids baby care breastfeeding aid breastfeeding
+    aid tags breathable nursing cover full coverage nursing cover foldable nursing
+    cover pouch nursing cover colorful nursing cover flowers nursing covers mustard
+    nursing covers nursing covers keywords flowers nursing covers mustard nursing
+    covers nursing covers description breastfeeding is one of the most special yet
+    challenging things in motherhood we just wanted to add some more colors to this
+    special moment with all its colors product details soft light breathable fabric
+    machine washable full coverage comes with its pouch foldable in seconds
+datasets:
+- KhaledReda/pairs_with_scores_v120_tag_true_positives_and_false_negatives_description
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v24-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [KhaledReda/all-MiniLM-L6-v23-pair_score](https://huggingface.co/KhaledReda/all-MiniLM-L6-v23-pair_score) on the [pairs_with_scores_v120_tag_true_positives_and_false_negatives_description](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v120_tag_true_positives_and_false_negatives_description) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [KhaledReda/all-MiniLM-L6-v23-pair_score](https://huggingface.co/KhaledReda/all-MiniLM-L6-v23-pair_score) <!-- at revision ecc0fc98a44e832815c4ccf46162422ded24993e -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [pairs_with_scores_v120_tag_true_positives_and_false_negatives_description](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v120_tag_true_positives_and_false_negatives_description)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'laces boot',
+    'nursing covers mustard flowers category kids baby care breastfeeding aid breastfeeding aid tags breathable nursing cover full coverage nursing cover foldable nursing cover pouch nursing cover colorful nursing cover flowers nursing covers mustard nursing covers nursing covers keywords flowers nursing covers mustard nursing covers nursing covers description breastfeeding is one of the most special yet challenging things in motherhood we just wanted to add some more colors to this special moment with all its colors product details soft light breathable fabric machine washable full coverage comes with its pouch foldable in seconds',
+    'raw african coffee soap category beauty skincare face soap face soap tags shea butter soap coconut oil soap antioxidant soap firming skin soap dark spots soap coffee soap raw african raw african soap soap ahwa soap kahwa soap kahwah soap qahwa soap raw african ahwa soap raw african kahwa soap raw african kahwah soap raw african qahwa soap keywords coffee soap raw african raw african soap soap ahwa soap kahwa soap kahwah soap qahwa soap raw african ahwa soap raw african kahwa soap raw african kahwah soap raw african qahwa soap description our coffee-based soap bar gives you a boosting and energizing sensation this soap is rich in antioxidants and nutrients that fight age signs firms and tighten the skin and gives you a youthful look. it helps in reducing dark spots and acne scars. for all skin types. this product is free of harsh chemicals like parabens sulphates or mineral oils. we never test our products on animals and we don t deal with suppliers who test their products on animals. ingredients shea butter coconut oil olive oil coffee.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000, -0.0817, -0.0683],
+#         [-0.0817,  1.0000, -0.0380],
+#         [-0.0683, -0.0380,  1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### pairs_with_scores_v120_tag_true_positives_and_false_negatives_description
+
+* Dataset: [pairs_with_scores_v120_tag_true_positives_and_false_negatives_description](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v120_tag_true_positives_and_false_negatives_description) at [25785dc](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v120_tag_true_positives_and_false_negatives_description/tree/25785dc72f76c6af9685abb8377e1ed5ed04e9aa)
+* Size: 12,582,766 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                            | score                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                               | float                                                         |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.59 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 104.43 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.1</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                             | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | score            |
+  |:--------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>bergamot eau de toilette</code> | <code>knitted crop top in beige category fashion casual wear top top tags women top beige top comfort top breathable top fabric top stretch top knitted sets crop top top keywords crop top top attrs gender women brand psych generic name top size s features high-waisted soft breathable cut cropped material knitted color beige occasion beach description elevate your style with our chic knitted set featuring a matching pair of knitted pants and a knitted crop top. designed for comfort and effortless elegance this set is perfect for any occasion. the knitted crop top offers a flattering fit with a touch of stretch while the high-waisted knitted pants provide a sleek silhouette and ultimate comfort. made from soft breathable fabric this set is perfect for both enjoying a day at the beach and stepping out in style. versatile and stylish this knitted set is a must-have addition to your wardrobe. mix and match with your favorite accessories for a look that s uniquely you. model is 177 cm wearing size...</code> | <code>0.0</code> |
