Upload folder using huggingface_hub

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,1087 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:8826496
+- loss:CoSENTLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: floral bouquet bikini cream
+  sentences:
+  - fudgy cake
+  - asian dressing quinoa salad
+  - dry skin body lotion
+- source_sentence: biscuit baklava
+  sentences:
+  - multicolour hoodie
+  - lokali pancakes
+  - off dress
+- source_sentence: sugar sprinkles cupcake
+  sentences:
+  - heavyweight incense holder
+  - konafa
+  - wool top
+- source_sentence: shirt chemise
+  sentences:
+  - crafted plate
+  - refreshing face serum
+  - winter jacket
+- source_sentence: chocolate cookie
+  sentences:
+  - shrimp rice
+  - sports wristbands
+  - women bathing cover
+datasets:
+- KhaledReda/pairs_three_scores_v7_balanced_calibrated
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v7-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [pairs_three_scores_v7_balanced_calibrated](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v7_balanced_calibrated) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [pairs_three_scores_v7_balanced_calibrated](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v7_balanced_calibrated)
+- **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 = [
+    'chocolate cookie',
+    'women bathing cover',
+    'sports wristbands',
+]
+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, 0.4979, 0.4782],
+#         [0.4979, 1.0000, 0.7929],
+#         [0.4782, 0.7929, 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_three_scores_v7_balanced_calibrated
+
+* Dataset: [pairs_three_scores_v7_balanced_calibrated](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v7_balanced_calibrated) at [9c7e882](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v7_balanced_calibrated/tree/9c7e882c2bceda2c6c9206a1f557de7d70b521e0)
+* Size: 8,826,496 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: 5.71 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.78 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                      | sentence2                          | score             |
+  |:-------------------------------|:-----------------------------------|:------------------|
+  | <code>lamb dog food</code>     | <code>frizz control shampoo</code> | <code>0.0</code>  |
+  | <code>high fiber muesli</code> | <code>cloth clutch</code>          | <code>0.04</code> |
+  | <code>smoked sausage</code>    | <code>ready to cook kofta</code>   | <code>0.43</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_three_scores_v7_balanced_calibrated
+
+* Dataset: [pairs_three_scores_v7_balanced_calibrated](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v7_balanced_calibrated) at [9c7e882](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v7_balanced_calibrated/tree/9c7e882c2bceda2c6c9206a1f557de7d70b521e0)
+* Size: 44,355 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: 5.59 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.7 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                         | sentence2                                  | score             |
+  |:----------------------------------|:-------------------------------------------|:------------------|
+  | <code>sandwich fries meal</code>  | <code>grilled chicken souvlaki wrap</code> | <code>1.0</code>  |
+  | <code>deli</code>                 | <code>juice glass</code>                   | <code>0.05</code> |
+  | <code>classic coffee shake</code> | <code>basic dress</code>                   | <code>0.05</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 |
