Add new SentenceTransformer model

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 +1,2103 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:9020
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: python multiprocessing show cpu count
+  sentences:
+  - "def unique(seq):\n    \"\"\"Return the unique elements of a collection even if\
+    \ those elements are\n       unhashable and unsortable, like dicts and sets\"\"\
+    \"\n    cleaned = []\n    for each in seq:\n        if each not in cleaned:\n\
+    \            cleaned.append(each)\n    return cleaned"
+  - "def is_in(self, point_x, point_y):\n        \"\"\" Test if a point is within\
+    \ this polygonal region \"\"\"\n\n        point_array = array(((point_x, point_y),))\n\
+    \        vertices = array(self.points)\n        winding = self.inside_rule ==\
+    \ \"winding\"\n        result = points_in_polygon(point_array, vertices, winding)\n\
+    \        return result[0]"
+  - "def machine_info():\n    \"\"\"Retrieve core and memory information for the current\
+    \ machine.\n    \"\"\"\n    import psutil\n    BYTES_IN_GIG = 1073741824.0\n \
+    \   free_bytes = psutil.virtual_memory().total\n    return [{\"memory\": float(\"\
+    %.1f\" % (free_bytes / BYTES_IN_GIG)), \"cores\": multiprocessing.cpu_count(),\n\
+    \             \"name\": socket.gethostname()}]"
+- source_sentence: python subplot set the whole title
+  sentences:
+  - "def set_title(self, title, **kwargs):\n        \"\"\"Sets the title on the underlying\
+    \ matplotlib AxesSubplot.\"\"\"\n        ax = self.get_axes()\n        ax.set_title(title,\
+    \ **kwargs)"
+  - "def moving_average(array, n=3):\n    \"\"\"\n    Calculates the moving average\
+    \ of an array.\n\n    Parameters\n    ----------\n    array : array\n        The\
+    \ array to have the moving average taken of\n    n : int\n        The number of\
+    \ points of moving average to take\n    \n    Returns\n    -------\n    MovingAverageArray\
+    \ : array\n        The n-point moving average of the input array\n    \"\"\"\n\
+    \    ret = _np.cumsum(array, dtype=float)\n    ret[n:] = ret[n:] - ret[:-n]\n\
+    \    return ret[n - 1:] / n"
+  - "def to_query_parameters(parameters):\n    \"\"\"Converts DB-API parameter values\
+    \ into query parameters.\n\n    :type parameters: Mapping[str, Any] or Sequence[Any]\n\
+    \    :param parameters: A dictionary or sequence of query parameter values.\n\n\
+    \    :rtype: List[google.cloud.bigquery.query._AbstractQueryParameter]\n    :returns:\
+    \ A list of query parameters.\n    \"\"\"\n    if parameters is None:\n      \
+    \  return []\n\n    if isinstance(parameters, collections_abc.Mapping):\n    \
+    \    return to_query_parameters_dict(parameters)\n\n    return to_query_parameters_list(parameters)"
+- source_sentence: python merge two set to dict
+  sentences:
+  - "def make_regex(separator):\n    \"\"\"Utility function to create regexp for matching\
+    \ escaped separators\n    in strings.\n\n    \"\"\"\n    return re.compile(r'(?:'\
+    \ + re.escape(separator) + r')?((?:[^' +\n                      re.escape(separator)\
+    \ + r'\\\\]|\\\\.)+)')"
+  - "def csvtolist(inputstr):\n    \"\"\" converts a csv string into a list \"\"\"\
+    \n    reader = csv.reader([inputstr], skipinitialspace=True)\n    output = []\n\
+    \    for r in reader:\n        output += r\n    return output"
+  - "def dict_merge(set1, set2):\n    \"\"\"Joins two dictionaries.\"\"\"\n    return\
+    \ dict(list(set1.items()) + list(set2.items()))"
+- source_sentence: python string % substitution float
+  sentences:
+  - "def _configure_logger():\n    \"\"\"Configure the logging module.\"\"\"\n   \
+    \ if not app.debug:\n        _configure_logger_for_production(logging.getLogger())\n\
+    \    elif not app.testing:\n        _configure_logger_for_debugging(logging.getLogger())"
+  - "def __set__(self, instance, value):\n        \"\"\" Set a related object for\
+    \ an instance. \"\"\"\n\n        self.map[id(instance)] = (weakref.ref(instance),\
+    \ value)"
+  - "def format_float(value): # not used\n    \"\"\"Modified form of the 'g' format\
+    \ specifier.\n    \"\"\"\n    string = \"{:g}\".format(value).replace(\"e+\",\
+    \ \"e\")\n    string = re.sub(\"e(-?)0*(\\d+)\", r\"e\\1\\2\", string)\n    return\
+    \ string"
+- source_sentence: bottom 5 rows in python
+  sentences:
+  - "def refresh(self, document):\n\t\t\"\"\" Load a new copy of a document from the\
+    \ database.  does not\n\t\t\treplace the old one \"\"\"\n\t\ttry:\n\t\t\told_cache_size\
+    \ = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\
+    \t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\
+    \t\treturn obj"
+  - "def table_top_abs(self):\n        \"\"\"Returns the absolute position of table\
+    \ top\"\"\"\n        table_height = np.array([0, 0, self.table_full_size[2]])\n\
+    \        return string_to_array(self.floor.get(\"pos\")) + table_height"
+  - "def get_dimension_array(array):\n    \"\"\"\n    Get dimension of an array getting\
+    \ the number of rows and the max num of\n    columns.\n    \"\"\"\n    if all(isinstance(el,\
+    \ list) for el in array):\n        result = [len(array), len(max([x for x in array],\
+    \ key=len,))]\n\n    # elif array and isinstance(array, list):\n    else:\n  \
+    \      result = [len(array), 1]\n\n    return result"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 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("Devy1/MiniLM-cosqa-16")
+# Run inference
+sentences = [
+    'bottom 5 rows in python',
+    'def table_top_abs(self):\n        """Returns the absolute position of table top"""\n        table_height = np.array([0, 0, self.table_full_size[2]])\n        return string_to_array(self.floor.get("pos")) + table_height',
+    'def refresh(self, document):\n\t\t""" Load a new copy of a document from the database.  does not\n\t\t\treplace the old one """\n\t\ttry:\n\t\t\told_cache_size = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\t\treturn obj',
+]
+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,  0.5117, -0.0480],
+#         [ 0.5117,  1.0000, -0.0416],
+#         [-0.0480, -0.0416,  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
+
+#### Unnamed Dataset
+
+* Size: 9,020 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                            |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 9.67 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 86.17 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | anchor                                                  | positive                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>1d array in char datatype in python</code>        | <code>def _convert_to_array(array_like, dtype):<br>        """<br>        Convert Matrix attributes which are array-like or buffer to array.<br>        """<br>        if isinstance(array_like, bytes):<br>            return np.frombuffer(array_like, dtype=dtype)<br>        return np.asarray(array_like, dtype=dtype)</code>                                                                                            |