+  | <code>wide leg pants</code>           | <code>titania solingen no/1063 category beauty cosmetics make-up tool tweezers tags solingen tweezers titania titania tweezers tweezers keywords solingen tweezers titania titania tweezers tweezers</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>0.0</code> |
+  | <code>women pumps</code>              | <code>neurimax 30/cap 2 ex.new category health and nutrition dietary supplements joint supplement joint supplement tags neurimax neurimax supplement keywords neurimax neurimax supplement attrs pharmacies form cap</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### pairs_with_scores_v120_tag_true_positives_and_false_negatives_description
+
+* Dataset: [pairs_with_scores_v120_tag_true_positives_and_false_negatives_description](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v120_tag_true_positives_and_false_negatives_description) at [25785dc](https://huggingface.co/datasets/KhaledReda/pairs_with_scores_v120_tag_true_positives_and_false_negatives_description/tree/25785dc72f76c6af9685abb8377e1ed5ed04e9aa)
+* Size: 63,230 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                            | score                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                               | float                                                         |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.51 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 104.12 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.1</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                      | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | score            |
+  |:-------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>always maxi long</code>  | <code>american - buffalo chicken hot category restaurants pizza deli pizza tags cheesy american pizza thick pizza mozzarella pizza cheese pizza hot chicken pizza american pizza buffalo chicken pizza chicken pizza pizza keywords american pizza buffalo chicken pizza chicken pizza pizza description a thick pizza with a generous amount of cheese mozzarella tomato sauce chicken buffalo sauce</code>                                                                                                                                                                                                 | <code>0.0</code> |
+  | <code>sushi</code>             | <code>lemon fluffy set category fashion casual wear outfit outfit tags linen outfit summer outfit women outfit blouse skirt fluffy outfit lemon outfit outfit outfit set keywords fluffy outfit lemon outfit outfit outfit set attrs gender women brand dovera generic name outfit size one size features fluffy outfit style skirt top material linen color lemon season summer description modest comfyand summery set with fluffy skirt and top comes in 3 colors apple green - brown - blue one size. outside materials linen. blouse length 55 cm width 65 cm shoulder 25 cm skirt length 100 cm</code> | <code>0.0</code> |
+  | <code>eyefree lid wipes</code> | <code>disposable - 5 ml syringe - latex free - 1 pcs category pharmacies first aid and medical equipment medical accessory medical accessory tags disposable syringe syringe keywords disposable syringe syringe attrs units 1 pcs 5 millilitre</code>                                                                                                                                                                                                                                                                                                                                                       | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 1
+- `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`: True
+- `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
+- `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`: None
+- `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 |
+|:------:|:-----:|:-------------:|
+| 0.0010 | 100   | 5.7173        |
+| 0.0020 | 200   | 5.7311        |
+| 0.0031 | 300   | 5.4355        |
+| 0.0041 | 400   | 5.4133        |
+| 0.0051 | 500   | 5.1707        |
+| 0.0061 | 600   | 5.1954        |
+| 0.0071 | 700   | 4.9123        |
+| 0.0081 | 800   | 4.852         |
+| 0.0092 | 900   | 4.8442        |
+| 0.0102 | 1000  | 4.4788        |
+| 0.0112 | 1100  | 4.5735        |
+| 0.0122 | 1200  | 4.3171        |
+| 0.0132 | 1300  | 4.3714        |
+| 0.0142 | 1400  | 4.2536        |
+| 0.0153 | 1500  | 4.1945        |
+| 0.0163 | 1600  | 4.0586        |
+| 0.0173 | 1700  | 3.8934        |
+| 0.0183 | 1800  | 3.9997        |
+| 0.0193 | 1900  | 3.6822        |
+| 0.0203 | 2000  | 3.6699        |
+| 0.0214 | 2100  | 3.7657        |
+| 0.0224 | 2200  | 3.6693        |
+| 0.0234 | 2300  | 3.52          |
+| 0.0244 | 2400  | 3.5535        |
+| 0.0254 | 2500  | 3.3532        |
+| 0.0264 | 2600  | 3.3329        |
+| 0.0275 | 2700  | 3.3375        |
+| 0.0285 | 2800  | 3.1954        |
+| 0.0295 | 2900  | 3.1045        |
+| 0.0305 | 3000  | 3.2362        |
+| 0.0315 | 3100  | 3.1298        |
+| 0.0326 | 3200  | 2.9862        |
+| 0.0336 | 3300  | 2.9966        |
+| 0.0346 | 3400  | 2.9131        |
+| 0.0356 | 3500  | 3.0058        |
+| 0.0366 | 3600  | 2.5357        |
+| 0.0376 | 3700  | 2.7563        |
+| 0.0387 | 3800  | 2.8085        |
+| 0.0397 | 3900  | 2.6729        |
+| 0.0407 | 4000  | 2.5785        |
+| 0.0417 | 4100  | 2.7879        |
+| 0.0427 | 4200  | 2.502         |
+| 0.0437 | 4300  | 2.3824        |
+| 0.0448 | 4400  | 2.4391        |
+| 0.0458 | 4500  | 2.3122        |