+|:------:|:-----:|:-------------:|
+| 0.0015 | 100   | 13.3048       |
+| 0.0029 | 200   | 13.1862       |
+| 0.0044 | 300   | 12.8397       |
+| 0.0058 | 400   | 12.0152       |
+| 0.0073 | 500   | 11.4185       |
+| 0.0087 | 600   | 10.7677       |
+| 0.0102 | 700   | 9.8943        |
+| 0.0116 | 800   | 9.4991        |
+| 0.0131 | 900   | 9.1097        |
+| 0.0145 | 1000  | 8.7566        |
+| 0.0160 | 1100  | 8.5687        |
+| 0.0174 | 1200  | 8.4785        |
+| 0.0189 | 1300  | 8.4378        |
+| 0.0203 | 1400  | 8.3864        |
+| 0.0218 | 1500  | 8.3545        |
+| 0.0232 | 1600  | 8.3495        |
+| 0.0247 | 1700  | 8.3226        |
+| 0.0261 | 1800  | 8.3071        |
+| 0.0276 | 1900  | 8.2657        |
+| 0.0290 | 2000  | 8.2359        |
+| 0.0305 | 2100  | 8.2483        |
+| 0.0319 | 2200  | 8.2237        |
+| 0.0334 | 2300  | 8.1927        |
+| 0.0348 | 2400  | 8.1974        |
+| 0.0363 | 2500  | 8.1804        |
+| 0.0377 | 2600  | 8.1663        |
+| 0.0392 | 2700  | 8.1552        |
+| 0.0406 | 2800  | 8.1536        |
+| 0.0421 | 2900  | 8.132         |
+| 0.0435 | 3000  | 8.1287        |
+| 0.0450 | 3100  | 8.1068        |
+| 0.0464 | 3200  | 8.0878        |
+| 0.0479 | 3300  | 8.0974        |
+| 0.0493 | 3400  | 8.085         |
+| 0.0508 | 3500  | 8.0707        |
+| 0.0522 | 3600  | 8.0684        |
+| 0.0537 | 3700  | 8.0549        |
+| 0.0551 | 3800  | 8.0311        |
+| 0.0566 | 3900  | 8.0207        |
+| 0.0580 | 4000  | 8.0163        |
+| 0.0595 | 4100  | 8.0261        |
+| 0.0609 | 4200  | 8.0119        |
+| 0.0624 | 4300  | 7.9935        |
+| 0.0638 | 4400  | 7.9862        |
+| 0.0653 | 4500  | 7.9841        |
+| 0.0667 | 4600  | 7.973         |
+| 0.0682 | 4700  | 7.9396        |
+| 0.0696 | 4800  | 7.9148        |
+| 0.0711 | 4900  | 7.9454        |
+| 0.0725 | 5000  | 7.9606        |
+| 0.0740 | 5100  | 7.9254        |
+| 0.0754 | 5200  | 7.898         |
+| 0.0769 | 5300  | 7.8979        |
+| 0.0783 | 5400  | 7.8754        |
+| 0.0798 | 5500  | 7.9112        |
+| 0.0812 | 5600  | 7.8664        |
+| 0.0827 | 5700  | 7.8963        |
+| 0.0841 | 5800  | 7.8563        |
+| 0.0856 | 5900  | 7.8445        |
+| 0.0870 | 6000  | 7.857         |
+| 0.0885 | 6100  | 7.8432        |
+| 0.0899 | 6200  | 7.8388        |
+| 0.0914 | 6300  | 7.7983        |
+| 0.0928 | 6400  | 7.8176        |
+| 0.0943 | 6500  | 7.8265        |
+| 0.0957 | 6600  | 7.8054        |
+| 0.0972 | 6700  | 7.8091        |
+| 0.0986 | 6800  | 7.7831        |
+| 0.1001 | 6900  | 7.7593        |
+| 0.1015 | 7000  | 7.8059        |
+| 0.1030 | 7100  | 7.7663        |
+| 0.1044 | 7200  | 7.7572        |
+| 0.1059 | 7300  | 7.7801        |
+| 0.1073 | 7400  | 7.7551        |
+| 0.1088 | 7500  | 7.7223        |
+| 0.1102 | 7600  | 7.758         |
+| 0.1117 | 7700  | 7.7588        |
+| 0.1131 | 7800  | 7.7418        |
+| 0.1146 | 7900  | 7.7174        |
+| 0.1160 | 8000  | 7.6791        |
+| 0.1175 | 8100  | 7.6968        |
+| 0.1189 | 8200  | 7.7013        |
+| 0.1204 | 8300  | 7.6958        |
+| 0.1218 | 8400  | 7.6967        |
+| 0.1233 | 8500  | 7.6606        |
+| 0.1247 | 8600  | 7.6977        |
+| 0.1262 | 8700  | 7.6956        |
+| 0.1276 | 8800  | 7.6707        |
+| 0.1291 | 8900  | 7.6763        |
+| 0.1305 | 9000  | 7.6413        |
+| 0.1320 | 9100  | 7.6582        |
+| 0.1334 | 9200  | 7.641         |
+| 0.1349 | 9300  | 7.6999        |
+| 0.1363 | 9400  | 7.6453        |
+| 0.1378 | 9500  | 7.6351        |
+| 0.1392 | 9600  | 7.64          |
+| 0.1407 | 9700  | 7.6578        |
+| 0.1421 | 9800  | 7.6345        |