+  | <code>python condition non none</code>                  | <code>def _not(condition=None, **kwargs):<br>    """<br>    Return the opposite of input condition.<br><br>    :param condition: condition to process.<br><br>    :result: not condition.<br>    :rtype: bool<br>    """<br><br>    result = True<br><br>    if condition is not None:<br>        result = not run(condition, **kwargs)<br><br>    return result</code>                                                       |
+  | <code>accessing a column from a matrix in python</code> | <code>def get_column(self, X, column):<br>        """Return a column of the given matrix.<br><br>        Args:<br>            X: `numpy.ndarray` or `pandas.DataFrame`.<br>            column: `int` or `str`.<br><br>        Returns:<br>            np.ndarray: Selected column.<br>        """<br>        if isinstance(X, pd.DataFrame):<br>            return X[column].values<br><br>        return X[:, column]</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-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`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: 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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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 |
+|:------:|:----:|:-------------:|
+| 0.0018 | 1    | 0.2852        |
+| 0.0035 | 2    | 0.3503        |
+| 0.0053 | 3    | 0.0422        |
+| 0.0071 | 4    | 0.1306        |
+| 0.0089 | 5    | 0.1997        |
+| 0.0106 | 6    | 0.0307        |
+| 0.0124 | 7    | 0.1194        |
+| 0.0142 | 8    | 0.114         |
+| 0.0160 | 9    | 0.0128        |
+| 0.0177 | 10   | 0.0523        |
+| 0.0195 | 11   | 0.0228        |
+| 0.0213 | 12   | 0.056         |
+| 0.0230 | 13   | 0.2108        |
+| 0.0248 | 14   | 0.0856        |
+| 0.0266 | 15   | 0.058         |
+| 0.0284 | 16   | 0.0311        |
+| 0.0301 | 17   | 0.015         |
+| 0.0319 | 18   | 0.0098        |
+| 0.0337 | 19   | 0.3063        |
+| 0.0355 | 20   | 0.0124        |
+| 0.0372 | 21   | 0.0151        |
+| 0.0390 | 22   | 0.2788        |
+| 0.0408 | 23   | 0.0228        |
+| 0.0426 | 24   | 0.0634        |
+| 0.0443 | 25   | 0.0482        |
+| 0.0461 | 26   | 0.0647        |
+| 0.0479 | 27   | 0.0045        |
+| 0.0496 | 28   | 0.0315        |
+| 0.0514 | 29   | 0.0316        |
+| 0.0532 | 30   | 0.1654        |
+| 0.0550 | 31   | 0.1931        |
+| 0.0567 | 32   | 0.0157        |
+| 0.0585 | 33   | 0.286         |
+| 0.0603 | 34   | 0.1894        |
+| 0.0621 | 35   | 0.0308        |
+| 0.0638 | 36   | 0.0181        |
+| 0.0656 | 37   | 0.126         |
+| 0.0674 | 38   | 0.0258        |
+| 0.0691 | 39   | 0.0669        |
+| 0.0709 | 40   | 0.0979        |
+| 0.0727 | 41   | 0.1078        |
+| 0.0745 | 42   | 0.3883        |
+| 0.0762 | 43   | 0.0341        |
+| 0.0780 | 44   | 0.0439        |
+| 0.0798 | 45   | 0.0733        |
+| 0.0816 | 46   | 0.399         |
+| 0.0833 | 47   | 0.1246        |
+| 0.0851 | 48   | 0.0095        |
+| 0.0869 | 49   | 0.3253        |
+| 0.0887 | 50   | 0.0405        |
+| 0.0904 | 51   | 0.1117        |
+| 0.0922 | 52   | 0.0389        |
+| 0.0940 | 53   | 0.1124        |
+| 0.0957 | 54   | 0.118         |
+| 0.0975 | 55   | 0.2116        |
+| 0.0993 | 56   | 0.0721        |
+| 0.1011 | 57   | 0.1326        |
+| 0.1028 | 58   | 0.1217        |
+| 0.1046 | 59   | 0.0216        |
+| 0.1064 | 60   | 0.0798        |
+| 0.1082 | 61   | 0.1676        |
+| 0.1099 | 62   | 0.0314        |
+| 0.1117 | 63   | 0.045         |
+| 0.1135 | 64   | 0.0325        |
+| 0.1152 | 65   | 0.0624        |
+| 0.1170 | 66   | 0.0282        |
+| 0.1188 | 67   | 0.0164        |
+| 0.1206 | 68   | 0.0632        |
+| 0.1223 | 69   | 0.1402        |
+| 0.1241 | 70   | 0.0271        |
+| 0.1259 | 71   | 0.0449        |
+| 0.1277 | 72   | 0.0107        |
+| 0.1294 | 73   | 0.0531        |
+| 0.1312 | 74   | 0.0489        |
+| 0.1330 | 75   | 0.1134        |
+| 0.1348 | 76   | 0.0657        |
+| 0.1365 | 77   | 0.0383        |
+| 0.1383 | 78   | 0.0288        |
+| 0.1401 | 79   | 0.0514        |
+| 0.1418 | 80   | 0.0173        |
+| 0.1436 | 81   | 0.1886        |
+| 0.1454 | 82   | 0.0532        |
+| 0.1472 | 83   | 0.0024        |
+| 0.1489 | 84   | 0.0076        |
+| 0.1507 | 85   | 0.1116        |
+| 0.1525 | 86   | 0.0089        |
+| 0.1543 | 87   | 0.4592        |
+| 0.1560 | 88   | 0.0552        |
+| 0.1578 | 89   | 0.0327        |
+| 0.1596 | 90   | 0.1102        |
+| 0.1613 | 91   | 0.0077        |
+| 0.1631 | 92   | 0.0048        |
+| 0.1649 | 93   | 0.003         |
+| 0.1667 | 94   | 0.0042        |
+| 0.1684 | 95   | 0.1047        |
+| 0.1702 | 96   | 0.0212        |
+| 0.1720 | 97   | 0.0287        |
+| 0.1738 | 98   | 0.0233        |
+| 0.1755 | 99   | 0.0319        |
+| 0.1773 | 100  | 0.0206        |
+| 0.1791 | 101  | 0.018         |
+| 0.1809 | 102  | 0.059         |
+| 0.1826 | 103  | 0.172         |
+| 0.1844 | 104  | 0.1555        |
+| 0.1862 | 105  | 0.0479        |
+| 0.1879 | 106  | 0.0336        |
+| 0.1897 | 107  | 0.0889        |
+| 0.1915 | 108  | 0.0094        |
+| 0.1933 | 109  | 0.053         |
+| 0.1950 | 110  | 0.0944        |
+| 0.1968 | 111  | 0.2731        |
+| 0.1986 | 112  | 0.0315        |
+| 0.2004 | 113  | 0.162         |
+| 0.2021 | 114  | 0.1024        |
+| 0.2039 | 115  | 0.013         |
+| 0.2057 | 116  | 0.0998        |
+| 0.2074 | 117  | 0.0035        |
+| 0.2092 | 118  | 0.0035        |
+| 0.2110 | 119  | 0.136         |
+| 0.2128 | 120  | 0.0626        |
+| 0.2145 | 121  | 0.0597        |
+| 0.2163 | 122  | 0.1202        |
+| 0.2181 | 123  | 0.1017        |
+| 0.2199 | 124  | 0.0241        |
+| 0.2216 | 125  | 0.0527        |
+| 0.2234 | 126  | 0.0158        |
+| 0.2252 | 127  | 0.005         |
+| 0.2270 | 128  | 0.3728        |
+| 0.2287 | 129  | 0.0049        |
+| 0.2305 | 130  | 0.0426        |
+| 0.2323 | 131  | 0.1093        |
+| 0.2340 | 132  | 0.0607        |
+| 0.2358 | 133  | 0.0387        |
+| 0.2376 | 134  | 0.0672        |
+| 0.2394 | 135  | 0.0187        |
+| 0.2411 | 136  | 0.1737        |
+| 0.2429 | 137  | 0.042         |
+| 0.2447 | 138  | 0.0934        |
+| 0.2465 | 139  | 0.0135        |
+| 0.2482 | 140  | 0.1649        |
+| 0.25   | 141  | 0.1029        |
+| 0.2518 | 142  | 0.0183        |
+| 0.2535 | 143  | 0.1689        |
+| 0.2553 | 144  | 0.6752        |
+| 0.2571 | 145  | 0.076         |
+| 0.2589 | 146  | 0.0961        |
+| 0.2606 | 147  | 0.127         |
+| 0.2624 | 148  | 0.1866        |
+| 0.2642 | 149  | 0.0652        |
+| 0.2660 | 150  | 0.029         |
+| 0.2677 | 151  | 0.0175        |
+| 0.2695 | 152  | 0.0034        |
+| 0.2713 | 153  | 0.2149        |
+| 0.2730 | 154  | 0.0564        |
+| 0.2748 | 155  | 0.0205        |
+| 0.2766 | 156  | 0.0193        |
+| 0.2784 | 157  | 0.1054        |
+| 0.2801 | 158  | 0.0209        |
+| 0.2819 | 159  | 0.1948        |
+| 0.2837 | 160  | 0.0176        |
+| 0.2855 | 161  | 0.1101        |
+| 0.2872 | 162  | 0.003         |