+| 0.0468 | 4600  | 2.2223        |
+| 0.0478 | 4700  | 2.4876        |
+| 0.0488 | 4800  | 2.5127        |
+| 0.0498 | 4900  | 2.3576        |
+| 0.0509 | 5000  | 1.961         |
+| 0.0519 | 5100  | 2.4402        |
+| 0.0529 | 5200  | 2.145         |
+| 0.0539 | 5300  | 2.2863        |
+| 0.0549 | 5400  | 2.2647        |
+| 0.0559 | 5500  | 2.1835        |
+| 0.0570 | 5600  | 2.0451        |
+| 0.0580 | 5700  | 2.0484        |
+| 0.0590 | 5800  | 2.1578        |
+| 0.0600 | 5900  | 2.1455        |
+| 0.0610 | 6000  | 2.0281        |
+| 0.0621 | 6100  | 2.0751        |
+| 0.0631 | 6200  | 1.9221        |
+| 0.0641 | 6300  | 1.8355        |
+| 0.0651 | 6400  | 1.9353        |
+| 0.0661 | 6500  | 1.8617        |
+| 0.0671 | 6600  | 1.8399        |
+| 0.0682 | 6700  | 1.927         |
+| 0.0692 | 6800  | 1.6166        |
+| 0.0702 | 6900  | 2.1288        |
+| 0.0712 | 7000  | 1.7884        |
+| 0.0722 | 7100  | 1.8565        |
+| 0.0732 | 7200  | 1.85          |
+| 0.0743 | 7300  | 1.7127        |
+| 0.0753 | 7400  | 1.7836        |
+| 0.0763 | 7500  | 1.6113        |
+| 0.0773 | 7600  | 1.8484        |
+| 0.0783 | 7700  | 1.8673        |
+| 0.0793 | 7800  | 1.6261        |
+| 0.0804 | 7900  | 1.6207        |
+| 0.0814 | 8000  | 2.0533        |
+| 0.0824 | 8100  | 1.729         |
+| 0.0834 | 8200  | 1.5739        |
+| 0.0844 | 8300  | 1.7526        |
+| 0.0855 | 8400  | 1.7466        |
+| 0.0865 | 8500  | 1.6939        |
+| 0.0875 | 8600  | 1.4806        |
+| 0.0885 | 8700  | 1.6851        |
+| 0.0895 | 8800  | 1.6117        |
+| 0.0905 | 8900  | 1.5053        |
+| 0.0916 | 9000  | 1.6736        |
+| 0.0926 | 9100  | 1.5396        |
+| 0.0936 | 9200  | 1.5309        |
+| 0.0946 | 9300  | 1.5081        |
+| 0.0956 | 9400  | 1.34          |
+| 0.0966 | 9500  | 1.5146        |
+| 0.0977 | 9600  | 1.3838        |
+| 0.0987 | 9700  | 1.559         |
+| 0.0997 | 9800  | 1.5523        |
+| 0.1007 | 9900  | 1.3153        |
+| 0.1017 | 10000 | 1.277         |
+| 0.1027 | 10100 | 1.5285        |
+| 0.1038 | 10200 | 1.3658        |
+| 0.1048 | 10300 | 1.4931        |
+| 0.1058 | 10400 | 1.3631        |
+| 0.1068 | 10500 | 1.3536        |
+| 0.1078 | 10600 | 1.4563        |
+| 0.1088 | 10700 | 1.4296        |
+| 0.1099 | 10800 | 1.4555        |
+| 0.1109 | 10900 | 1.5459        |
+| 0.1119 | 11000 | 1.4178        |
+| 0.1129 | 11100 | 1.4425        |
+| 0.1139 | 11200 | 1.3951        |
+| 0.1150 | 11300 | 1.2531        |
+| 0.1160 | 11400 | 1.4435        |
+| 0.1170 | 11500 | 1.168         |
+| 0.1180 | 11600 | 1.3839        |
+| 0.1190 | 11700 | 1.4541        |
+| 0.1200 | 11800 | 1.2666        |
+| 0.1211 | 11900 | 1.3136        |
+| 0.1221 | 12000 | 1.3001        |
+| 0.1231 | 12100 | 1.1904        |
+| 0.1241 | 12200 | 1.2617        |
+| 0.1251 | 12300 | 1.2397        |
+| 0.1261 | 12400 | 1.5342        |
+| 0.1272 | 12500 | 1.3735        |
+| 0.1282 | 12600 | 1.2123        |
+| 0.1292 | 12700 | 1.28          |
+| 0.1302 | 12800 | 1.3773        |
+| 0.1312 | 12900 | 1.3931        |
+| 0.1322 | 13000 | 1.4614        |
+| 0.1333 | 13100 | 1.3945        |
+| 0.1343 | 13200 | 1.4541        |
+| 0.1353 | 13300 | 1.2571        |
+| 0.1363 | 13400 | 1.1574        |
+| 0.1373 | 13500 | 1.2597        |
+| 0.1383 | 13600 | 1.2595        |
+| 0.1394 | 13700 | 1.218         |
+| 0.1404 | 13800 | 1.262         |
+| 0.1414 | 13900 | 1.0565        |
+| 0.1424 | 14000 | 1.1767        |
+| 0.1434 | 14100 | 1.2089        |
+| 0.1445 | 14200 | 1.211         |
+| 0.1455 | 14300 | 0.8943        |
+| 0.1465 | 14400 | 1.2541        |
+| 0.1475 | 14500 | 1.1358        |
+| 0.1485 | 14600 | 0.9817        |
+| 0.1495 | 14700 | 1.1535        |
+| 0.1506 | 14800 | 1.2066        |
+| 0.1516 | 14900 | 1.2272        |
+| 0.1526 | 15000 | 0.9362        |
+| 0.1536 | 15100 | 1.3058        |
+| 0.1546 | 15200 | 1.2812        |
+| 0.1556 | 15300 | 1.1447        |
+| 0.1567 | 15400 | 1.2213        |
+| 0.1577 | 15500 | 1.1535        |
+| 0.1587 | 15600 | 1.5273        |
+| 0.1597 | 15700 | 1.0432        |
+| 0.1607 | 15800 | 1.3215        |
+| 0.1617 | 15900 | 1.0787        |
+| 0.1628 | 16000 | 1.1641        |
+| 0.1638 | 16100 | 1.0483        |
+| 0.1648 | 16200 | 1.3148        |
+| 0.1658 | 16300 | 1.0111        |
+| 0.1668 | 16400 | 1.1823        |
+| 0.1678 | 16500 | 1.2526        |
+| 0.1689 | 16600 | 0.8983        |
+| 0.1699 | 16700 | 1.1997        |
+| 0.1709 | 16800 | 1.1394        |
+| 0.1719 | 16900 | 1.1923        |
+| 0.1729 | 17000 | 1.1439        |
+| 0.1740 | 17100 | 1.259         |
+| 0.1750 | 17200 | 1.3803        |
+| 0.1760 | 17300 | 1.1672        |
+| 0.1770 | 17400 | 1.149         |
+| 0.1780 | 17500 | 1.0019        |
+| 0.1790 | 17600 | 0.9692        |
+| 0.1801 | 17700 | 1.1611        |
+| 0.1811 | 17800 | 1.111         |
+| 0.1821 | 17900 | 0.9874        |
+| 0.1831 | 18000 | 1.2028        |
+| 0.1841 | 18100 | 0.9416        |
+| 0.1851 | 18200 | 1.1619        |
+| 0.1862 | 18300 | 1.17          |
+| 0.1872 | 18400 | 1.003         |
+| 0.1882 | 18500 | 0.9409        |
+| 0.1892 | 18600 | 0.9224        |
+| 0.1902 | 18700 | 0.9215        |
+| 0.1912 | 18800 | 1.2007        |
+| 0.1923 | 18900 | 1.0021        |
+| 0.1933 | 19000 | 1.0305        |
+| 0.1943 | 19100 | 1.1084        |
+| 0.1953 | 19200 | 0.961         |
+| 0.1963 | 19300 | 0.9769        |
+| 0.1973 | 19400 | 1.218         |
+| 0.1984 | 19500 | 1.043         |
+| 0.1994 | 19600 | 1.0366        |
+| 0.2004 | 19700 | 0.9459        |
+| 0.2014 | 19800 | 1.0557        |
+| 0.2024 | 19900 | 1.0953        |
+| 0.2035 | 20000 | 1.0327        |
+| 0.2045 | 20100 | 1.0284        |
+| 0.2055 | 20200 | 0.9376        |
+| 0.2065 | 20300 | 1.1122        |