+| 0.1436 | 9900  | 7.6143        |
+| 0.1450 | 10000 | 7.6331        |
+| 0.1465 | 10100 | 7.5877        |
+| 0.1479 | 10200 | 7.6273        |
+| 0.1494 | 10300 | 7.5957        |
+| 0.1508 | 10400 | 7.6315        |
+| 0.1523 | 10500 | 7.5612        |
+| 0.1537 | 10600 | 7.6246        |
+| 0.1552 | 10700 | 7.5974        |
+| 0.1566 | 10800 | 7.6075        |
+| 0.1581 | 10900 | 7.5713        |
+| 0.1595 | 11000 | 7.5874        |
+| 0.1610 | 11100 | 7.5549        |
+| 0.1624 | 11200 | 7.5743        |
+| 0.1639 | 11300 | 7.5223        |
+| 0.1653 | 11400 | 7.5496        |
+| 0.1668 | 11500 | 7.5846        |
+| 0.1682 | 11600 | 7.5876        |
+| 0.1697 | 11700 | 7.5735        |
+| 0.1711 | 11800 | 7.5434        |
+| 0.1726 | 11900 | 7.5673        |
+| 0.1740 | 12000 | 7.5181        |
+| 0.1755 | 12100 | 7.5778        |
+| 0.1769 | 12200 | 7.5557        |
+| 0.1784 | 12300 | 7.5137        |
+| 0.1798 | 12400 | 7.546         |
+| 0.1813 | 12500 | 7.5375        |
+| 0.1827 | 12600 | 7.5576        |
+| 0.1842 | 12700 | 7.5075        |
+| 0.1856 | 12800 | 7.5178        |
+| 0.1871 | 12900 | 7.5254        |
+| 0.1885 | 13000 | 7.5349        |
+| 0.1900 | 13100 | 7.5193        |
+| 0.1914 | 13200 | 7.5352        |
+| 0.1929 | 13300 | 7.5146        |
+| 0.1943 | 13400 | 7.5305        |
+| 0.1958 | 13500 | 7.5202        |
+| 0.1972 | 13600 | 7.4845        |
+| 0.1987 | 13700 | 7.4707        |
+| 0.2001 | 13800 | 7.5193        |
+| 0.2016 | 13900 | 7.4866        |
+| 0.2030 | 14000 | 7.4782        |
+| 0.2045 | 14100 | 7.507         |
+| 0.2059 | 14200 | 7.47          |
+| 0.2074 | 14300 | 7.5295        |
+| 0.2088 | 14400 | 7.5049        |
+| 0.2103 | 14500 | 7.4738        |
+| 0.2117 | 14600 | 7.4808        |
+| 0.2132 | 14700 | 7.5044        |
+| 0.2146 | 14800 | 7.4933        |
+| 0.2161 | 14900 | 7.4481        |
+| 0.2175 | 15000 | 7.4323        |
+| 0.2190 | 15100 | 7.4591        |
+| 0.2204 | 15200 | 7.4819        |
+| 0.2219 | 15300 | 7.4255        |
+| 0.2233 | 15400 | 7.4383        |
+| 0.2248 | 15500 | 7.4858        |
+| 0.2262 | 15600 | 7.4464        |
+| 0.2277 | 15700 | 7.4639        |
+| 0.2291 | 15800 | 7.4269        |
+| 0.2306 | 15900 | 7.4219        |
+| 0.2320 | 16000 | 7.4719        |
+| 0.2335 | 16100 | 7.4446        |
+| 0.2349 | 16200 | 7.4658        |
+| 0.2364 | 16300 | 7.4489        |
+| 0.2378 | 16400 | 7.4082        |
+| 0.2393 | 16500 | 7.456         |
+| 0.2407 | 16600 | 7.4113        |
+| 0.2422 | 16700 | 7.4144        |
+| 0.2436 | 16800 | 7.4304        |
+| 0.2451 | 16900 | 7.4192        |
+| 0.2465 | 17000 | 7.4019        |
+| 0.2480 | 17100 | 7.4118        |
+| 0.2494 | 17200 | 7.4348        |
+| 0.2509 | 17300 | 7.4117        |
+| 0.2523 | 17400 | 7.3867        |
+| 0.2538 | 17500 | 7.4006        |
+| 0.2552 | 17600 | 7.4335        |
+| 0.2567 | 17700 | 7.404         |
+| 0.2581 | 17800 | 7.3861        |
+| 0.2596 | 17900 | 7.4109        |
+| 0.2610 | 18000 | 7.417         |
+| 0.2625 | 18100 | 7.3861        |
+| 0.2639 | 18200 | 7.4313        |
+| 0.2654 | 18300 | 7.4198        |
+| 0.2668 | 18400 | 7.4177        |
+| 0.2683 | 18500 | 7.4113        |
+| 0.2697 | 18600 | 7.4214        |
+| 0.2712 | 18700 | 7.3925        |
+| 0.2726 | 18800 | 7.4241        |
+| 0.2741 | 18900 | 7.3627        |
+| 0.2755 | 19000 | 7.3991        |
+| 0.2770 | 19100 | 7.3401        |
+| 0.2784 | 19200 | 7.3592        |
+| 0.2799 | 19300 | 7.3619        |
+| 0.2813 | 19400 | 7.3968        |
+| 0.2828 | 19500 | 7.3659        |
+| 0.2842 | 19600 | 7.3553        |
+| 0.2857 | 19700 | 7.4121        |
+| 0.2871 | 19800 | 7.3858        |