+| 0.2890 | 163  | 0.0373        |
+| 0.2908 | 164  | 0.1793        |
+| 0.2926 | 165  | 0.0878        |
+| 0.2943 | 166  | 0.0346        |
+| 0.2961 | 167  | 0.0051        |
+| 0.2979 | 168  | 0.2891        |
+| 0.2996 | 169  | 0.2409        |
+| 0.3014 | 170  | 0.0056        |
+| 0.3032 | 171  | 0.0051        |
+| 0.3050 | 172  | 0.1651        |
+| 0.3067 | 173  | 0.0802        |
+| 0.3085 | 174  | 0.1191        |
+| 0.3103 | 175  | 0.0453        |
+| 0.3121 | 176  | 0.0972        |
+| 0.3138 | 177  | 0.0157        |
+| 0.3156 | 178  | 0.0339        |
+| 0.3174 | 179  | 0.0759        |
+| 0.3191 | 180  | 0.196         |
+| 0.3209 | 181  | 0.1043        |
+| 0.3227 | 182  | 0.0603        |
+| 0.3245 | 183  | 0.0163        |
+| 0.3262 | 184  | 0.0115        |
+| 0.3280 | 185  | 0.1027        |
+| 0.3298 | 186  | 0.0726        |
+| 0.3316 | 187  | 0.089         |
+| 0.3333 | 188  | 0.0385        |
+| 0.3351 | 189  | 0.0082        |
+| 0.3369 | 190  | 0.1135        |
+| 0.3387 | 191  | 0.074         |
+| 0.3404 | 192  | 0.1149        |
+| 0.3422 | 193  | 0.1642        |
+| 0.3440 | 194  | 0.0166        |
+| 0.3457 | 195  | 0.0105        |
+| 0.3475 | 196  | 0.0313        |
+| 0.3493 | 197  | 0.1255        |
+| 0.3511 | 198  | 0.0471        |
+| 0.3528 | 199  | 0.067         |
+| 0.3546 | 200  | 0.0227        |
+| 0.3564 | 201  | 0.1239        |
+| 0.3582 | 202  | 0.237         |
+| 0.3599 | 203  | 0.0141        |
+| 0.3617 | 204  | 0.0077        |
+| 0.3635 | 205  | 0.0073        |
+| 0.3652 | 206  | 0.0417        |
+| 0.3670 | 207  | 0.0297        |
+| 0.3688 | 208  | 0.0752        |
+| 0.3706 | 209  | 0.0155        |
+| 0.3723 | 210  | 0.0536        |
+| 0.3741 | 211  | 0.0034        |
+| 0.3759 | 212  | 0.0273        |
+| 0.3777 | 213  | 0.2597        |
+| 0.3794 | 214  | 0.0574        |
+| 0.3812 | 215  | 0.0554        |
+| 0.3830 | 216  | 0.0806        |
+| 0.3848 | 217  | 0.018         |
+| 0.3865 | 218  | 0.215         |
+| 0.3883 | 219  | 0.0527        |
+| 0.3901 | 220  | 0.0025        |
+| 0.3918 | 221  | 0.0459        |
+| 0.3936 | 222  | 0.0074        |
+| 0.3954 | 223  | 0.0603        |
+| 0.3972 | 224  | 0.0092        |
+| 0.3989 | 225  | 0.0832        |
+| 0.4007 | 226  | 0.0144        |
+| 0.4025 | 227  | 0.1483        |
+| 0.4043 | 228  | 0.4177        |
+| 0.4060 | 229  | 0.0061        |
+| 0.4078 | 230  | 0.0034        |
+| 0.4096 | 231  | 0.0917        |
+| 0.4113 | 232  | 0.0039        |
+| 0.4131 | 233  | 0.0369        |
+| 0.4149 | 234  | 0.0619        |
+| 0.4167 | 235  | 0.1598        |
+| 0.4184 | 236  | 0.0699        |
+| 0.4202 | 237  | 0.0641        |
+| 0.4220 | 238  | 0.0162        |
+| 0.4238 | 239  | 0.1175        |
+| 0.4255 | 240  | 0.0043        |
+| 0.4273 | 241  | 0.0171        |
+| 0.4291 | 242  | 0.005         |
+| 0.4309 | 243  | 0.169         |
+| 0.4326 | 244  | 0.0124        |
+| 0.4344 | 245  | 0.1141        |
+| 0.4362 | 246  | 0.0467        |
+| 0.4379 | 247  | 0.0074        |
+| 0.4397 | 248  | 0.2058        |
+| 0.4415 | 249  | 0.0186        |
+| 0.4433 | 250  | 0.0112        |
+| 0.4450 | 251  | 0.2977        |
+| 0.4468 | 252  | 0.0384        |
+| 0.4486 | 253  | 0.1525        |
+| 0.4504 | 254  | 0.2781        |
+| 0.4521 | 255  | 0.1463        |
+| 0.4539 | 256  | 0.1352        |
+| 0.4557 | 257  | 0.0789        |
+| 0.4574 | 258  | 0.013         |
+| 0.4592 | 259  | 0.2722        |
+| 0.4610 | 260  | 0.0701        |
+| 0.4628 | 261  | 0.036         |
+| 0.4645 | 262  | 0.0363        |
+| 0.4663 | 263  | 0.1835        |
+| 0.4681 | 264  | 0.2061        |
+| 0.4699 | 265  | 0.0639        |
+| 0.4716 | 266  | 0.0007        |
+| 0.4734 | 267  | 0.0107        |
+| 0.4752 | 268  | 0.1097        |
+| 0.4770 | 269  | 0.2531        |
+| 0.4787 | 270  | 0.0205        |
+| 0.4805 | 271  | 0.1076        |
+| 0.4823 | 272  | 0.0621        |
+| 0.4840 | 273  | 0.0065        |
+| 0.4858 | 274  | 0.0444        |
+| 0.4876 | 275  | 0.0613        |
+| 0.4894 | 276  | 0.0373        |
+| 0.4911 | 277  | 0.4446        |
+| 0.4929 | 278  | 0.071         |
+| 0.4947 | 279  | 0.0839        |
+| 0.4965 | 280  | 0.2712        |
+| 0.4982 | 281  | 0.3855        |
+| 0.5    | 282  | 0.02          |
+| 0.5018 | 283  | 0.1209        |
+| 0.5035 | 284  | 0.0428        |
+| 0.5053 | 285  | 0.0859        |
+| 0.5071 | 286  | 0.0076        |
+| 0.5089 | 287  | 0.0137        |
+| 0.5106 | 288  | 0.1124        |
+| 0.5124 | 289  | 0.2544        |
+| 0.5142 | 290  | 0.0029        |
+| 0.5160 | 291  | 0.0142        |
+| 0.5177 | 292  | 0.0709        |
+| 0.5195 | 293  | 0.0418        |
+| 0.5213 | 294  | 0.1344        |
+| 0.5230 | 295  | 0.0105        |
+| 0.5248 | 296  | 0.1553        |
+| 0.5266 | 297  | 0.0281        |
+| 0.5284 | 298  | 0.0122        |
+| 0.5301 | 299  | 0.0383        |
+| 0.5319 | 300  | 0.2396        |
+| 0.5337 | 301  | 0.1094        |
+| 0.5355 | 302  | 0.0929        |
+| 0.5372 | 303  | 0.0312        |
+| 0.5390 | 304  | 0.068         |
+| 0.5408 | 305  | 0.0128        |
+| 0.5426 | 306  | 0.127         |
+| 0.5443 | 307  | 0.0414        |
+| 0.5461 | 308  | 0.1497        |
+| 0.5479 | 309  | 0.041         |
+| 0.5496 | 310  | 0.0288        |
+| 0.5514 | 311  | 0.0479        |
+| 0.5532 | 312  | 0.0204        |
+| 0.5550 | 313  | 0.0828        |
+| 0.5567 | 314  | 0.0149        |
+| 0.5585 | 315  | 0.1651        |
+| 0.5603 | 316  | 0.0982        |
+| 0.5621 | 317  | 0.0118        |
+| 0.5638 | 318  | 0.1905        |
+| 0.5656 | 319  | 0.0074        |
+| 0.5674 | 320  | 0.1277        |
+| 0.5691 | 321  | 0.0336        |
+| 0.5709 | 322  | 0.037         |
+| 0.5727 | 323  | 0.0228        |
+| 0.5745 | 324  | 0.5044        |
+| 0.5762 | 325  | 0.2475        |
+| 0.5780 | 326  | 0.0389        |
+| 0.5798 | 327  | 0.0035        |
+| 0.5816 | 328  | 0.0812        |
+| 0.5833 | 329  | 0.1005        |
+| 0.5851 | 330  | 0.3384        |
+| 0.5869 | 331  | 0.0345        |
+| 0.5887 | 332  | 0.0903        |
+| 0.5904 | 333  | 0.0144        |
+| 0.5922 | 334  | 0.0853        |
+| 0.5940 | 335  | 0.1661        |
+| 0.5957 | 336  | 0.0339        |
+| 0.5975 | 337  | 0.0749        |
+| 0.5993 | 338  | 0.2761        |
+| 0.6011 | 339  | 0.0036        |
+| 0.6028 | 340  | 0.0843        |
+| 0.6046 | 341  | 0.0963        |
+| 0.6064 | 342  | 0.0261        |
+| 0.6082 | 343  | 0.0305        |
+| 0.6099 | 344  | 0.0076        |
+| 0.6117 | 345  | 0.006         |
+| 0.6135 | 346  | 0.0034        |
+| 0.6152 | 347  | 0.0278        |
+| 0.6170 | 348  | 0.01          |
+| 0.6188 | 349  | 0.0059        |
+| 0.6206 | 350  | 0.0663        |
+| 0.6223 | 351  | 0.0198        |
+| 0.6241 | 352  | 0.0134        |
+| 0.6259 | 353  | 0.123         |
+| 0.6277 | 354  | 0.0899        |
+| 0.6294 | 355  | 0.0943        |
+| 0.6312 | 356  | 0.011         |
+| 0.6330 | 357  | 0.1238        |
+| 0.6348 | 358  | 0.0283        |
+| 0.6365 | 359  | 0.0248        |
+| 0.6383 | 360  | 0.0365        |
+| 0.6401 | 361  | 0.0349        |
+| 0.6418 | 362  | 0.0183        |
+| 0.6436 | 363  | 0.0106        |
+| 0.6454 | 364  | 0.0523        |