+| 0.2075 | 20400 | 0.9807        |
+| 0.2085 | 20500 | 0.9054        |
+| 0.2096 | 20600 | 1.069         |
+| 0.2106 | 20700 | 1.0802        |
+| 0.2116 | 20800 | 0.9857        |
+| 0.2126 | 20900 | 1.1127        |
+| 0.2136 | 21000 | 1.2601        |
+| 0.2146 | 21100 | 0.9709        |
+| 0.2157 | 21200 | 0.9984        |
+| 0.2167 | 21300 | 1.1281        |
+| 0.2177 | 21400 | 0.8692        |
+| 0.2187 | 21500 | 1.1773        |
+| 0.2197 | 21600 | 0.9221        |
+| 0.2207 | 21700 | 0.9007        |
+| 0.2218 | 21800 | 1.0686        |
+| 0.2228 | 21900 | 1.1078        |
+| 0.2238 | 22000 | 0.999         |
+| 0.2248 | 22100 | 0.8577        |
+| 0.2258 | 22200 | 1.0215        |
+| 0.2268 | 22300 | 0.9952        |
+| 0.2279 | 22400 | 0.9597        |
+| 0.2289 | 22500 | 0.79          |
+| 0.2299 | 22600 | 1.1086        |
+| 0.2309 | 22700 | 1.1255        |
+| 0.2319 | 22800 | 1.0515        |
+| 0.2330 | 22900 | 0.9184        |
+| 0.2340 | 23000 | 1.0096        |
+| 0.2350 | 23100 | 1.0243        |
+| 0.2360 | 23200 | 1.0578        |
+| 0.2370 | 23300 | 0.9486        |
+| 0.2380 | 23400 | 1.0553        |
+| 0.2391 | 23500 | 0.9279        |
+| 0.2401 | 23600 | 0.9487        |
+| 0.2411 | 23700 | 1.0134        |
+| 0.2421 | 23800 | 0.7462        |
+| 0.2431 | 23900 | 0.7586        |
+| 0.2441 | 24000 | 0.9968        |
+| 0.2452 | 24100 | 1.1576        |
+| 0.2462 | 24200 | 0.8984        |
+| 0.2472 | 24300 | 1.0449        |
+| 0.2482 | 24400 | 0.886         |
+| 0.2492 | 24500 | 0.9021        |
+| 0.2502 | 24600 | 1.1053        |
+| 0.2513 | 24700 | 0.9241        |
+| 0.2523 | 24800 | 1.0178        |
+| 0.2533 | 24900 | 1.0758        |
+| 0.2543 | 25000 | 0.8807        |
+| 0.2553 | 25100 | 0.9876        |
+| 0.2564 | 25200 | 1.0116        |
+| 0.2574 | 25300 | 0.7735        |
+| 0.2584 | 25400 | 1.0378        |
+| 0.2594 | 25500 | 1.0           |
+| 0.2604 | 25600 | 0.8934        |
+| 0.2614 | 25700 | 0.9769        |
+| 0.2625 | 25800 | 1.2004        |
+| 0.2635 | 25900 | 0.9047        |
+| 0.2645 | 26000 | 0.8331        |
+| 0.2655 | 26100 | 1.0331        |
+| 0.2665 | 26200 | 1.0265        |
+| 0.2675 | 26300 | 0.8131        |
+| 0.2686 | 26400 | 1.0083        |
+| 0.2696 | 26500 | 1.0486        |
+| 0.2706 | 26600 | 0.8721        |
+| 0.2716 | 26700 | 0.9227        |
+| 0.2726 | 26800 | 1.0438        |
+| 0.2736 | 26900 | 0.6701        |
+| 0.2747 | 27000 | 0.8246        |
+| 0.2757 | 27100 | 0.8877        |
+| 0.2767 | 27200 | 0.8974        |
+| 0.2777 | 27300 | 0.9877        |
+| 0.2787 | 27400 | 0.8809        |
+| 0.2797 | 27500 | 0.8058        |
+| 0.2808 | 27600 | 1.0499        |
+| 0.2818 | 27700 | 1.0949        |
+| 0.2828 | 27800 | 1.0794        |
+| 0.2838 | 27900 | 0.7273        |
+| 0.2848 | 28000 | 0.8775        |
+| 0.2859 | 28100 | 0.7947        |
+| 0.2869 | 28200 | 0.9967        |
+| 0.2879 | 28300 | 1.0834        |
+| 0.2889 | 28400 | 0.8397        |
+| 0.2899 | 28500 | 0.9808        |
+| 0.2909 | 28600 | 0.8525        |
+| 0.2920 | 28700 | 0.6795        |
+| 0.2930 | 28800 | 0.8213        |
+| 0.2940 | 28900 | 0.7962        |
+| 0.2950 | 29000 | 0.7181        |
+| 0.2960 | 29100 | 0.7304        |
+| 0.2970 | 29200 | 0.8983        |
+| 0.2981 | 29300 | 0.8157        |
+| 0.2991 | 29400 | 0.9902        |
+| 0.3001 | 29500 | 1.106         |
+| 0.3011 | 29600 | 0.9016        |
+| 0.3021 | 29700 | 0.9756        |
+| 0.3031 | 29800 | 0.9426        |
+| 0.3042 | 29900 | 0.8033        |
+| 0.3052 | 30000 | 0.7583        |
+| 0.3062 | 30100 | 0.8602        |
+| 0.3072 | 30200 | 0.8691        |
+| 0.3082 | 30300 | 1.0453        |
+| 0.3092 | 30400 | 0.9485        |
+| 0.3103 | 30500 | 0.9637        |
+| 0.3113 | 30600 | 0.8028        |
+| 0.3123 | 30700 | 0.9261        |
+| 0.3133 | 30800 | 0.7166        |
+| 0.3143 | 30900 | 0.8809        |
+| 0.3154 | 31000 | 0.8061        |
+| 0.3164 | 31100 | 0.9817        |
+| 0.3174 | 31200 | 0.94          |
+| 0.3184 | 31300 | 0.7935        |
+| 0.3194 | 31400 | 0.8372        |
+| 0.3204 | 31500 | 1.1727        |
+| 0.3215 | 31600 | 0.7606        |
+| 0.3225 | 31700 | 0.9101        |
+| 0.3235 | 31800 | 0.681         |
+| 0.3245 | 31900 | 0.9235        |
+| 0.3255 | 32000 | 0.7649        |
+| 0.3265 | 32100 | 0.7917        |
+| 0.3276 | 32200 | 0.9602        |
+| 0.3286 | 32300 | 0.8561        |
+| 0.3296 | 32400 | 0.7201        |
+| 0.3306 | 32500 | 0.9261        |
+| 0.3316 | 32600 | 0.9769        |
+| 0.3326 | 32700 | 0.7281        |
+| 0.3337 | 32800 | 0.8497        |
+| 0.3347 | 32900 | 0.935         |
+| 0.3357 | 33000 | 0.8837        |
+| 0.3367 | 33100 | 0.6759        |
+| 0.3377 | 33200 | 0.9258        |
+| 0.3387 | 33300 | 0.8128        |
+| 0.3398 | 33400 | 0.8352        |
+| 0.3408 | 33500 | 0.7642        |
+| 0.3418 | 33600 | 0.8117        |
+| 0.3428 | 33700 | 0.8024        |
+| 0.3438 | 33800 | 0.6297        |
+| 0.3449 | 33900 | 0.8447        |
+| 0.3459 | 34000 | 0.9483        |
+| 0.3469 | 34100 | 0.6316        |
+| 0.3479 | 34200 | 0.9778        |
+| 0.3489 | 34300 | 1.2536        |
+| 0.3499 | 34400 | 0.8554        |
+| 0.3510 | 34500 | 0.7636        |
+| 0.3520 | 34600 | 0.9228        |
+| 0.3530 | 34700 | 1.2065        |
+| 0.3540 | 34800 | 0.7422        |
+| 0.3550 | 34900 | 0.836         |
+| 0.3560 | 35000 | 0.7612        |
+| 0.3571 | 35100 | 1.0686        |
+| 0.3581 | 35200 | 0.8227        |
+| 0.3591 | 35300 | 0.8035        |
+| 0.3601 | 35400 | 0.8518        |
+| 0.3611 | 35500 | 0.7877        |
+| 0.3621 | 35600 | 0.977         |
+| 0.3632 | 35700 | 0.7444        |
+| 0.3642 | 35800 | 1.0152        |
+| 0.3652 | 35900 | 0.9753        |
+| 0.3662 | 36000 | 0.7451        |