+| 0.2886 | 19900 | 7.4223        |
+| 0.2900 | 20000 | 7.3699        |
+| 0.2915 | 20100 | 7.3452        |
+| 0.2929 | 20200 | 7.3673        |
+| 0.2944 | 20300 | 7.3568        |
+| 0.2958 | 20400 | 7.3753        |
+| 0.2973 | 20500 | 7.375         |
+| 0.2987 | 20600 | 7.3699        |
+| 0.3002 | 20700 | 7.3537        |
+| 0.3016 | 20800 | 7.3739        |
+| 0.3031 | 20900 | 7.3218        |
+| 0.3045 | 21000 | 7.3739        |
+| 0.3060 | 21100 | 7.3781        |
+| 0.3074 | 21200 | 7.3724        |
+| 0.3089 | 21300 | 7.3396        |
+| 0.3103 | 21400 | 7.3074        |
+| 0.3118 | 21500 | 7.3135        |
+| 0.3132 | 21600 | 7.4059        |
+| 0.3147 | 21700 | 7.3887        |
+| 0.3161 | 21800 | 7.2713        |
+| 0.3176 | 21900 | 7.3444        |
+| 0.3190 | 22000 | 7.3375        |
+| 0.3205 | 22100 | 7.3426        |
+| 0.3219 | 22200 | 7.3319        |
+| 0.3234 | 22300 | 7.3323        |
+| 0.3248 | 22400 | 7.3345        |
+| 0.3263 | 22500 | 7.2884        |
+| 0.3277 | 22600 | 7.285         |
+| 0.3292 | 22700 | 7.3242        |
+| 0.3306 | 22800 | 7.3198        |
+| 0.3321 | 22900 | 7.2734        |
+| 0.3335 | 23000 | 7.3444        |
+| 0.3350 | 23100 | 7.3301        |
+| 0.3364 | 23200 | 7.3302        |
+| 0.3379 | 23300 | 7.3194        |
+| 0.3393 | 23400 | 7.3118        |
+| 0.3408 | 23500 | 7.3143        |
+| 0.3422 | 23600 | 7.3177        |
+| 0.3437 | 23700 | 7.3174        |
+| 0.3451 | 23800 | 7.3054        |
+| 0.3466 | 23900 | 7.3186        |
+| 0.3480 | 24000 | 7.3204        |
+| 0.3495 | 24100 | 7.3106        |
+| 0.3509 | 24200 | 7.2729        |
+| 0.3524 | 24300 | 7.2944        |
+| 0.3538 | 24400 | 7.3143        |
+| 0.3553 | 24500 | 7.3126        |
+| 0.3567 | 24600 | 7.3428        |
+| 0.3582 | 24700 | 7.3465        |
+| 0.3596 | 24800 | 7.2775        |
+| 0.3611 | 24900 | 7.3395        |
+| 0.3625 | 25000 | 7.2664        |
+| 0.3640 | 25100 | 7.3029        |
+| 0.3654 | 25200 | 7.2996        |
+| 0.3669 | 25300 | 7.297         |
+| 0.3683 | 25400 | 7.2545        |
+| 0.3698 | 25500 | 7.3155        |
+| 0.3712 | 25600 | 7.3197        |
+| 0.3727 | 25700 | 7.2667        |
+| 0.3741 | 25800 | 7.2578        |
+| 0.3756 | 25900 | 7.2993        |
+| 0.3770 | 26000 | 7.2528        |
+| 0.3785 | 26100 | 7.3429        |
+| 0.3799 | 26200 | 7.3111        |
+| 0.3814 | 26300 | 7.3344        |
+| 0.3828 | 26400 | 7.2869        |
+| 0.3843 | 26500 | 7.2636        |
+| 0.3857 | 26600 | 7.3047        |
+| 0.3872 | 26700 | 7.2681        |
+| 0.3886 | 26800 | 7.297         |
+| 0.3901 | 26900 | 7.2075        |
+| 0.3915 | 27000 | 7.2792        |
+| 0.3930 | 27100 | 7.2952        |
+| 0.3944 | 27200 | 7.3172        |
+| 0.3959 | 27300 | 7.3063        |
+| 0.3973 | 27400 | 7.2786        |
+| 0.3988 | 27500 | 7.2072        |
+| 0.4002 | 27600 | 7.2589        |
+| 0.4017 | 27700 | 7.2975        |
+| 0.4031 | 27800 | 7.2228        |
+| 0.4046 | 27900 | 7.2548        |
+| 0.4061 | 28000 | 7.2572        |
+| 0.4075 | 28100 | 7.2664        |
+| 0.4090 | 28200 | 7.2913        |
+| 0.4104 | 28300 | 7.3064        |
+| 0.4119 | 28400 | 7.2726        |
+| 0.4133 | 28500 | 7.2588        |
+| 0.4148 | 28600 | 7.2715        |
+| 0.4162 | 28700 | 7.275         |
+| 0.4177 | 28800 | 7.2364        |
+| 0.4191 | 28900 | 7.2308        |
+| 0.4206 | 29000 | 7.3067        |
+| 0.4220 | 29100 | 7.2369        |
+| 0.4235 | 29200 | 7.2519        |
+| 0.4249 | 29300 | 7.2358        |
+| 0.4264 | 29400 | 7.244         |
+| 0.4278 | 29500 | 7.2531        |
+| 0.4293 | 29600 | 7.2268        |
+| 0.4307 | 29700 | 7.2785        |
+| 0.4322 | 29800 | 7.2429        |