+| 0.6472 | 365  | 0.1742        |
+| 0.6489 | 366  | 0.1366        |
+| 0.6507 | 367  | 0.2887        |
+| 0.6525 | 368  | 0.0802        |
+| 0.6543 | 369  | 0.0532        |
+| 0.6560 | 370  | 0.1194        |
+| 0.6578 | 371  | 0.0648        |
+| 0.6596 | 372  | 0.1022        |
+| 0.6613 | 373  | 0.0596        |
+| 0.6631 | 374  | 0.1083        |
+| 0.6649 | 375  | 0.0121        |
+| 0.6667 | 376  | 0.0448        |
+| 0.6684 | 377  | 0.0261        |
+| 0.6702 | 378  | 0.1448        |
+| 0.6720 | 379  | 0.0822        |
+| 0.6738 | 380  | 0.0141        |
+| 0.6755 | 381  | 0.0187        |
+| 0.6773 | 382  | 0.0639        |
+| 0.6791 | 383  | 0.3279        |
+| 0.6809 | 384  | 0.0084        |
+| 0.6826 | 385  | 0.0256        |
+| 0.6844 | 386  | 0.0886        |
+| 0.6862 | 387  | 0.0671        |
+| 0.6879 | 388  | 0.0365        |
+| 0.6897 | 389  | 0.0112        |
+| 0.6915 | 390  | 0.018         |
+| 0.6933 | 391  | 0.2417        |
+| 0.6950 | 392  | 0.1742        |
+| 0.6968 | 393  | 0.0083        |
+| 0.6986 | 394  | 0.0202        |
+| 0.7004 | 395  | 0.0371        |
+| 0.7021 | 396  | 0.0249        |
+| 0.7039 | 397  | 0.019         |
+| 0.7057 | 398  | 0.0546        |
+| 0.7074 | 399  | 0.0287        |
+| 0.7092 | 400  | 0.0234        |
+| 0.7110 | 401  | 0.005         |
+| 0.7128 | 402  | 0.0089        |
+| 0.7145 | 403  | 0.0097        |
+| 0.7163 | 404  | 0.0545        |
+| 0.7181 | 405  | 0.0079        |
+| 0.7199 | 406  | 0.2158        |
+| 0.7216 | 407  | 0.0216        |
+| 0.7234 | 408  | 0.0033        |
+| 0.7252 | 409  | 0.0313        |
+| 0.7270 | 410  | 0.0527        |
+| 0.7287 | 411  | 0.1268        |
+| 0.7305 | 412  | 0.0025        |
+| 0.7323 | 413  | 0.0597        |
+| 0.7340 | 414  | 0.0291        |
+| 0.7358 | 415  | 0.0219        |
+| 0.7376 | 416  | 0.0818        |
+| 0.7394 | 417  | 0.1946        |
+| 0.7411 | 418  | 0.5806        |
+| 0.7429 | 419  | 0.0348        |
+| 0.7447 | 420  | 0.0138        |
+| 0.7465 | 421  | 0.0445        |
+| 0.7482 | 422  | 0.639         |
+| 0.75   | 423  | 0.028         |
+| 0.7518 | 424  | 0.1737        |
+| 0.7535 | 425  | 0.0038        |
+| 0.7553 | 426  | 0.014         |
+| 0.7571 | 427  | 0.1141        |
+| 0.7589 | 428  | 0.0936        |
+| 0.7606 | 429  | 0.0724        |
+| 0.7624 | 430  | 0.0438        |
+| 0.7642 | 431  | 0.0044        |
+| 0.7660 | 432  | 0.003         |
+| 0.7677 | 433  | 0.0147        |
+| 0.7695 | 434  | 0.1538        |
+| 0.7713 | 435  | 0.0203        |
+| 0.7730 | 436  | 0.0223        |
+| 0.7748 | 437  | 0.0056        |
+| 0.7766 | 438  | 0.0114        |
+| 0.7784 | 439  | 0.0097        |
+| 0.7801 | 440  | 0.0169        |
+| 0.7819 | 441  | 0.0453        |
+| 0.7837 | 442  | 0.1687        |
+| 0.7855 | 443  | 0.1222        |
+| 0.7872 | 444  | 0.0091        |
+| 0.7890 | 445  | 0.0155        |
+| 0.7908 | 446  | 0.1198        |
+| 0.7926 | 447  | 0.0922        |
+| 0.7943 | 448  | 0.017         |
+| 0.7961 | 449  | 0.0853        |
+| 0.7979 | 450  | 0.0946        |
+| 0.7996 | 451  | 0.0558        |
+| 0.8014 | 452  | 0.0229        |
+| 0.8032 | 453  | 0.0062        |
+| 0.8050 | 454  | 0.0175        |
+| 0.8067 | 455  | 0.0339        |
+| 0.8085 | 456  | 0.0445        |
+| 0.8103 | 457  | 0.0411        |
+| 0.8121 | 458  | 0.0037        |
+| 0.8138 | 459  | 0.0244        |
+| 0.8156 | 460  | 0.0358        |
+| 0.8174 | 461  | 0.062         |
+| 0.8191 | 462  | 0.0201        |
+| 0.8209 | 463  | 0.0055        |
+| 0.8227 | 464  | 0.152         |
+| 0.8245 | 465  | 0.0032        |
+| 0.8262 | 466  | 0.2056        |
+| 0.8280 | 467  | 0.0245        |
+| 0.8298 | 468  | 0.0239        |
+| 0.8316 | 469  | 0.0323        |
+| 0.8333 | 470  | 0.2737        |
+| 0.8351 | 471  | 0.0205        |
+| 0.8369 | 472  | 0.0037        |
+| 0.8387 | 473  | 0.2092        |
+| 0.8404 | 474  | 0.0659        |
+| 0.8422 | 475  | 0.0361        |
+| 0.8440 | 476  | 0.0845        |
+| 0.8457 | 477  | 0.015         |
+| 0.8475 | 478  | 0.0055        |
+| 0.8493 | 479  | 0.0012        |
+| 0.8511 | 480  | 0.0241        |
+| 0.8528 | 481  | 0.1986        |
+| 0.8546 | 482  | 0.1794        |
+| 0.8564 | 483  | 0.0477        |
+| 0.8582 | 484  | 0.1216        |
+| 0.8599 | 485  | 0.0423        |
+| 0.8617 | 486  | 0.0124        |
+| 0.8635 | 487  | 0.0724        |
+| 0.8652 | 488  | 0.3665        |
+| 0.8670 | 489  | 0.0338        |
+| 0.8688 | 490  | 0.0327        |
+| 0.8706 | 491  | 0.0875        |
+| 0.8723 | 492  | 0.1198        |
+| 0.8741 | 493  | 0.0959        |
+| 0.8759 | 494  | 0.4752        |
+| 0.8777 | 495  | 0.0248        |
+| 0.8794 | 496  | 0.0955        |
+| 0.8812 | 497  | 0.0988        |
+| 0.8830 | 498  | 0.0053        |
+| 0.8848 | 499  | 0.2546        |
+| 0.8865 | 500  | 0.2137        |
+| 0.8883 | 501  | 0.0013        |
+| 0.8901 | 502  | 0.0053        |
+| 0.8918 | 503  | 0.0021        |
+| 0.8936 | 504  | 0.0357        |
+| 0.8954 | 505  | 0.1408        |
+| 0.8972 | 506  | 0.0475        |
+| 0.8989 | 507  | 0.0041        |
+| 0.9007 | 508  | 0.1138        |
+| 0.9025 | 509  | 0.1568        |
+| 0.9043 | 510  | 0.0094        |
+| 0.9060 | 511  | 0.0015        |
+| 0.9078 | 512  | 0.028         |
+| 0.9096 | 513  | 0.2884        |
+| 0.9113 | 514  | 0.0929        |
+| 0.9131 | 515  | 0.2441        |
+| 0.9149 | 516  | 0.0067        |
+| 0.9167 | 517  | 0.0327        |
+| 0.9184 | 518  | 0.029         |
+| 0.9202 | 519  | 0.0835        |
+| 0.9220 | 520  | 0.006         |
+| 0.9238 | 521  | 0.0103        |
+| 0.9255 | 522  | 0.1339        |
+| 0.9273 | 523  | 0.0084        |
+| 0.9291 | 524  | 0.0101        |
+| 0.9309 | 525  | 0.0053        |
+| 0.9326 | 526  | 0.0236        |
+| 0.9344 | 527  | 0.0927        |
+| 0.9362 | 528  | 0.0636        |
+| 0.9379 | 529  | 0.1854        |
+| 0.9397 | 530  | 0.117         |
+| 0.9415 | 531  | 0.0115        |
+| 0.9433 | 532  | 0.1472        |
+| 0.9450 | 533  | 0.0226        |
+| 0.9468 | 534  | 0.0531        |
+| 0.9486 | 535  | 0.0272        |
+| 0.9504 | 536  | 0.0213        |
+| 0.9521 | 537  | 0.008         |
+| 0.9539 | 538  | 0.0244        |
+| 0.9557 | 539  | 0.0061        |
+| 0.9574 | 540  | 0.0987        |
+| 0.9592 | 541  | 0.021         |
+| 0.9610 | 542  | 0.0556        |
+| 0.9628 | 543  | 0.0214        |
+| 0.9645 | 544  | 0.1886        |
+| 0.9663 | 545  | 0.1871        |
+| 0.9681 | 546  | 0.1497        |
+| 0.9699 | 547  | 0.2943        |
+| 0.9716 | 548  | 0.0207        |
+| 0.9734 | 549  | 0.0032        |
+| 0.9752 | 550  | 0.066         |
+| 0.9770 | 551  | 0.0986        |
+| 0.9787 | 552  | 0.0255        |
+| 0.9805 | 553  | 0.1584        |
+| 0.9823 | 554  | 0.0939        |
+| 0.9840 | 555  | 0.0543        |
+| 0.9858 | 556  | 0.0293        |
+| 0.9876 | 557  | 0.1172        |
+| 0.9894 | 558  | 0.0345        |
+| 0.9911 | 559  | 0.0188        |
+| 0.9929 | 560  | 0.0108        |
+| 0.9947 | 561  | 0.0069        |
+| 0.9965 | 562  | 0.0965        |
+| 0.9982 | 563  | 0.1211        |
+| 1.0    | 564  | 0.0011        |
+| 1.0018 | 565  | 0.002         |
+| 1.0035 | 566  | 0.0409        |
+| 1.0053 | 567  | 0.0062        |