+| 0.3672 | 36100 | 0.9164        |
+| 0.3682 | 36200 | 0.8737        |
+| 0.3693 | 36300 | 0.7609        |
+| 0.3703 | 36400 | 0.9682        |
+| 0.3713 | 36500 | 0.7839        |
+| 0.3723 | 36600 | 0.7669        |
+| 0.3733 | 36700 | 0.7462        |
+| 0.3744 | 36800 | 0.816         |
+| 0.3754 | 36900 | 0.7701        |
+| 0.3764 | 37000 | 0.9624        |
+| 0.3774 | 37100 | 0.7194        |
+| 0.3784 | 37200 | 0.8559        |
+| 0.3794 | 37300 | 1.0938        |
+| 0.3805 | 37400 | 0.7587        |
+| 0.3815 | 37500 | 0.641         |
+| 0.3825 | 37600 | 0.891         |
+| 0.3835 | 37700 | 0.6906        |
+| 0.3845 | 37800 | 1.0998        |
+| 0.3855 | 37900 | 0.7198        |
+| 0.3866 | 38000 | 0.8502        |
+| 0.3876 | 38100 | 0.8793        |
+| 0.3886 | 38200 | 0.6859        |
+| 0.3896 | 38300 | 1.0219        |
+| 0.3906 | 38400 | 0.7076        |
+| 0.3916 | 38500 | 0.6722        |
+| 0.3927 | 38600 | 0.9803        |
+| 0.3937 | 38700 | 0.7202        |
+| 0.3947 | 38800 | 0.9244        |
+| 0.3957 | 38900 | 0.6677        |
+| 0.3967 | 39000 | 0.7115        |
+| 0.3977 | 39100 | 0.8265        |
+| 0.3988 | 39200 | 0.7452        |
+| 0.3998 | 39300 | 0.9035        |
+| 0.4008 | 39400 | 0.8995        |
+| 0.4018 | 39500 | 0.8057        |
+| 0.4028 | 39600 | 0.5763        |
+| 0.4039 | 39700 | 0.8714        |
+| 0.4049 | 39800 | 0.8986        |
+| 0.4059 | 39900 | 0.9301        |
+| 0.4069 | 40000 | 0.6497        |
+| 0.4079 | 40100 | 0.6254        |
+| 0.4089 | 40200 | 0.6554        |
+| 0.4100 | 40300 | 0.6868        |
+| 0.4110 | 40400 | 0.7385        |
+| 0.4120 | 40500 | 0.7142        |
+| 0.4130 | 40600 | 0.6881        |
+| 0.4140 | 40700 | 0.692         |
+| 0.4150 | 40800 | 0.642         |
+| 0.4161 | 40900 | 0.6089        |
+| 0.4171 | 41000 | 0.8139        |
+| 0.4181 | 41100 | 0.8346        |
+| 0.4191 | 41200 | 0.7895        |
+| 0.4201 | 41300 | 0.7008        |
+| 0.4211 | 41400 | 0.8188        |
+| 0.4222 | 41500 | 0.7435        |
+| 0.4232 | 41600 | 0.791         |
+| 0.4242 | 41700 | 0.6331        |
+| 0.4252 | 41800 | 1.0351        |
+| 0.4262 | 41900 | 0.6224        |
+| 0.4273 | 42000 | 0.8503        |
+| 0.4283 | 42100 | 0.6022        |
+| 0.4293 | 42200 | 0.6865        |
+| 0.4303 | 42300 | 0.7772        |
+| 0.4313 | 42400 | 0.8394        |
+| 0.4323 | 42500 | 0.878         |
+| 0.4334 | 42600 | 0.7826        |
+| 0.4344 | 42700 | 0.7188        |
+| 0.4354 | 42800 | 0.8372        |
+| 0.4364 | 42900 | 0.5603        |
+| 0.4374 | 43000 | 0.8899        |
+| 0.4384 | 43100 | 0.7556        |
+| 0.4395 | 43200 | 0.7705        |
+| 0.4405 | 43300 | 0.6577        |
+| 0.4415 | 43400 | 0.7987        |
+| 0.4425 | 43500 | 0.8235        |
+| 0.4435 | 43600 | 0.7176        |
+| 0.4445 | 43700 | 0.9219        |
+| 0.4456 | 43800 | 0.7193        |
+| 0.4466 | 43900 | 0.8563        |
+| 0.4476 | 44000 | 0.821         |
+| 0.4486 | 44100 | 0.7397        |
+| 0.4496 | 44200 | 0.6185        |
+| 0.4506 | 44300 | 0.8103        |
+| 0.4517 | 44400 | 0.7249        |
+| 0.4527 | 44500 | 0.5748        |
+| 0.4537 | 44600 | 0.502         |
+| 0.4547 | 44700 | 0.6905        |
+| 0.4557 | 44800 | 0.5475        |
+| 0.4568 | 44900 | 0.8287        |
+| 0.4578 | 45000 | 0.8498        |
+| 0.4588 | 45100 | 0.7757        |
+| 0.4598 | 45200 | 0.7711        |
+| 0.4608 | 45300 | 0.602         |
+| 0.4618 | 45400 | 0.7462        |
+| 0.4629 | 45500 | 0.8515        |
+| 0.4639 | 45600 | 0.7722        |
+| 0.4649 | 45700 | 0.8844        |
+| 0.4659 | 45800 | 0.5903        |
+| 0.4669 | 45900 | 0.5556        |
+| 0.4679 | 46000 | 0.7143        |
+| 0.4690 | 46100 | 0.7083        |
+| 0.4700 | 46200 | 0.6673        |
+| 0.4710 | 46300 | 0.7972        |
+| 0.4720 | 46400 | 0.6685        |
+| 0.4730 | 46500 | 0.751         |
+| 0.4740 | 46600 | 0.5364        |
+| 0.4751 | 46700 | 0.7858        |
+| 0.4761 | 46800 | 0.7102        |
+| 0.4771 | 46900 | 0.6758        |
+| 0.4781 | 47000 | 0.8075        |
+| 0.4791 | 47100 | 0.785         |
+| 0.4801 | 47200 | 0.602         |
+| 0.4812 | 47300 | 0.619         |
+| 0.4822 | 47400 | 0.8525        |
+| 0.4832 | 47500 | 0.6255        |
+| 0.4842 | 47600 | 0.7516        |
+| 0.4852 | 47700 | 0.6707        |
+| 0.4863 | 47800 | 0.5144        |
+| 0.4873 | 47900 | 0.7654        |
+| 0.4883 | 48000 | 0.9047        |
+| 0.4893 | 48100 | 0.786         |
+| 0.4903 | 48200 | 0.6384        |
+| 0.4913 | 48300 | 0.6442        |
+| 0.4924 | 48400 | 0.7419        |
+| 0.4934 | 48500 | 0.6694        |
+| 0.4944 | 48600 | 0.7353        |
+| 0.4954 | 48700 | 0.7712        |
+| 0.4964 | 48800 | 0.6879        |
+| 0.4974 | 48900 | 0.5942        |
+| 0.4985 | 49000 | 0.678         |
+| 0.4995 | 49100 | 0.6405        |
+| 0.5005 | 49200 | 0.7724        |
+| 0.5015 | 49300 | 0.8365        |
+| 0.5025 | 49400 | 0.7915        |
+| 0.5035 | 49500 | 0.8199        |
+| 0.5046 | 49600 | 0.8333        |
+| 0.5056 | 49700 | 0.8168        |
+| 0.5066 | 49800 | 0.7845        |
+| 0.5076 | 49900 | 0.8433        |
+| 0.5086 | 50000 | 0.6277        |
+| 0.5096 | 50100 | 0.8093        |
+| 0.5107 | 50200 | 0.574         |
+| 0.5117 | 50300 | 0.6589        |
+| 0.5127 | 50400 | 0.7758        |
+| 0.5137 | 50500 | 0.6896        |
+| 0.5147 | 50600 | 0.6508        |
+| 0.5158 | 50700 | 0.6148        |
+| 0.5168 | 50800 | 0.7687        |
+| 0.5178 | 50900 | 0.6126        |
+| 0.5188 | 51000 | 0.7048        |
+| 0.5198 | 51100 | 0.7072        |
+| 0.5208 | 51200 | 0.5995        |
+| 0.5219 | 51300 | 0.5265        |
+| 0.5229 | 51400 | 0.6596        |
+| 0.5239 | 51500 | 0.6224        |
+| 0.5249 | 51600 | 0.7283        |
+| 0.5259 | 51700 | 0.7278        |
+| 0.5269 | 51800 | 0.6278        |