+| 0.4336 | 29900 | 7.2478        |
+| 0.4351 | 30000 | 7.22          |
+| 0.4365 | 30100 | 7.2125        |
+| 0.4380 | 30200 | 7.2839        |
+| 0.4394 | 30300 | 7.323         |
+| 0.4409 | 30400 | 7.269         |
+| 0.4423 | 30500 | 7.2373        |
+| 0.4438 | 30600 | 7.3115        |
+| 0.4452 | 30700 | 7.2345        |
+| 0.4467 | 30800 | 7.2022        |
+| 0.4481 | 30900 | 7.2235        |
+| 0.4496 | 31000 | 7.207         |
+| 0.4510 | 31100 | 7.2684        |
+| 0.4525 | 31200 | 7.2309        |
+| 0.4539 | 31300 | 7.2201        |
+| 0.4554 | 31400 | 7.2645        |
+| 0.4568 | 31500 | 7.2171        |
+| 0.4583 | 31600 | 7.1958        |
+| 0.4597 | 31700 | 7.2728        |
+| 0.4612 | 31800 | 7.2175        |
+| 0.4626 | 31900 | 7.1995        |
+| 0.4641 | 32000 | 7.1677        |
+| 0.4655 | 32100 | 7.2124        |
+| 0.4670 | 32200 | 7.2163        |
+| 0.4684 | 32300 | 7.1728        |
+| 0.4699 | 32400 | 7.1941        |
+| 0.4713 | 32500 | 7.1826        |
+| 0.4728 | 32600 | 7.2542        |
+| 0.4742 | 32700 | 7.1866        |
+| 0.4757 | 32800 | 7.2511        |
+| 0.4771 | 32900 | 7.2587        |
+| 0.4786 | 33000 | 7.1963        |
+| 0.4800 | 33100 | 7.213         |
+| 0.4815 | 33200 | 7.2072        |
+| 0.4829 | 33300 | 7.3           |
+| 0.4844 | 33400 | 7.1509        |
+| 0.4858 | 33500 | 7.1924        |
+| 0.4873 | 33600 | 7.2083        |
+| 0.4887 | 33700 | 7.2112        |
+| 0.4902 | 33800 | 7.2109        |
+| 0.4916 | 33900 | 7.1624        |
+| 0.4931 | 34000 | 7.1807        |
+| 0.4945 | 34100 | 7.1922        |
+| 0.4960 | 34200 | 7.2215        |
+| 0.4974 | 34300 | 7.2036        |
+| 0.4989 | 34400 | 7.204         |
+| 0.5003 | 34500 | 7.1716        |
+| 0.5018 | 34600 | 7.2463        |
+| 0.5032 | 34700 | 7.2446        |
+| 0.5047 | 34800 | 7.2203        |
+| 0.5061 | 34900 | 7.2153        |
+| 0.5076 | 35000 | 7.153         |
+| 0.5090 | 35100 | 7.2332        |
+| 0.5105 | 35200 | 7.1656        |
+| 0.5119 | 35300 | 7.142         |
+| 0.5134 | 35400 | 7.2018        |
+| 0.5148 | 35500 | 7.177         |
+| 0.5163 | 35600 | 7.1974        |
+| 0.5177 | 35700 | 7.2767        |
+| 0.5192 | 35800 | 7.2503        |
+| 0.5206 | 35900 | 7.1468        |
+| 0.5221 | 36000 | 7.1862        |
+| 0.5235 | 36100 | 7.2403        |
+| 0.5250 | 36200 | 7.2375        |
+| 0.5264 | 36300 | 7.1832        |
+| 0.5279 | 36400 | 7.1365        |
+| 0.5293 | 36500 | 7.2009        |
+| 0.5308 | 36600 | 7.2533        |
+| 0.5322 | 36700 | 7.2221        |
+| 0.5337 | 36800 | 7.2076        |
+| 0.5351 | 36900 | 7.185         |
+| 0.5366 | 37000 | 7.2257        |
+| 0.5380 | 37100 | 7.1446        |
+| 0.5395 | 37200 | 7.2148        |
+| 0.5409 | 37300 | 7.1769        |
+| 0.5424 | 37400 | 7.1636        |
+| 0.5438 | 37500 | 7.2075        |
+| 0.5453 | 37600 | 7.2295        |
+| 0.5467 | 37700 | 7.1181        |
+| 0.5482 | 37800 | 7.1449        |
+| 0.5496 | 37900 | 7.1355        |
+| 0.5511 | 38000 | 7.1549        |
+| 0.5525 | 38100 | 7.1616        |
+| 0.5540 | 38200 | 7.1747        |
+| 0.5554 | 38300 | 7.1871        |
+| 0.5569 | 38400 | 7.1817        |
+| 0.5583 | 38500 | 7.1495        |
+| 0.5598 | 38600 | 7.2552        |
+| 0.5612 | 38700 | 7.1916        |
+| 0.5627 | 38800 | 7.226         |
+| 0.5641 | 38900 | 7.1136        |
+| 0.5656 | 39000 | 7.1555        |
+| 0.5670 | 39100 | 7.1272        |
+| 0.5685 | 39200 | 7.1998        |
+| 0.5699 | 39300 | 7.1242        |
+| 0.5714 | 39400 | 7.2167        |
+| 0.5728 | 39500 | 7.1848        |
+| 0.5743 | 39600 | 7.178         |
+| 0.5757 | 39700 | 7.1347        |
+| 0.5772 | 39800 | 7.1059        |