+| 1.0071 | 568  | 0.0074        |
+| 1.0089 | 569  | 0.0012        |
+| 1.0106 | 570  | 0.0454        |
+| 1.0124 | 571  | 0.0017        |
+| 1.0142 | 572  | 0.0727        |
+| 1.0160 | 573  | 0.0096        |
+| 1.0177 | 574  | 0.1944        |
+| 1.0195 | 575  | 0.0129        |
+| 1.0213 | 576  | 0.0077        |
+| 1.0230 | 577  | 0.0203        |
+| 1.0248 | 578  | 0.046         |
+| 1.0266 | 579  | 0.0011        |
+| 1.0284 | 580  | 0.0014        |
+| 1.0301 | 581  | 0.002         |
+| 1.0319 | 582  | 0.0362        |
+| 1.0337 | 583  | 0.0023        |
+| 1.0355 | 584  | 0.0055        |
+| 1.0372 | 585  | 0.1081        |
+| 1.0390 | 586  | 0.1659        |
+| 1.0408 | 587  | 0.012         |
+| 1.0426 | 588  | 0.0225        |
+| 1.0443 | 589  | 0.1943        |
+| 1.0461 | 590  | 0.0045        |
+| 1.0479 | 591  | 0.0024        |
+| 1.0496 | 592  | 0.1368        |
+| 1.0514 | 593  | 0.0895        |
+| 1.0532 | 594  | 0.2384        |
+| 1.0550 | 595  | 0.0842        |
+| 1.0567 | 596  | 0.0669        |
+| 1.0585 | 597  | 0.0039        |
+| 1.0603 | 598  | 0.0031        |
+| 1.0621 | 599  | 0.0044        |
+| 1.0638 | 600  | 0.1103        |
+| 1.0656 | 601  | 0.0232        |
+| 1.0674 | 602  | 0.0644        |
+| 1.0691 | 603  | 0.0104        |
+| 1.0709 | 604  | 0.0383        |
+| 1.0727 | 605  | 0.1454        |
+| 1.0745 | 606  | 0.0123        |
+| 1.0762 | 607  | 0.0094        |
+| 1.0780 | 608  | 0.0247        |
+| 1.0798 | 609  | 0.0473        |
+| 1.0816 | 610  | 0.0212        |
+| 1.0833 | 611  | 0.0506        |
+| 1.0851 | 612  | 0.0854        |
+| 1.0869 | 613  | 0.021         |
+| 1.0887 | 614  | 0.012         |
+| 1.0904 | 615  | 0.012         |
+| 1.0922 | 616  | 0.1787        |
+| 1.0940 | 617  | 0.0229        |
+| 1.0957 | 618  | 0.0123        |
+| 1.0975 | 619  | 0.0381        |
+| 1.0993 | 620  | 0.1896        |
+| 1.1011 | 621  | 0.1764        |
+| 1.1028 | 622  | 0.0046        |
+| 1.1046 | 623  | 0.0075        |
+| 1.1064 | 624  | 0.013         |
+| 1.1082 | 625  | 0.0592        |
+| 1.1099 | 626  | 0.0127        |
+| 1.1117 | 627  | 0.0952        |
+| 1.1135 | 628  | 0.0051        |
+| 1.1152 | 629  | 0.1906        |
+| 1.1170 | 630  | 0.0105        |
+| 1.1188 | 631  | 0.0526        |
+| 1.1206 | 632  | 0.1145        |
+| 1.1223 | 633  | 0.0086        |
+| 1.1241 | 634  | 0.0669        |
+| 1.1259 | 635  | 0.0183        |
+| 1.1277 | 636  | 0.0424        |
+| 1.1294 | 637  | 0.0444        |
+| 1.1312 | 638  | 0.0085        |
+| 1.1330 | 639  | 0.0057        |
+| 1.1348 | 640  | 0.0067        |
+| 1.1365 | 641  | 0.0007        |
+| 1.1383 | 642  | 0.0052        |
+| 1.1401 | 643  | 0.0066        |
+| 1.1418 | 644  | 0.0005        |
+| 1.1436 | 645  | 0.0011        |
+| 1.1454 | 646  | 0.0872        |
+| 1.1472 | 647  | 0.0125        |
+| 1.1489 | 648  | 0.0985        |
+| 1.1507 | 649  | 0.0628        |
+| 1.1525 | 650  | 0.0313        |
+| 1.1543 | 651  | 0.0083        |
+| 1.1560 | 652  | 0.0379        |
+| 1.1578 | 653  | 0.0314        |
+| 1.1596 | 654  | 0.0029        |
+| 1.1613 | 655  | 0.0078        |
+| 1.1631 | 656  | 0.1272        |
+| 1.1649 | 657  | 0.0167        |
+| 1.1667 | 658  | 0.12          |
+| 1.1684 | 659  | 0.0224        |
+| 1.1702 | 660  | 0.0193        |
+| 1.1720 | 661  | 0.0104        |
+| 1.1738 | 662  | 0.022         |
+| 1.1755 | 663  | 0.1915        |
+| 1.1773 | 664  | 0.0466        |
+| 1.1791 | 665  | 0.024         |
+| 1.1809 | 666  | 0.0385        |
+| 1.1826 | 667  | 0.0914        |
+| 1.1844 | 668  | 0.0364        |
+| 1.1862 | 669  | 0.0165        |
+| 1.1879 | 670  | 0.003         |
+| 1.1897 | 671  | 0.0111        |
+| 1.1915 | 672  | 0.0097        |
+| 1.1933 | 673  | 0.0354        |
+| 1.1950 | 674  | 0.0496        |
+| 1.1968 | 675  | 0.0767        |
+| 1.1986 | 676  | 0.0138        |
+| 1.2004 | 677  | 0.0441        |
+| 1.2021 | 678  | 0.0036        |
+| 1.2039 | 679  | 0.0078        |
+| 1.2057 | 680  | 0.0104        |
+| 1.2074 | 681  | 0.0121        |
+| 1.2092 | 682  | 0.1018        |
+| 1.2110 | 683  | 0.0146        |
+| 1.2128 | 684  | 0.0025        |
+| 1.2145 | 685  | 0.0145        |
+| 1.2163 | 686  | 0.0205        |
+| 1.2181 | 687  | 0.124         |
+| 1.2199 | 688  | 0.0165        |
+| 1.2216 | 689  | 0.1345        |
+| 1.2234 | 690  | 0.0104        |
+| 1.2252 | 691  | 0.0056        |
+| 1.2270 | 692  | 0.001         |
+| 1.2287 | 693  | 0.0047        |
+| 1.2305 | 694  | 0.0218        |
+| 1.2323 | 695  | 0.0161        |
+| 1.2340 | 696  | 0.0163        |
+| 1.2358 | 697  | 0.0214        |
+| 1.2376 | 698  | 0.0059        |
+| 1.2394 | 699  | 0.001         |
+| 1.2411 | 700  | 0.0069        |
+| 1.2429 | 701  | 0.0011        |
+| 1.2447 | 702  | 0.0345        |
+| 1.2465 | 703  | 0.0061        |
+| 1.2482 | 704  | 0.1855        |
+| 1.25   | 705  | 0.0193        |
+| 1.2518 | 706  | 0.0076        |
+| 1.2535 | 707  | 0.1165        |
+| 1.2553 | 708  | 0.0278        |
+| 1.2571 | 709  | 0.0039        |
+| 1.2589 | 710  | 0.0241        |
+| 1.2606 | 711  | 0.0419        |
+| 1.2624 | 712  | 0.0079        |
+| 1.2642 | 713  | 0.0148        |
+| 1.2660 | 714  | 0.0333        |
+| 1.2677 | 715  | 0.0133        |
+| 1.2695 | 716  | 0.2561        |
+| 1.2713 | 717  | 0.0353        |
+| 1.2730 | 718  | 0.0035        |
+| 1.2748 | 719  | 0.0142        |
+| 1.2766 | 720  | 0.0843        |
+| 1.2784 | 721  | 0.0074        |
+| 1.2801 | 722  | 0.0117        |
+| 1.2819 | 723  | 0.014         |
+| 1.2837 | 724  | 0.0197        |
+| 1.2855 | 725  | 0.0235        |
+| 1.2872 | 726  | 0.0243        |
+| 1.2890 | 727  | 0.0023        |
+| 1.2908 | 728  | 0.0048        |
+| 1.2926 | 729  | 0.056         |
+| 1.2943 | 730  | 0.0517        |
+| 1.2961 | 731  | 0.0073        |
+| 1.2979 | 732  | 0.2383        |
+| 1.2996 | 733  | 0.0165        |
+| 1.3014 | 734  | 0.0703        |
+| 1.3032 | 735  | 0.0091        |
+| 1.3050 | 736  | 0.0447        |
+| 1.3067 | 737  | 0.0504        |
+| 1.3085 | 738  | 0.0279        |
+| 1.3103 | 739  | 0.257         |
+| 1.3121 | 740  | 0.0372        |
+| 1.3138 | 741  | 0.0111        |
+| 1.3156 | 742  | 0.0229        |
+| 1.3174 | 743  | 0.062         |
+| 1.3191 | 744  | 0.0186        |
+| 1.3209 | 745  | 0.05          |
+| 1.3227 | 746  | 0.0029        |
+| 1.3245 | 747  | 0.0355        |
+| 1.3262 | 748  | 0.097         |
+| 1.3280 | 749  | 0.1409        |
+| 1.3298 | 750  | 0.0811        |
+| 1.3316 | 751  | 0.0475        |
+| 1.3333 | 752  | 0.0023        |
+| 1.3351 | 753  | 0.0034        |
+| 1.3369 | 754  | 0.0022        |
+| 1.3387 | 755  | 0.0307        |
+| 1.3404 | 756  | 0.1478        |
+| 1.3422 | 757  | 0.0311        |
+| 1.3440 | 758  | 0.0016        |
+| 1.3457 | 759  | 0.018         |
+| 1.3475 | 760  | 0.0024        |
+| 1.3493 | 761  | 0.0067        |
+| 1.3511 | 762  | 0.0209        |
+| 1.3528 | 763  | 0.0405        |
+| 1.3546 | 764  | 0.093         |
+| 1.3564 | 765  | 0.0069        |
+| 1.3582 | 766  | 0.0552        |
+| 1.3599 | 767  | 0.011         |
+| 1.3617 | 768  | 0.0035        |
+| 1.3635 | 769  | 0.014         |
+| 1.3652 | 770  | 0.0235        |