+| 0.5280 | 51900 | 0.8234        |
+| 0.5290 | 52000 | 0.5623        |
+| 0.5300 | 52100 | 0.6815        |
+| 0.5310 | 52200 | 0.671         |
+| 0.5320 | 52300 | 0.6753        |
+| 0.5330 | 52400 | 0.777         |
+| 0.5341 | 52500 | 0.6418        |
+| 0.5351 | 52600 | 0.8762        |
+| 0.5361 | 52700 | 0.6557        |
+| 0.5371 | 52800 | 0.8074        |
+| 0.5381 | 52900 | 0.6798        |
+| 0.5391 | 53000 | 0.7247        |
+| 0.5402 | 53100 | 0.9169        |
+| 0.5412 | 53200 | 0.5862        |
+| 0.5422 | 53300 | 0.7443        |
+| 0.5432 | 53400 | 0.7391        |
+| 0.5442 | 53500 | 0.6815        |
+| 0.5453 | 53600 | 0.6833        |
+| 0.5463 | 53700 | 0.7782        |
+| 0.5473 | 53800 | 0.7014        |
+| 0.5483 | 53900 | 0.555         |
+| 0.5493 | 54000 | 0.579         |
+| 0.5503 | 54100 | 0.5532        |
+| 0.5514 | 54200 | 0.7326        |
+| 0.5524 | 54300 | 0.7446        |
+| 0.5534 | 54400 | 0.6812        |
+| 0.5544 | 54500 | 0.7733        |
+| 0.5554 | 54600 | 0.8537        |
+| 0.5564 | 54700 | 0.7317        |
+| 0.5575 | 54800 | 0.4924        |
+| 0.5585 | 54900 | 0.7506        |
+| 0.5595 | 55000 | 0.7103        |
+| 0.5605 | 55100 | 0.7394        |
+| 0.5615 | 55200 | 0.7605        |
+| 0.5625 | 55300 | 0.4556        |
+| 0.5636 | 55400 | 0.7929        |
+| 0.5646 | 55500 | 0.6873        |
+| 0.5656 | 55600 | 0.6985        |
+| 0.5666 | 55700 | 0.6687        |
+| 0.5676 | 55800 | 0.5939        |
+| 0.5686 | 55900 | 0.7572        |
+| 0.5697 | 56000 | 0.8489        |
+| 0.5707 | 56100 | 0.6354        |
+| 0.5717 | 56200 | 0.85          |
+| 0.5727 | 56300 | 0.8828        |
+| 0.5737 | 56400 | 0.652         |
+| 0.5748 | 56500 | 0.7322        |
+| 0.5758 | 56600 | 0.6399        |
+| 0.5768 | 56700 | 0.6225        |
+| 0.5778 | 56800 | 0.6981        |
+| 0.5788 | 56900 | 0.6672        |
+| 0.5798 | 57000 | 0.6847        |
+| 0.5809 | 57100 | 0.7851        |
+| 0.5819 | 57200 | 0.8353        |
+| 0.5829 | 57300 | 0.7278        |
+| 0.5839 | 57400 | 0.8386        |
+| 0.5849 | 57500 | 0.5678        |
+| 0.5859 | 57600 | 0.6292        |
+| 0.5870 | 57700 | 0.6984        |
+| 0.5880 | 57800 | 0.6169        |
+| 0.5890 | 57900 | 0.7627        |
+| 0.5900 | 58000 | 0.7501        |
+| 0.5910 | 58100 | 0.7363        |
+| 0.5920 | 58200 | 0.7827        |
+| 0.5931 | 58300 | 0.6598        |
+| 0.5941 | 58400 | 0.6824        |
+| 0.5951 | 58500 | 0.583         |
+| 0.5961 | 58600 | 0.5993        |
+| 0.5971 | 58700 | 0.4432        |
+| 0.5982 | 58800 | 0.9913        |
+| 0.5992 | 58900 | 0.7253        |
+| 0.6002 | 59000 | 0.7429        |
+| 0.6012 | 59100 | 0.6201        |
+| 0.6022 | 59200 | 0.6567        |
+| 0.6032 | 59300 | 0.6578        |
+| 0.6043 | 59400 | 0.7048        |
+| 0.6053 | 59500 | 0.8529        |
+| 0.6063 | 59600 | 0.6652        |
+| 0.6073 | 59700 | 0.7866        |
+| 0.6083 | 59800 | 0.4627        |
+| 0.6093 | 59900 | 0.6565        |
+| 0.6104 | 60000 | 0.6052        |
+| 0.6114 | 60100 | 0.5639        |
+| 0.6124 | 60200 | 0.5185        |
+| 0.6134 | 60300 | 0.5568        |
+| 0.6144 | 60400 | 0.5924        |
+| 0.6154 | 60500 | 0.664         |
+| 0.6165 | 60600 | 0.6261        |
+| 0.6175 | 60700 | 0.8437        |
+| 0.6185 | 60800 | 0.654         |
+| 0.6195 | 60900 | 0.5362        |
+| 0.6205 | 61000 | 0.6213        |
+| 0.6215 | 61100 | 0.7202        |
+| 0.6226 | 61200 | 0.633         |
+| 0.6236 | 61300 | 0.8508        |
+| 0.6246 | 61400 | 0.6462        |
+| 0.6256 | 61500 | 0.63          |
+| 0.6266 | 61600 | 0.8234        |
+| 0.6277 | 61700 | 0.5974        |
+| 0.6287 | 61800 | 0.7921        |
+| 0.6297 | 61900 | 0.5961        |
+| 0.6307 | 62000 | 0.614         |
+| 0.6317 | 62100 | 0.6615        |
+| 0.6327 | 62200 | 0.6531        |
+| 0.6338 | 62300 | 0.4864        |
+| 0.6348 | 62400 | 0.647         |
+| 0.6358 | 62500 | 0.6113        |
+| 0.6368 | 62600 | 0.6921        |
+| 0.6378 | 62700 | 0.5747        |
+| 0.6388 | 62800 | 0.7385        |
+| 0.6399 | 62900 | 0.5917        |
+| 0.6409 | 63000 | 0.5889        |
+| 0.6419 | 63100 | 0.6054        |
+| 0.6429 | 63200 | 0.561         |
+| 0.6439 | 63300 | 0.5997        |
+| 0.6449 | 63400 | 0.794         |
+| 0.6460 | 63500 | 0.7496        |
+| 0.6470 | 63600 | 0.6024        |
+| 0.6480 | 63700 | 0.5696        |
+| 0.6490 | 63800 | 0.5421        |
+| 0.6500 | 63900 | 0.4456        |
+| 0.6510 | 64000 | 0.6023        |
+| 0.6521 | 64100 | 0.4959        |
+| 0.6531 | 64200 | 0.5642        |
+| 0.6541 | 64300 | 0.6949        |
+| 0.6551 | 64400 | 0.6484        |
+| 0.6561 | 64500 | 0.7129        |
+| 0.6572 | 64600 | 0.6671        |
+| 0.6582 | 64700 | 0.4386        |
+| 0.6592 | 64800 | 0.6304        |
+| 0.6602 | 64900 | 0.7319        |
+| 0.6612 | 65000 | 0.5852        |
+| 0.6622 | 65100 | 0.6596        |
+| 0.6633 | 65200 | 0.5671        |
+| 0.6643 | 65300 | 0.738         |
+| 0.6653 | 65400 | 0.6173        |
+| 0.6663 | 65500 | 0.6302        |
+| 0.6673 | 65600 | 0.6919        |
+| 0.6683 | 65700 | 0.8582        |
+| 0.6694 | 65800 | 0.7258        |
+| 0.6704 | 65900 | 0.6371        |
+| 0.6714 | 66000 | 0.6237        |
+| 0.6724 | 66100 | 0.5698        |
+| 0.6734 | 66200 | 0.6232        |
+| 0.6744 | 66300 | 0.5277        |
+| 0.6755 | 66400 | 0.7142        |
+| 0.6765 | 66500 | 0.3874        |
+| 0.6775 | 66600 | 0.7239        |
+| 0.6785 | 66700 | 0.649         |
+| 0.6795 | 66800 | 0.5919        |
+| 0.6805 | 66900 | 0.611         |
+| 0.6816 | 67000 | 0.6857        |
+| 0.6826 | 67100 | 0.7571        |
+| 0.6836 | 67200 | 0.6295        |
+| 0.6846 | 67300 | 0.6233        |
+| 0.6856 | 67400 | 0.4612        |
+| 0.6867 | 67500 | 0.6029        |
+| 0.6877 | 67600 | 0.8331        |