+| 0.5786 | 39900 | 7.1004        |
+| 0.5801 | 40000 | 7.1096        |
+| 0.5815 | 40100 | 7.1272        |
+| 0.5830 | 40200 | 7.2099        |
+| 0.5844 | 40300 | 7.158         |
+| 0.5859 | 40400 | 7.0955        |
+| 0.5873 | 40500 | 7.1366        |
+| 0.5888 | 40600 | 7.12          |
+| 0.5902 | 40700 | 7.1694        |
+| 0.5917 | 40800 | 7.1576        |
+| 0.5931 | 40900 | 7.2108        |
+| 0.5946 | 41000 | 7.112         |
+| 0.5960 | 41100 | 7.184         |
+| 0.5975 | 41200 | 7.1528        |
+| 0.5989 | 41300 | 7.1737        |
+| 0.6004 | 41400 | 7.2168        |
+| 0.6018 | 41500 | 7.1676        |
+| 0.6033 | 41600 | 7.17          |
+| 0.6047 | 41700 | 7.077         |
+| 0.6062 | 41800 | 7.1684        |
+| 0.6076 | 41900 | 7.1425        |
+| 0.6091 | 42000 | 7.1449        |
+| 0.6105 | 42100 | 7.1325        |
+| 0.6120 | 42200 | 7.195         |
+| 0.6134 | 42300 | 7.159         |
+| 0.6149 | 42400 | 7.179         |
+| 0.6163 | 42500 | 7.1657        |
+| 0.6178 | 42600 | 7.19          |
+| 0.6192 | 42700 | 7.0956        |
+| 0.6207 | 42800 | 7.0952        |
+| 0.6221 | 42900 | 7.1905        |
+| 0.6236 | 43000 | 7.1203        |
+| 0.6250 | 43100 | 7.1293        |
+| 0.6265 | 43200 | 7.1273        |
+| 0.6279 | 43300 | 7.1374        |
+| 0.6294 | 43400 | 7.1003        |
+| 0.6308 | 43500 | 7.1644        |
+| 0.6323 | 43600 | 7.1432        |
+| 0.6337 | 43700 | 7.1237        |
+| 0.6352 | 43800 | 7.1663        |
+| 0.6366 | 43900 | 7.1404        |
+| 0.6381 | 44000 | 7.1664        |
+| 0.6395 | 44100 | 7.1906        |
+| 0.6410 | 44200 | 7.1707        |
+| 0.6424 | 44300 | 7.0727        |
+| 0.6439 | 44400 | 7.1768        |
+| 0.6453 | 44500 | 7.117         |
+| 0.6468 | 44600 | 7.0683        |
+| 0.6482 | 44700 | 7.0952        |
+| 0.6497 | 44800 | 7.1768        |
+| 0.6511 | 44900 | 7.1327        |
+| 0.6526 | 45000 | 7.0887        |
+| 0.6540 | 45100 | 7.1047        |
+| 0.6555 | 45200 | 7.1983        |
+| 0.6569 | 45300 | 7.0902        |
+| 0.6584 | 45400 | 7.1647        |
+| 0.6598 | 45500 | 7.1453        |
+| 0.6613 | 45600 | 7.1043        |
+| 0.6627 | 45700 | 7.0729        |
+| 0.6642 | 45800 | 7.1508        |
+| 0.6656 | 45900 | 7.1688        |
+| 0.6671 | 46000 | 7.1259        |
+| 0.6685 | 46100 | 7.1323        |
+| 0.6700 | 46200 | 7.0916        |
+| 0.6714 | 46300 | 7.1297        |
+| 0.6729 | 46400 | 7.1138        |
+| 0.6743 | 46500 | 7.129         |
+| 0.6758 | 46600 | 7.1228        |
+| 0.6772 | 46700 | 7.0995        |
+| 0.6787 | 46800 | 7.1034        |
+| 0.6801 | 46900 | 7.1085        |
+| 0.6816 | 47000 | 7.1223        |
+| 0.6830 | 47100 | 7.0934        |
+| 0.6845 | 47200 | 7.0865        |
+| 0.6859 | 47300 | 7.076         |
+| 0.6874 | 47400 | 7.1252        |
+| 0.6888 | 47500 | 7.1788        |
+| 0.6903 | 47600 | 7.0807        |
+| 0.6917 | 47700 | 7.1163        |
+| 0.6932 | 47800 | 7.0631        |
+| 0.6946 | 47900 | 7.1842        |
+| 0.6961 | 48000 | 7.1084        |
+| 0.6975 | 48100 | 7.0994        |
+| 0.6990 | 48200 | 7.1163        |
+| 0.7004 | 48300 | 7.0563        |
+| 0.7019 | 48400 | 7.1086        |
+| 0.7033 | 48500 | 7.1233        |
+| 0.7048 | 48600 | 7.0724        |
+| 0.7062 | 48700 | 7.095         |
+| 0.7077 | 48800 | 7.1039        |
+| 0.7091 | 48900 | 7.1138        |
+| 0.7106 | 49000 | 7.1184        |
+| 0.7120 | 49100 | 7.0557        |
+| 0.7135 | 49200 | 7.1065        |
+| 0.7149 | 49300 | 7.1455        |
+| 0.7164 | 49400 | 7.0808        |
+| 0.7178 | 49500 | 7.1734        |
+| 0.7193 | 49600 | 7.1396        |
+| 0.7207 | 49700 | 7.1184        |
+| 0.7222 | 49800 | 7.0714        |