+| 1.3670 | 771  | 0.0304        |
+| 1.3688 | 772  | 0.019         |
+| 1.3706 | 773  | 0.0307        |
+| 1.3723 | 774  | 0.0089        |
+| 1.3741 | 775  | 0.0035        |
+| 1.3759 | 776  | 0.0021        |
+| 1.3777 | 777  | 0.0014        |
+| 1.3794 | 778  | 0.0068        |
+| 1.3812 | 779  | 0.0065        |
+| 1.3830 | 780  | 0.0176        |
+| 1.3848 | 781  | 0.0297        |
+| 1.3865 | 782  | 0.0025        |
+| 1.3883 | 783  | 0.0102        |
+| 1.3901 | 784  | 0.0141        |
+| 1.3918 | 785  | 0.0854        |
+| 1.3936 | 786  | 0.0044        |
+| 1.3954 | 787  | 0.0287        |
+| 1.3972 | 788  | 0.0145        |
+| 1.3989 | 789  | 0.0055        |
+| 1.4007 | 790  | 0.0121        |
+| 1.4025 | 791  | 0.0038        |
+| 1.4043 | 792  | 0.1916        |
+| 1.4060 | 793  | 0.0804        |
+| 1.4078 | 794  | 0.1413        |
+| 1.4096 | 795  | 0.0272        |
+| 1.4113 | 796  | 0.0349        |
+| 1.4131 | 797  | 0.0203        |
+| 1.4149 | 798  | 0.0053        |
+| 1.4167 | 799  | 0.0008        |
+| 1.4184 | 800  | 0.0259        |
+| 1.4202 | 801  | 0.0209        |
+| 1.4220 | 802  | 0.1249        |
+| 1.4238 | 803  | 0.4471        |
+| 1.4255 | 804  | 0.012         |
+| 1.4273 | 805  | 0.1615        |
+| 1.4291 | 806  | 0.0353        |
+| 1.4309 | 807  | 0.0295        |
+| 1.4326 | 808  | 0.0089        |
+| 1.4344 | 809  | 0.0033        |
+| 1.4362 | 810  | 0.0012        |
+| 1.4379 | 811  | 0.0091        |
+| 1.4397 | 812  | 0.0327        |
+| 1.4415 | 813  | 0.0829        |
+| 1.4433 | 814  | 0.1153        |
+| 1.4450 | 815  | 0.013         |
+| 1.4468 | 816  | 0.041         |
+| 1.4486 | 817  | 0.003         |
+| 1.4504 | 818  | 0.2116        |
+| 1.4521 | 819  | 0.0278        |
+| 1.4539 | 820  | 0.0026        |
+| 1.4557 | 821  | 0.1155        |
+| 1.4574 | 822  | 0.0901        |
+| 1.4592 | 823  | 0.0081        |
+| 1.4610 | 824  | 0.0013        |
+| 1.4628 | 825  | 0.0867        |
+| 1.4645 | 826  | 0.0798        |
+| 1.4663 | 827  | 0.0015        |
+| 1.4681 | 828  | 0.0025        |
+| 1.4699 | 829  | 0.0063        |
+| 1.4716 | 830  | 0.0102        |
+| 1.4734 | 831  | 0.0041        |
+| 1.4752 | 832  | 0.021         |
+| 1.4770 | 833  | 0.0392        |
+| 1.4787 | 834  | 0.0058        |
+| 1.4805 | 835  | 0.0086        |
+| 1.4823 | 836  | 0.0084        |
+| 1.4840 | 837  | 0.0568        |
+| 1.4858 | 838  | 0.0127        |
+| 1.4876 | 839  | 0.0653        |
+| 1.4894 | 840  | 0.0042        |
+| 1.4911 | 841  | 0.0164        |
+| 1.4929 | 842  | 0.026         |
+| 1.4947 | 843  | 0.0515        |
+| 1.4965 | 844  | 0.0074        |
+| 1.4982 | 845  | 0.0254        |
+| 1.5    | 846  | 0.0906        |
+| 1.5018 | 847  | 0.0311        |
+| 1.5035 | 848  | 0.0096        |
+| 1.5053 | 849  | 0.0909        |
+| 1.5071 | 850  | 0.0124        |
+| 1.5089 | 851  | 0.0373        |
+| 1.5106 | 852  | 0.001         |
+| 1.5124 | 853  | 0.0202        |
+| 1.5142 | 854  | 0.1159        |
+| 1.5160 | 855  | 0.0006        |
+| 1.5177 | 856  | 0.0211        |
+| 1.5195 | 857  | 0.0173        |
+| 1.5213 | 858  | 0.0029        |
+| 1.5230 | 859  | 0.0107        |
+| 1.5248 | 860  | 0.0249        |
+| 1.5266 | 861  | 0.0071        |
+| 1.5284 | 862  | 0.0392        |
+| 1.5301 | 863  | 0.0051        |
+| 1.5319 | 864  | 0.0157        |
+| 1.5337 | 865  | 0.2098        |
+| 1.5355 | 866  | 0.1102        |
+| 1.5372 | 867  | 0.0141        |
+| 1.5390 | 868  | 0.0158        |
+| 1.5408 | 869  | 0.0014        |
+| 1.5426 | 870  | 0.0045        |
+| 1.5443 | 871  | 0.0085        |
+| 1.5461 | 872  | 0.0184        |
+| 1.5479 | 873  | 0.0147        |
+| 1.5496 | 874  | 0.0018        |
+| 1.5514 | 875  | 0.0235        |
+| 1.5532 | 876  | 0.0464        |
+| 1.5550 | 877  | 0.0249        |
+| 1.5567 | 878  | 0.0027        |
+| 1.5585 | 879  | 0.0209        |
+| 1.5603 | 880  | 0.0672        |
+| 1.5621 | 881  | 0.0032        |
+| 1.5638 | 882  | 0.0032        |
+| 1.5656 | 883  | 0.0297        |
+| 1.5674 | 884  | 0.0121        |
+| 1.5691 | 885  | 0.0192        |
+| 1.5709 | 886  | 0.0153        |
+| 1.5727 | 887  | 0.0016        |
+| 1.5745 | 888  | 0.041         |
+| 1.5762 | 889  | 0.099         |
+| 1.5780 | 890  | 0.1625        |
+| 1.5798 | 891  | 0.0037        |
+| 1.5816 | 892  | 0.1435        |
+| 1.5833 | 893  | 0.2743        |
+| 1.5851 | 894  | 0.0027        |
+| 1.5869 | 895  | 0.01          |
+| 1.5887 | 896  | 0.0556        |
+| 1.5904 | 897  | 0.0019        |
+| 1.5922 | 898  | 0.0127        |
+| 1.5940 | 899  | 0.0183        |
+| 1.5957 | 900  | 0.0128        |
+| 1.5975 | 901  | 0.0136        |
+| 1.5993 | 902  | 0.0423        |
+| 1.6011 | 903  | 0.0053        |
+| 1.6028 | 904  | 0.0356        |
+| 1.6046 | 905  | 0.1253        |
+| 1.6064 | 906  | 0.0055        |
+| 1.6082 | 907  | 0.0966        |
+| 1.6099 | 908  | 0.0426        |
+| 1.6117 | 909  | 0.1751        |
+| 1.6135 | 910  | 0.0049        |
+| 1.6152 | 911  | 0.0591        |
+| 1.6170 | 912  | 0.0198        |
+| 1.6188 | 913  | 0.2293        |
+| 1.6206 | 914  | 0.0449        |
+| 1.6223 | 915  | 0.0107        |
+| 1.6241 | 916  | 0.0974        |
+| 1.6259 | 917  | 0.001         |
+| 1.6277 | 918  | 0.0063        |
+| 1.6294 | 919  | 0.0022        |
+| 1.6312 | 920  | 0.1739        |
+| 1.6330 | 921  | 0.005         |
+| 1.6348 | 922  | 0.0028        |
+| 1.6365 | 923  | 0.1195        |
+| 1.6383 | 924  | 0.0656        |
+| 1.6401 | 925  | 0.0033        |
+| 1.6418 | 926  | 0.0253        |
+| 1.6436 | 927  | 0.0222        |
+| 1.6454 | 928  | 0.0102        |
+| 1.6472 | 929  | 0.0006        |
+| 1.6489 | 930  | 0.0021        |
+| 1.6507 | 931  | 0.0111        |
+| 1.6525 | 932  | 0.0087        |
+| 1.6543 | 933  | 0.0154        |
+| 1.6560 | 934  | 0.0225        |
+| 1.6578 | 935  | 0.0215        |
+| 1.6596 | 936  | 0.004         |
+| 1.6613 | 937  | 0.0041        |
+| 1.6631 | 938  | 0.0129        |
+| 1.6649 | 939  | 0.0356        |
+| 1.6667 | 940  | 0.0339        |
+| 1.6684 | 941  | 0.0185        |
+| 1.6702 | 942  | 0.0157        |
+| 1.6720 | 943  | 0.0585        |
+| 1.6738 | 944  | 0.0961        |
+| 1.6755 | 945  | 0.0031        |
+| 1.6773 | 946  | 0.004         |
+| 1.6791 | 947  | 0.0169        |
+| 1.6809 | 948  | 0.0555        |
+| 1.6826 | 949  | 0.0052        |
+| 1.6844 | 950  | 0.0065        |
+| 1.6862 | 951  | 0.126         |
+| 1.6879 | 952  | 0.0052        |
+| 1.6897 | 953  | 0.0045        |
+| 1.6915 | 954  | 0.0806        |
+| 1.6933 | 955  | 0.0513        |
+| 1.6950 | 956  | 0.1021        |
+| 1.6968 | 957  | 0.0233        |
+| 1.6986 | 958  | 0.0068        |
+| 1.7004 | 959  | 0.0019        |
+| 1.7021 | 960  | 0.0256        |
+| 1.7039 | 961  | 0.06          |
+| 1.7057 | 962  | 0.0452        |
+| 1.7074 | 963  | 0.102         |
+| 1.7092 | 964  | 0.0588        |
+| 1.7110 | 965  | 0.1179        |
+| 1.7128 | 966  | 0.0052        |
+| 1.7145 | 967  | 0.0545        |
+| 1.7163 | 968  | 0.0028        |
+| 1.7181 | 969  | 0.0215        |
+| 1.7199 | 970  | 0.0136        |
+| 1.7216 | 971  | 0.0204        |
+| 1.7234 | 972  | 0.0246        |