+| 0.6887 | 67700 | 0.5839        |
+| 0.6897 | 67800 | 0.7239        |
+| 0.6907 | 67900 | 0.7111        |
+| 0.6917 | 68000 | 0.4719        |
+| 0.6928 | 68100 | 0.6431        |
+| 0.6938 | 68200 | 0.5993        |
+| 0.6948 | 68300 | 0.5523        |
+| 0.6958 | 68400 | 0.7109        |
+| 0.6968 | 68500 | 0.7398        |
+| 0.6978 | 68600 | 0.5519        |
+| 0.6989 | 68700 | 0.6474        |
+| 0.6999 | 68800 | 0.7263        |
+| 0.7009 | 68900 | 0.5115        |
+| 0.7019 | 69000 | 0.4325        |
+| 0.7029 | 69100 | 0.5022        |
+| 0.7039 | 69200 | 0.5915        |
+| 0.7050 | 69300 | 0.3593        |
+| 0.7060 | 69400 | 0.6064        |
+| 0.7070 | 69500 | 0.9334        |
+| 0.7080 | 69600 | 0.5801        |
+| 0.7090 | 69700 | 0.7087        |
+| 0.7100 | 69800 | 0.5999        |
+| 0.7111 | 69900 | 0.6629        |
+| 0.7121 | 70000 | 0.5959        |
+| 0.7131 | 70100 | 0.7499        |
+| 0.7141 | 70200 | 0.5318        |
+| 0.7151 | 70300 | 0.5121        |
+| 0.7162 | 70400 | 0.9055        |
+| 0.7172 | 70500 | 0.4307        |
+| 0.7182 | 70600 | 0.4902        |
+| 0.7192 | 70700 | 0.5367        |
+| 0.7202 | 70800 | 0.4899        |
+| 0.7212 | 70900 | 0.6768        |
+| 0.7223 | 71000 | 0.7288        |
+| 0.7233 | 71100 | 0.5998        |
+| 0.7243 | 71200 | 0.7799        |
+| 0.7253 | 71300 | 0.5984        |
+| 0.7263 | 71400 | 0.7752        |
+| 0.7273 | 71500 | 0.4164        |
+| 0.7284 | 71600 | 0.71          |
+| 0.7294 | 71700 | 0.5335        |
+| 0.7304 | 71800 | 0.5932        |
+| 0.7314 | 71900 | 0.6342        |
+| 0.7324 | 72000 | 0.5675        |
+| 0.7334 | 72100 | 0.7243        |
+| 0.7345 | 72200 | 0.7112        |
+| 0.7355 | 72300 | 0.6712        |
+| 0.7365 | 72400 | 0.6164        |
+| 0.7375 | 72500 | 0.5798        |
+| 0.7385 | 72600 | 0.5249        |
+| 0.7396 | 72700 | 0.4702        |
+| 0.7406 | 72800 | 0.4924        |
+| 0.7416 | 72900 | 0.598         |
+| 0.7426 | 73000 | 0.6151        |
+| 0.7436 | 73100 | 0.7369        |
+| 0.7446 | 73200 | 0.5661        |
+| 0.7457 | 73300 | 0.8368        |
+| 0.7467 | 73400 | 0.604         |
+| 0.7477 | 73500 | 0.5657        |
+| 0.7487 | 73600 | 0.4921        |
+| 0.7497 | 73700 | 0.5238        |
+| 0.7507 | 73800 | 0.6692        |
+| 0.7518 | 73900 | 0.6181        |
+| 0.7528 | 74000 | 0.6532        |
+| 0.7538 | 74100 | 0.5932        |
+| 0.7548 | 74200 | 0.6546        |
+| 0.7558 | 74300 | 0.7575        |
+| 0.7568 | 74400 | 0.6888        |
+| 0.7579 | 74500 | 0.6133        |
+| 0.7589 | 74600 | 0.6941        |
+| 0.7599 | 74700 | 0.6219        |
+| 0.7609 | 74800 | 0.6053        |
+| 0.7619 | 74900 | 0.5401        |
+| 0.7629 | 75000 | 0.6957        |
+| 0.7640 | 75100 | 0.7152        |
+| 0.7650 | 75200 | 0.5549        |
+| 0.7660 | 75300 | 0.7595        |
+| 0.7670 | 75400 | 0.6008        |
+| 0.7680 | 75500 | 0.6865        |
+| 0.7691 | 75600 | 0.6998        |
+| 0.7701 | 75700 | 0.5809        |
+| 0.7711 | 75800 | 0.6945        |
+| 0.7721 | 75900 | 0.5277        |
+| 0.7731 | 76000 | 0.4838        |
+| 0.7741 | 76100 | 0.6694        |
+| 0.7752 | 76200 | 0.7267        |
+| 0.7762 | 76300 | 0.5172        |
+| 0.7772 | 76400 | 0.6081        |
+| 0.7782 | 76500 | 0.5904        |
+| 0.7792 | 76600 | 0.7423        |
+| 0.7802 | 76700 | 0.5854        |
+| 0.7813 | 76800 | 0.5187        |
+| 0.7823 | 76900 | 0.5163        |
+| 0.7833 | 77000 | 0.59          |
+| 0.7843 | 77100 | 0.6303        |
+| 0.7853 | 77200 | 0.7633        |
+| 0.7863 | 77300 | 0.3922        |
+| 0.7874 | 77400 | 0.5958        |
+| 0.7884 | 77500 | 0.5794        |
+| 0.7894 | 77600 | 0.7614        |
+| 0.7904 | 77700 | 0.6195        |
+| 0.7914 | 77800 | 0.6392        |
+| 0.7924 | 77900 | 0.5152        |
+| 0.7935 | 78000 | 0.6551        |
+| 0.7945 | 78100 | 0.6728        |
+| 0.7955 | 78200 | 0.4994        |
+| 0.7965 | 78300 | 0.4807        |
+| 0.7975 | 78400 | 0.5193        |
+| 0.7986 | 78500 | 0.6285        |
+| 0.7996 | 78600 | 0.4851        |
+| 0.8006 | 78700 | 0.5756        |
+| 0.8016 | 78800 | 0.5533        |
+| 0.8026 | 78900 | 0.705         |
+| 0.8036 | 79000 | 0.5025        |
+| 0.8047 | 79100 | 0.463         |
+| 0.8057 | 79200 | 0.6687        |
+| 0.8067 | 79300 | 0.5076        |
+| 0.8077 | 79400 | 0.6565        |
+| 0.8087 | 79500 | 0.6617        |
+| 0.8097 | 79600 | 0.4685        |
+| 0.8108 | 79700 | 0.6223        |
+| 0.8118 | 79800 | 0.6922        |
+| 0.8128 | 79900 | 0.7718        |
+| 0.8138 | 80000 | 0.5657        |
+| 0.8148 | 80100 | 0.543         |
+| 0.8158 | 80200 | 0.7921        |
+| 0.8169 | 80300 | 0.6572        |
+| 0.8179 | 80400 | 0.7411        |
+| 0.8189 | 80500 | 0.5726        |
+| 0.8199 | 80600 | 0.6093        |
+| 0.8209 | 80700 | 0.5758        |
+| 0.8219 | 80800 | 0.518         |
+| 0.8230 | 80900 | 0.694         |
+| 0.8240 | 81000 | 0.7515        |
+| 0.8250 | 81100 | 0.6002        |
+| 0.8260 | 81200 | 0.4633        |
+| 0.8270 | 81300 | 0.6218        |
+| 0.8281 | 81400 | 0.5532        |
+| 0.8291 | 81500 | 0.4466        |
+| 0.8301 | 81600 | 0.5202        |
+| 0.8311 | 81700 | 0.6743        |
+| 0.8321 | 81800 | 0.5741        |
+| 0.8331 | 81900 | 0.6996        |
+| 0.8342 | 82000 | 0.7846        |
+| 0.8352 | 82100 | 0.6618        |
+| 0.8362 | 82200 | 0.6033        |
+| 0.8372 | 82300 | 0.4995        |
+| 0.8382 | 82400 | 0.5191        |
+| 0.8392 | 82500 | 0.6053        |
+| 0.8403 | 82600 | 0.525         |
+| 0.8413 | 82700 | 0.6632        |
+| 0.8423 | 82800 | 0.4557        |
+| 0.8433 | 82900 | 0.4545        |
+| 0.8443 | 83000 | 0.582         |
+| 0.8453 | 83100 | 0.4116        |
+| 0.8464 | 83200 | 0.7503        |
+| 0.8474 | 83300 | 0.8223        |
+| 0.8484 | 83400 | 0.6802        |