+| 0.7236 | 49900 | 7.1617        |
+| 0.7251 | 50000 | 7.1244        |
+| 0.7265 | 50100 | 7.0367        |
+| 0.7280 | 50200 | 7.1068        |
+| 0.7294 | 50300 | 7.1152        |
+| 0.7309 | 50400 | 7.0782        |
+| 0.7323 | 50500 | 7.0937        |
+| 0.7338 | 50600 | 7.0899        |
+| 0.7352 | 50700 | 7.0212        |
+| 0.7367 | 50800 | 7.0308        |
+| 0.7381 | 50900 | 7.0298        |
+| 0.7396 | 51000 | 7.094         |
+| 0.7410 | 51100 | 7.1962        |
+| 0.7425 | 51200 | 7.0881        |
+| 0.7439 | 51300 | 7.0484        |
+| 0.7454 | 51400 | 7.0536        |
+| 0.7468 | 51500 | 7.1169        |
+| 0.7483 | 51600 | 7.0975        |
+| 0.7497 | 51700 | 7.1155        |
+| 0.7512 | 51800 | 7.0659        |
+| 0.7526 | 51900 | 7.0567        |
+| 0.7541 | 52000 | 7.0942        |
+| 0.7555 | 52100 | 7.1224        |
+| 0.7570 | 52200 | 7.1349        |
+| 0.7584 | 52300 | 7.0874        |
+| 0.7599 | 52400 | 7.0636        |
+| 0.7613 | 52500 | 7.1171        |
+| 0.7628 | 52600 | 7.1359        |
+| 0.7642 | 52700 | 7.1599        |
+| 0.7657 | 52800 | 7.0568        |
+| 0.7671 | 52900 | 7.0948        |
+| 0.7686 | 53000 | 7.0982        |
+| 0.7700 | 53100 | 7.0856        |
+| 0.7715 | 53200 | 7.071         |
+| 0.7729 | 53300 | 7.0562        |
+| 0.7744 | 53400 | 7.066         |
+| 0.7758 | 53500 | 7.119         |
+| 0.7773 | 53600 | 7.1492        |
+| 0.7787 | 53700 | 7.0461        |
+| 0.7802 | 53800 | 7.1003        |
+| 0.7816 | 53900 | 7.0984        |
+| 0.7831 | 54000 | 7.0875        |
+| 0.7845 | 54100 | 7.1238        |
+| 0.7860 | 54200 | 7.0896        |
+| 0.7874 | 54300 | 7.0569        |
+| 0.7889 | 54400 | 7.0994        |
+| 0.7903 | 54500 | 7.146         |
+| 0.7918 | 54600 | 7.0819        |
+| 0.7932 | 54700 | 7.1003        |
+| 0.7947 | 54800 | 7.0744        |
+| 0.7961 | 54900 | 7.1047        |
+| 0.7976 | 55000 | 7.1147        |
+| 0.7990 | 55100 | 7.0318        |
+| 0.8005 | 55200 | 7.0548        |
+| 0.8019 | 55300 | 7.1215        |
+| 0.8034 | 55400 | 7.1124        |
+| 0.8048 | 55500 | 7.1096        |
+| 0.8063 | 55600 | 7.0467        |
+| 0.8077 | 55700 | 7.0454        |
+| 0.8092 | 55800 | 7.0824        |
+| 0.8107 | 55900 | 7.075         |
+| 0.8121 | 56000 | 7.0316        |
+| 0.8136 | 56100 | 7.1231        |
+| 0.8150 | 56200 | 7.0449        |
+| 0.8165 | 56300 | 7.1556        |
+| 0.8179 | 56400 | 7.1036        |
+| 0.8194 | 56500 | 6.9778        |
+| 0.8208 | 56600 | 7.0649        |
+| 0.8223 | 56700 | 7.1124        |
+| 0.8237 | 56800 | 7.0294        |
+| 0.8252 | 56900 | 7.1424        |
+| 0.8266 | 57000 | 7.0891        |
+| 0.8281 | 57100 | 7.0899        |
+| 0.8295 | 57200 | 7.1276        |
+| 0.8310 | 57300 | 7.0621        |
+| 0.8324 | 57400 | 7.0253        |
+| 0.8339 | 57500 | 7.0532        |
+| 0.8353 | 57600 | 7.0171        |
+| 0.8368 | 57700 | 7.0731        |
+| 0.8382 | 57800 | 7.0837        |
+| 0.8397 | 57900 | 7.0778        |
+| 0.8411 | 58000 | 7.0478        |
+| 0.8426 | 58100 | 7.0946        |
+| 0.8440 | 58200 | 7.0772        |
+| 0.8455 | 58300 | 7.0556        |
+| 0.8469 | 58400 | 7.0451        |
+| 0.8484 | 58500 | 7.0708        |
+| 0.8498 | 58600 | 7.0881        |
+| 0.8513 | 58700 | 7.0783        |
+| 0.8527 | 58800 | 7.0632        |
+| 0.8542 | 58900 | 7.0175        |
+| 0.8556 | 59000 | 7.0248        |
+| 0.8571 | 59100 | 7.0781        |
+| 0.8585 | 59200 | 7.0403        |
+| 0.8600 | 59300 | 7.1009        |
+| 0.8614 | 59400 | 7.0367        |
+| 0.8629 | 59500 | 7.0728        |
+| 0.8643 | 59600 | 7.0292        |
+| 0.8658 | 59700 | 7.027         |
+| 0.8672 | 59800 | 7.0737        |