+| 1.7252 | 973  | 0.0024        |
+| 1.7270 | 974  | 0.1334        |
+| 1.7287 | 975  | 0.0071        |
+| 1.7305 | 976  | 0.001         |
+| 1.7323 | 977  | 0.0013        |
+| 1.7340 | 978  | 0.0065        |
+| 1.7358 | 979  | 0.009         |
+| 1.7376 | 980  | 0.0033        |
+| 1.7394 | 981  | 0.0055        |
+| 1.7411 | 982  | 0.0028        |
+| 1.7429 | 983  | 0.0052        |
+| 1.7447 | 984  | 0.0182        |
+| 1.7465 | 985  | 0.0459        |
+| 1.7482 | 986  | 0.0023        |
+| 1.75   | 987  | 0.0823        |
+| 1.7518 | 988  | 0.0758        |
+| 1.7535 | 989  | 0.0186        |
+| 1.7553 | 990  | 0.0198        |
+| 1.7571 | 991  | 0.0043        |
+| 1.7589 | 992  | 0.0077        |
+| 1.7606 | 993  | 0.0606        |
+| 1.7624 | 994  | 0.0368        |
+| 1.7642 | 995  | 0.0061        |
+| 1.7660 | 996  | 0.0142        |
+| 1.7677 | 997  | 0.0049        |
+| 1.7695 | 998  | 0.0074        |
+| 1.7713 | 999  | 0.093         |
+| 1.7730 | 1000 | 0.1129        |
+| 1.7748 | 1001 | 0.0008        |
+| 1.7766 | 1002 | 0.1378        |
+| 1.7784 | 1003 | 0.0116        |
+| 1.7801 | 1004 | 0.0024        |
+| 1.7819 | 1005 | 0.0235        |
+| 1.7837 | 1006 | 0.0134        |
+| 1.7855 | 1007 | 0.0087        |
+| 1.7872 | 1008 | 0.0445        |
+| 1.7890 | 1009 | 0.0089        |
+| 1.7908 | 1010 | 0.0395        |
+| 1.7926 | 1011 | 0.001         |
+| 1.7943 | 1012 | 0.0072        |
+| 1.7961 | 1013 | 0.215         |
+| 1.7979 | 1014 | 0.0008        |
+| 1.7996 | 1015 | 0.0047        |
+| 1.8014 | 1016 | 0.0195        |
+| 1.8032 | 1017 | 0.0041        |
+| 1.8050 | 1018 | 0.0934        |
+| 1.8067 | 1019 | 0.0008        |
+| 1.8085 | 1020 | 0.0302        |
+| 1.8103 | 1021 | 0.1175        |
+| 1.8121 | 1022 | 0.0717        |
+| 1.8138 | 1023 | 0.0009        |
+| 1.8156 | 1024 | 0.0016        |
+| 1.8174 | 1025 | 0.2146        |
+| 1.8191 | 1026 | 0.0139        |
+| 1.8209 | 1027 | 0.0067        |
+| 1.8227 | 1028 | 0.054         |
+| 1.8245 | 1029 | 0.0097        |
+| 1.8262 | 1030 | 0.0353        |
+| 1.8280 | 1031 | 0.029         |
+| 1.8298 | 1032 | 0.093         |
+| 1.8316 | 1033 | 0.0028        |
+| 1.8333 | 1034 | 0.1996        |
+| 1.8351 | 1035 | 0.0838        |
+| 1.8369 | 1036 | 0.0651        |
+| 1.8387 | 1037 | 0.3878        |
+| 1.8404 | 1038 | 0.0232        |
+| 1.8422 | 1039 | 0.0141        |
+| 1.8440 | 1040 | 0.0039        |
+| 1.8457 | 1041 | 0.0456        |
+| 1.8475 | 1042 | 0.0093        |
+| 1.8493 | 1043 | 0.0142        |
+| 1.8511 | 1044 | 0.0092        |
+| 1.8528 | 1045 | 0.0492        |
+| 1.8546 | 1046 | 0.0503        |
+| 1.8564 | 1047 | 0.035         |
+| 1.8582 | 1048 | 0.1337        |
+| 1.8599 | 1049 | 0.0038        |
+| 1.8617 | 1050 | 0.003         |
+| 1.8635 | 1051 | 0.0156        |
+| 1.8652 | 1052 | 0.0141        |
+| 1.8670 | 1053 | 0.1854        |
+| 1.8688 | 1054 | 0.0029        |
+| 1.8706 | 1055 | 0.0523        |
+| 1.8723 | 1056 | 0.0313        |
+| 1.8741 | 1057 | 0.0539        |
+| 1.8759 | 1058 | 0.0044        |
+| 1.8777 | 1059 | 0.1037        |
+| 1.8794 | 1060 | 0.1125        |
+| 1.8812 | 1061 | 0.031         |
+| 1.8830 | 1062 | 0.0187        |
+| 1.8848 | 1063 | 0.1745        |
+| 1.8865 | 1064 | 0.0048        |
+| 1.8883 | 1065 | 0.0138        |
+| 1.8901 | 1066 | 0.0112        |
+| 1.8918 | 1067 | 0.0005        |
+| 1.8936 | 1068 | 0.0133        |
+| 1.8954 | 1069 | 0.0411        |
+| 1.8972 | 1070 | 0.0063        |
+| 1.8989 | 1071 | 0.0007        |
+| 1.9007 | 1072 | 0.063         |
+| 1.9025 | 1073 | 0.343         |
+| 1.9043 | 1074 | 0.0014        |
+| 1.9060 | 1075 | 0.0194        |
+| 1.9078 | 1076 | 0.0085        |
+| 1.9096 | 1077 | 0.0067        |
+| 1.9113 | 1078 | 0.0204        |
+| 1.9131 | 1079 | 0.0094        |
+| 1.9149 | 1080 | 0.2565        |
+| 1.9167 | 1081 | 0.0456        |
+| 1.9184 | 1082 | 0.0695        |
+| 1.9202 | 1083 | 0.0047        |
+| 1.9220 | 1084 | 0.0246        |
+| 1.9238 | 1085 | 0.0033        |
+| 1.9255 | 1086 | 0.0121        |
+| 1.9273 | 1087 | 0.0148        |
+| 1.9291 | 1088 | 0.0058        |
+| 1.9309 | 1089 | 0.0019        |
+| 1.9326 | 1090 | 0.0012        |
+| 1.9344 | 1091 | 0.0093        |
+| 1.9362 | 1092 | 0.0081        |
+| 1.9379 | 1093 | 0.2302        |
+| 1.9397 | 1094 | 0.0187        |
+| 1.9415 | 1095 | 0.0013        |
+| 1.9433 | 1096 | 0.0545        |
+| 1.9450 | 1097 | 0.0121        |
+| 1.9468 | 1098 | 0.008         |
+| 1.9486 | 1099 | 0.0114        |
+| 1.9504 | 1100 | 0.0938        |
+| 1.9521 | 1101 | 0.0557        |
+| 1.9539 | 1102 | 0.0522        |
+| 1.9557 | 1103 | 0.2804        |
+| 1.9574 | 1104 | 0.0126        |
+| 1.9592 | 1105 | 0.0515        |
+| 1.9610 | 1106 | 0.0458        |
+| 1.9628 | 1107 | 0.0226        |
+| 1.9645 | 1108 | 0.009         |
+| 1.9663 | 1109 | 0.0154        |
+| 1.9681 | 1110 | 0.0059        |
+| 1.9699 | 1111 | 0.0013        |
+| 1.9716 | 1112 | 0.0274        |
+| 1.9734 | 1113 | 0.0194        |
+| 1.9752 | 1114 | 0.0015        |
+| 1.9770 | 1115 | 0.0013        |
+| 1.9787 | 1116 | 0.0509        |
+| 1.9805 | 1117 | 0.0038        |
+| 1.9823 | 1118 | 0.0144        |
+| 1.9840 | 1119 | 0.0009        |
+| 1.9858 | 1120 | 0.0161        |
+| 1.9876 | 1121 | 0.0494        |
+| 1.9894 | 1122 | 0.0037        |
+| 1.9911 | 1123 | 0.0084        |
+| 1.9929 | 1124 | 0.0304        |
+| 1.9947 | 1125 | 0.1233        |
+| 1.9965 | 1126 | 0.0128        |
+| 1.9982 | 1127 | 0.0031        |
+| 2.0    | 1128 | 0.0021        |
+| 2.0018 | 1129 | 0.0326        |
+| 2.0035 | 1130 | 0.0091        |
+| 2.0053 | 1131 | 0.0197        |
+| 2.0071 | 1132 | 0.0184        |
+| 2.0089 | 1133 | 0.0785        |
+| 2.0106 | 1134 | 0.0013        |
+| 2.0124 | 1135 | 0.0203        |
+| 2.0142 | 1136 | 0.0527        |
+| 2.0160 | 1137 | 0.2003        |
+| 2.0177 | 1138 | 0.0256        |
+| 2.0195 | 1139 | 0.0348        |
+| 2.0213 | 1140 | 0.0064        |
+| 2.0230 | 1141 | 0.0192        |
+| 2.0248 | 1142 | 0.0011        |
+| 2.0266 | 1143 | 0.0166        |
+| 2.0284 | 1144 | 0.0069        |
+| 2.0301 | 1145 | 0.0012        |
+| 2.0319 | 1146 | 0.0021        |
+| 2.0337 | 1147 | 0.0111        |
+| 2.0355 | 1148 | 0.0307        |
+| 2.0372 | 1149 | 0.0553        |
+| 2.0390 | 1150 | 0.0178        |
+| 2.0408 | 1151 | 0.0214        |
+| 2.0426 | 1152 | 0.0115        |
+| 2.0443 | 1153 | 0.0836        |
+| 2.0461 | 1154 | 0.0008        |
+| 2.0479 | 1155 | 0.002         |
+| 2.0496 | 1156 | 0.0013        |
+| 2.0514 | 1157 | 0.1271        |
+| 2.0532 | 1158 | 0.0169        |
+| 2.0550 | 1159 | 0.0895        |
+| 2.0567 | 1160 | 0.1264        |
+| 2.0585 | 1161 | 0.0126        |
+| 2.0603 | 1162 | 0.0033        |
+| 2.0621 | 1163 | 0.0056        |
+| 2.0638 | 1164 | 0.0095        |
+| 2.0656 | 1165 | 0.0561        |
+| 2.0674 | 1166 | 0.001         |
+| 2.0691 | 1167 | 0.0119        |
+| 2.0709 | 1168 | 0.0016        |
+| 2.0727 | 1169 | 0.0184        |
+| 2.0745 | 1170 | 0.1006        |
+| 2.0762 | 1171 | 0.2481        |
+| 2.0780 | 1172 | 0.0295        |
+| 2.0798 | 1173 | 0.0054        |
+| 2.0816 | 1174 | 0.0028        |
+| 2.0833 | 1175 | 0.0251        |