+| 0.8494 | 83500 | 0.4549        |
+| 0.8504 | 83600 | 0.6192        |
+| 0.8514 | 83700 | 0.5877        |
+| 0.8525 | 83800 | 0.6831        |
+| 0.8535 | 83900 | 0.6177        |
+| 0.8545 | 84000 | 0.5918        |
+| 0.8555 | 84100 | 0.6674        |
+| 0.8565 | 84200 | 0.518         |
+| 0.8576 | 84300 | 0.6378        |
+| 0.8586 | 84400 | 0.6648        |
+| 0.8596 | 84500 | 0.6655        |
+| 0.8606 | 84600 | 0.5005        |
+| 0.8616 | 84700 | 0.5276        |
+| 0.8626 | 84800 | 0.6636        |
+| 0.8637 | 84900 | 0.6573        |
+| 0.8647 | 85000 | 0.6104        |
+| 0.8657 | 85100 | 0.606         |
+| 0.8667 | 85200 | 0.537         |
+| 0.8677 | 85300 | 0.5331        |
+| 0.8687 | 85400 | 0.6714        |
+| 0.8698 | 85500 | 0.5361        |
+| 0.8708 | 85600 | 0.6583        |
+| 0.8718 | 85700 | 0.6888        |
+| 0.8728 | 85800 | 0.5044        |
+| 0.8738 | 85900 | 0.5655        |
+| 0.8748 | 86000 | 0.4413        |
+| 0.8759 | 86100 | 0.5836        |
+| 0.8769 | 86200 | 0.9184        |
+| 0.8779 | 86300 | 0.4408        |
+| 0.8789 | 86400 | 0.4715        |
+| 0.8799 | 86500 | 0.6001        |
+| 0.8809 | 86600 | 0.7137        |
+| 0.8820 | 86700 | 0.4078        |
+| 0.8830 | 86800 | 0.5395        |
+| 0.8840 | 86900 | 0.6508        |
+| 0.8850 | 87000 | 0.5879        |
+| 0.8860 | 87100 | 0.747         |
+| 0.8871 | 87200 | 0.4727        |
+| 0.8881 | 87300 | 0.5537        |
+| 0.8891 | 87400 | 0.6939        |
+| 0.8901 | 87500 | 0.612         |
+| 0.8911 | 87600 | 0.6922        |
+| 0.8921 | 87700 | 0.5248        |
+| 0.8932 | 87800 | 0.7751        |
+| 0.8942 | 87900 | 0.5789        |
+| 0.8952 | 88000 | 0.548         |
+| 0.8962 | 88100 | 0.5582        |
+| 0.8972 | 88200 | 0.5283        |
+| 0.8982 | 88300 | 0.67          |
+| 0.8993 | 88400 | 0.4805        |
+| 0.9003 | 88500 | 0.5471        |
+| 0.9013 | 88600 | 0.6269        |
+| 0.9023 | 88700 | 0.5893        |
+| 0.9033 | 88800 | 0.6513        |
+| 0.9043 | 88900 | 0.3424        |
+| 0.9054 | 89000 | 0.521         |
+| 0.9064 | 89100 | 0.7           |
+| 0.9074 | 89200 | 0.4389        |
+| 0.9084 | 89300 | 0.7586        |
+| 0.9094 | 89400 | 0.6371        |
+| 0.9105 | 89500 | 0.4141        |
+| 0.9115 | 89600 | 0.6428        |
+| 0.9125 | 89700 | 0.5555        |
+| 0.9135 | 89800 | 0.5973        |
+| 0.9145 | 89900 | 0.4516        |
+| 0.9155 | 90000 | 0.5601        |
+| 0.9166 | 90100 | 0.3904        |
+| 0.9176 | 90200 | 0.4576        |
+| 0.9186 | 90300 | 0.6065        |
+| 0.9196 | 90400 | 0.448         |
+| 0.9206 | 90500 | 0.5387        |
+| 0.9216 | 90600 | 0.7406        |
+| 0.9227 | 90700 | 0.5682        |
+| 0.9237 | 90800 | 0.6075        |
+| 0.9247 | 90900 | 0.5166        |
+| 0.9257 | 91000 | 0.6627        |
+| 0.9267 | 91100 | 0.6125        |
+| 0.9277 | 91200 | 0.6151        |
+| 0.9288 | 91300 | 0.376         |
+| 0.9298 | 91400 | 0.7488        |
+| 0.9308 | 91500 | 0.3872        |
+| 0.9318 | 91600 | 0.622         |
+| 0.9328 | 91700 | 0.6095        |
+| 0.9338 | 91800 | 0.4772        |
+| 0.9349 | 91900 | 0.4708        |
+| 0.9359 | 92000 | 0.5463        |
+| 0.9369 | 92100 | 0.7436        |
+| 0.9379 | 92200 | 0.698         |
+| 0.9389 | 92300 | 0.3119        |
+| 0.9400 | 92400 | 0.4237        |
+| 0.9410 | 92500 | 0.5579        |
+| 0.9420 | 92600 | 0.6101        |
+| 0.9430 | 92700 | 0.6106        |
+| 0.9440 | 92800 | 0.614         |
+| 0.9450 | 92900 | 0.6228        |
+| 0.9461 | 93000 | 0.5155        |
+| 0.9471 | 93100 | 0.6098        |
+| 0.9481 | 93200 | 0.6685        |
+| 0.9491 | 93300 | 0.3962        |
+| 0.9501 | 93400 | 0.5151        |
+| 0.9511 | 93500 | 0.4819        |
+| 0.9522 | 93600 | 0.5941        |
+| 0.9532 | 93700 | 0.5932        |
+| 0.9542 | 93800 | 0.6307        |
+| 0.9552 | 93900 | 0.6368        |
+| 0.9562 | 94000 | 0.6799        |
+| 0.9572 | 94100 | 0.5089        |
+| 0.9583 | 94200 | 0.5623        |
+| 0.9593 | 94300 | 0.4027        |
+| 0.9603 | 94400 | 0.6181        |
+| 0.9613 | 94500 | 0.5755        |
+| 0.9623 | 94600 | 0.5631        |
+| 0.9633 | 94700 | 0.4376        |
+| 0.9644 | 94800 | 0.429         |
+| 0.9654 | 94900 | 0.4997        |
+| 0.9664 | 95000 | 0.5789        |
+| 0.9674 | 95100 | 0.5636        |
+| 0.9684 | 95200 | 0.6638        |
+| 0.9695 | 95300 | 0.8632        |
+| 0.9705 | 95400 | 0.5708        |
+| 0.9715 | 95500 | 0.5817        |
+| 0.9725 | 95600 | 0.5245        |
+| 0.9735 | 95700 | 0.5836        |
+| 0.9745 | 95800 | 0.5696        |
+| 0.9756 | 95900 | 0.5988        |
+| 0.9766 | 96000 | 0.5597        |
+| 0.9776 | 96100 | 0.5968        |
+| 0.9786 | 96200 | 0.7544        |
+| 0.9796 | 96300 | 0.6484        |
+| 0.9806 | 96400 | 0.3758        |
+| 0.9817 | 96500 | 0.6732        |
+| 0.9827 | 96600 | 0.5634        |
+| 0.9837 | 96700 | 0.4491        |
+| 0.9847 | 96800 | 0.349         |
+| 0.9857 | 96900 | 0.6564        |
+| 0.9867 | 97000 | 0.5724        |
+| 0.9878 | 97100 | 0.6022        |
+| 0.9888 | 97200 | 0.3853        |
+| 0.9898 | 97300 | 0.6601        |
+| 0.9908 | 97400 | 0.6511        |
+| 0.9918 | 97500 | 0.4784        |
+| 0.9928 | 97600 | 0.5943        |
+| 0.9939 | 97700 | 0.8411        |
+| 0.9949 | 97800 | 0.5165        |
+| 0.9959 | 97900 | 0.4567        |
+| 0.9969 | 98000 | 0.492         |
+| 0.9979 | 98100 | 0.5838        |
+| 0.9990 | 98200 | 0.5109        |
+| 1.0000 | 98300 | 0.4494        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.4
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02d05b5b2b940b0e03bafb0ff61c7589a51f2c84c751811834089ead4cc3b960
+size 90864192

modules.json

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

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}