+| 0.8687 | 59900 | 7.0514        |
+| 0.8701 | 60000 | 7.033         |
+| 0.8716 | 60100 | 6.9728        |
+| 0.8730 | 60200 | 7.0672        |
+| 0.8745 | 60300 | 7.1248        |
+| 0.8759 | 60400 | 7.1127        |
+| 0.8774 | 60500 | 7.0061        |
+| 0.8788 | 60600 | 7.0643        |
+| 0.8803 | 60700 | 7.0996        |
+| 0.8817 | 60800 | 7.0991        |
+| 0.8832 | 60900 | 7.05          |
+| 0.8846 | 61000 | 7.0198        |
+| 0.8861 | 61100 | 7.0489        |
+| 0.8875 | 61200 | 7.0778        |
+| 0.8890 | 61300 | 7.1017        |
+| 0.8904 | 61400 | 7.0815        |
+| 0.8919 | 61500 | 7.0565        |
+| 0.8933 | 61600 | 7.0187        |
+| 0.8948 | 61700 | 7.0809        |
+| 0.8962 | 61800 | 7.0219        |
+| 0.8977 | 61900 | 7.0343        |
+| 0.8991 | 62000 | 7.1052        |
+| 0.9006 | 62100 | 7.0448        |
+| 0.9020 | 62200 | 7.0598        |
+| 0.9035 | 62300 | 7.0498        |
+| 0.9049 | 62400 | 7.1148        |
+| 0.9064 | 62500 | 7.048         |
+| 0.9078 | 62600 | 7.1137        |
+| 0.9093 | 62700 | 7.0538        |
+| 0.9107 | 62800 | 7.089         |
+| 0.9122 | 62900 | 7.1247        |
+| 0.9136 | 63000 | 6.9895        |
+| 0.9151 | 63100 | 7.0364        |
+| 0.9165 | 63200 | 7.1219        |
+| 0.9180 | 63300 | 7.0399        |
+| 0.9194 | 63400 | 7.0669        |
+| 0.9209 | 63500 | 7.1429        |
+| 0.9223 | 63600 | 7.1196        |
+| 0.9238 | 63700 | 7.0336        |
+| 0.9252 | 63800 | 7.0792        |
+| 0.9267 | 63900 | 7.0935        |
+| 0.9281 | 64000 | 7.1163        |
+| 0.9296 | 64100 | 7.0483        |
+| 0.9310 | 64200 | 7.0632        |
+| 0.9325 | 64300 | 6.9877        |
+| 0.9339 | 64400 | 6.9545        |
+| 0.9354 | 64500 | 7.047         |
+| 0.9368 | 64600 | 7.0755        |
+| 0.9383 | 64700 | 6.9931        |
+| 0.9397 | 64800 | 7.0           |
+| 0.9412 | 64900 | 7.0293        |
+| 0.9426 | 65000 | 7.0471        |
+| 0.9441 | 65100 | 7.0567        |
+| 0.9455 | 65200 | 7.0853        |
+| 0.9470 | 65300 | 7.0801        |
+| 0.9484 | 65400 | 7.0202        |
+| 0.9499 | 65500 | 7.1004        |
+| 0.9513 | 65600 | 7.1051        |
+| 0.9528 | 65700 | 7.0403        |
+| 0.9542 | 65800 | 7.003         |
+| 0.9557 | 65900 | 7.0495        |
+| 0.9571 | 66000 | 6.9828        |
+| 0.9586 | 66100 | 7.0453        |
+| 0.9600 | 66200 | 7.0196        |
+| 0.9615 | 66300 | 7.1254        |
+| 0.9629 | 66400 | 7.0849        |
+| 0.9644 | 66500 | 7.0223        |
+| 0.9658 | 66600 | 7.1217        |
+| 0.9673 | 66700 | 6.9922        |
+| 0.9687 | 66800 | 7.025         |
+| 0.9702 | 66900 | 7.0705        |
+| 0.9716 | 67000 | 7.0794        |
+| 0.9731 | 67100 | 7.0162        |
+| 0.9745 | 67200 | 7.0855        |
+| 0.9760 | 67300 | 7.0078        |
+| 0.9774 | 67400 | 7.0908        |
+| 0.9789 | 67500 | 7.0663        |
+| 0.9803 | 67600 | 7.0175        |
+| 0.9818 | 67700 | 7.0059        |
+| 0.9832 | 67800 | 7.0673        |
+| 0.9847 | 67900 | 7.0495        |
+| 0.9861 | 68000 | 7.005         |
+| 0.9876 | 68100 | 7.0198        |
+| 0.9890 | 68200 | 7.0515        |
+| 0.9905 | 68300 | 7.0274        |
+| 0.9919 | 68400 | 7.1286        |
+| 0.9934 | 68500 | 7.0913        |
+| 0.9948 | 68600 | 7.0087        |
+| 0.9963 | 68700 | 7.0341        |
+| 0.9977 | 68800 | 7.046         |
+| 0.9992 | 68900 | 7.057         |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.4
+- PyTorch: 2.5.1+cu121
+- 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": "2.5.1+cu121"
+  },
+  "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 sha256:282fe7cac0910cc8b8cc74c9483049472df91ca3ff461fa8af35287e2ddbc9c8
+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]"
+}