+| 2.0851 | 1176 | 0.0066        |
+| 2.0869 | 1177 | 0.0915        |
+| 2.0887 | 1178 | 0.0259        |
+| 2.0904 | 1179 | 0.0038        |
+| 2.0922 | 1180 | 0.0351        |
+| 2.0940 | 1181 | 0.0073        |
+| 2.0957 | 1182 | 0.0009        |
+| 2.0975 | 1183 | 0.0026        |
+| 2.0993 | 1184 | 0.0013        |
+| 2.1011 | 1185 | 0.1223        |
+| 2.1028 | 1186 | 0.0057        |
+| 2.1046 | 1187 | 0.0056        |
+| 2.1064 | 1188 | 0.004         |
+| 2.1082 | 1189 | 0.0064        |
+| 2.1099 | 1190 | 0.0951        |
+| 2.1117 | 1191 | 0.0328        |
+| 2.1135 | 1192 | 0.0422        |
+| 2.1152 | 1193 | 0.003         |
+| 2.1170 | 1194 | 0.0199        |
+| 2.1188 | 1195 | 0.0024        |
+| 2.1206 | 1196 | 0.0493        |
+| 2.1223 | 1197 | 0.0532        |
+| 2.1241 | 1198 | 0.0006        |
+| 2.1259 | 1199 | 0.0039        |
+| 2.1277 | 1200 | 0.0067        |
+| 2.1294 | 1201 | 0.0169        |
+| 2.1312 | 1202 | 0.0012        |
+| 2.1330 | 1203 | 0.002         |
+| 2.1348 | 1204 | 0.0787        |
+| 2.1365 | 1205 | 0.032         |
+| 2.1383 | 1206 | 0.0018        |
+| 2.1401 | 1207 | 0.0014        |
+| 2.1418 | 1208 | 0.0073        |
+| 2.1436 | 1209 | 0.0256        |
+| 2.1454 | 1210 | 0.0073        |
+| 2.1472 | 1211 | 0.0006        |
+| 2.1489 | 1212 | 0.0112        |
+| 2.1507 | 1213 | 0.0116        |
+| 2.1525 | 1214 | 0.0044        |
+| 2.1543 | 1215 | 0.0033        |
+| 2.1560 | 1216 | 0.0094        |
+| 2.1578 | 1217 | 0.0823        |
+| 2.1596 | 1218 | 0.0064        |
+| 2.1613 | 1219 | 0.0052        |
+| 2.1631 | 1220 | 0.0056        |
+| 2.1649 | 1221 | 0.0205        |
+| 2.1667 | 1222 | 0.0508        |
+| 2.1684 | 1223 | 0.0069        |
+| 2.1702 | 1224 | 0.0813        |
+| 2.1720 | 1225 | 0.022         |
+| 2.1738 | 1226 | 0.0254        |
+| 2.1755 | 1227 | 0.0119        |
+| 2.1773 | 1228 | 0.001         |
+| 2.1791 | 1229 | 0.0074        |
+| 2.1809 | 1230 | 0.0104        |
+| 2.1826 | 1231 | 0.0034        |
+| 2.1844 | 1232 | 0.003         |
+| 2.1862 | 1233 | 0.0026        |
+| 2.1879 | 1234 | 0.0005        |
+| 2.1897 | 1235 | 0.0021        |
+| 2.1915 | 1236 | 0.0034        |
+| 2.1933 | 1237 | 0.1037        |
+| 2.1950 | 1238 | 0.0067        |
+| 2.1968 | 1239 | 0.0349        |
+| 2.1986 | 1240 | 0.0699        |
+| 2.2004 | 1241 | 0.0201        |
+| 2.2021 | 1242 | 0.0079        |
+| 2.2039 | 1243 | 0.0335        |
+| 2.2057 | 1244 | 0.0465        |
+| 2.2074 | 1245 | 0.0144        |
+| 2.2092 | 1246 | 0.1061        |
+| 2.2110 | 1247 | 0.0078        |
+| 2.2128 | 1248 | 0.0027        |
+| 2.2145 | 1249 | 0.0019        |
+| 2.2163 | 1250 | 0.0019        |
+| 2.2181 | 1251 | 0.0109        |
+| 2.2199 | 1252 | 0.0029        |
+| 2.2216 | 1253 | 0.0032        |
+| 2.2234 | 1254 | 0.0039        |
+| 2.2252 | 1255 | 0.0082        |
+| 2.2270 | 1256 | 0.0157        |
+| 2.2287 | 1257 | 0.0027        |
+| 2.2305 | 1258 | 0.0025        |
+| 2.2323 | 1259 | 0.0301        |
+| 2.2340 | 1260 | 0.1471        |
+| 2.2358 | 1261 | 0.0021        |
+| 2.2376 | 1262 | 0.0087        |
+| 2.2394 | 1263 | 0.0109        |
+| 2.2411 | 1264 | 0.2735        |
+| 2.2429 | 1265 | 0.0109        |
+| 2.2447 | 1266 | 0.0042        |
+| 2.2465 | 1267 | 0.0301        |
+| 2.2482 | 1268 | 0.1398        |
+| 2.25   | 1269 | 0.0137        |
+| 2.2518 | 1270 | 0.0059        |
+| 2.2535 | 1271 | 0.0076        |
+| 2.2553 | 1272 | 0.0023        |
+| 2.2571 | 1273 | 0.0281        |
+| 2.2589 | 1274 | 0.0012        |
+| 2.2606 | 1275 | 0.0032        |
+| 2.2624 | 1276 | 0.0151        |
+| 2.2642 | 1277 | 0.0021        |
+| 2.2660 | 1278 | 0.001         |
+| 2.2677 | 1279 | 0.0258        |
+| 2.2695 | 1280 | 0.26          |
+| 2.2713 | 1281 | 0.0036        |
+| 2.2730 | 1282 | 0.0005        |
+| 2.2748 | 1283 | 0.0038        |
+| 2.2766 | 1284 | 0.0016        |
+| 2.2784 | 1285 | 0.0401        |
+| 2.2801 | 1286 | 0.0028        |
+| 2.2819 | 1287 | 0.008         |
+| 2.2837 | 1288 | 0.0077        |
+| 2.2855 | 1289 | 0.0133        |
+| 2.2872 | 1290 | 0.0578        |
+| 2.2890 | 1291 | 0.0008        |
+| 2.2908 | 1292 | 0.0051        |
+| 2.2926 | 1293 | 0.0036        |
+| 2.2943 | 1294 | 0.047         |
+| 2.2961 | 1295 | 0.0026        |
+| 2.2979 | 1296 | 0.0109        |
+| 2.2996 | 1297 | 0.0432        |
+| 2.3014 | 1298 | 0.0184        |
+| 2.3032 | 1299 | 0.0483        |
+| 2.3050 | 1300 | 0.0101        |
+| 2.3067 | 1301 | 0.0098        |
+| 2.3085 | 1302 | 0.0232        |
+| 2.3103 | 1303 | 0.0105        |
+| 2.3121 | 1304 | 0.0062        |
+| 2.3138 | 1305 | 0.0541        |
+| 2.3156 | 1306 | 0.0646        |
+| 2.3174 | 1307 | 0.0084        |
+| 2.3191 | 1308 | 0.0313        |
+| 2.3209 | 1309 | 0.0081        |
+| 2.3227 | 1310 | 0.012         |
+| 2.3245 | 1311 | 0.0036        |
+| 2.3262 | 1312 | 0.0518        |
+| 2.3280 | 1313 | 0.0018        |
+| 2.3298 | 1314 | 0.0044        |
+| 2.3316 | 1315 | 0.0495        |
+| 2.3333 | 1316 | 0.0733        |
+| 2.3351 | 1317 | 0.0478        |
+| 2.3369 | 1318 | 0.0408        |
+| 2.3387 | 1319 | 0.0657        |
+| 2.3404 | 1320 | 0.0007        |
+| 2.3422 | 1321 | 0.0286        |
+| 2.3440 | 1322 | 0.0145        |
+| 2.3457 | 1323 | 0.0028        |
+| 2.3475 | 1324 | 0.013         |
+| 2.3493 | 1325 | 0.0088        |
+| 2.3511 | 1326 | 0.0091        |
+| 2.3528 | 1327 | 0.2375        |
+| 2.3546 | 1328 | 0.0332        |
+| 2.3564 | 1329 | 0.1036        |
+| 2.3582 | 1330 | 0.0073        |
+| 2.3599 | 1331 | 0.0177        |
+| 2.3617 | 1332 | 0.0008        |
+| 2.3635 | 1333 | 0.011         |
+| 2.3652 | 1334 | 0.0228        |
+| 2.3670 | 1335 | 0.0183        |
+| 2.3688 | 1336 | 0.0011        |
+| 2.3706 | 1337 | 0.0178        |
+| 2.3723 | 1338 | 0.2155        |
+| 2.3741 | 1339 | 0.0048        |
+| 2.3759 | 1340 | 0.0854        |
+| 2.3777 | 1341 | 0.0146        |
+| 2.3794 | 1342 | 0.0034        |
+| 2.3812 | 1343 | 0.0105        |
+| 2.3830 | 1344 | 0.0181        |
+| 2.3848 | 1345 | 0.0126        |
+| 2.3865 | 1346 | 0.0555        |
+| 2.3883 | 1347 | 0.1284        |
+| 2.3901 | 1348 | 0.0071        |
+| 2.3918 | 1349 | 0.0007        |
+| 2.3936 | 1350 | 0.003         |
+| 2.3954 | 1351 | 0.013         |
+| 2.3972 | 1352 | 0.0023        |
+| 2.3989 | 1353 | 0.0083        |
+| 2.4007 | 1354 | 0.0217        |
+| 2.4025 | 1355 | 0.2555        |
+| 2.4043 | 1356 | 0.0171        |
+| 2.4060 | 1357 | 0.0028        |
+| 2.4078 | 1358 | 0.0796        |
+| 2.4096 | 1359 | 0.0054        |
+| 2.4113 | 1360 | 0.1113        |
+| 2.4131 | 1361 | 0.0291        |
+| 2.4149 | 1362 | 0.0186        |
+| 2.4167 | 1363 | 0.0248        |
+| 2.4184 | 1364 | 0.0281        |
+| 2.4202 | 1365 | 0.0386        |
+| 2.4220 | 1366 | 0.0049        |
+| 2.4238 | 1367 | 0.0023        |
+| 2.4255 | 1368 | 0.0229        |
+| 2.4273 | 1369 | 0.0043        |
+| 2.4291 | 1370 | 0.0351        |
+| 2.4309 | 1371 | 0.003         |
+| 2.4326 | 1372 | 0.0593        |
+| 2.4344 | 1373 | 0.0746        |
+| 2.4362 | 1374 | 0.1464        |
+| 2.4379 | 1375 | 0.0143        |
+| 2.4397 | 1376 | 0.0871        |
+| 2.4415 | 1377 | 0.034         |
+| 2.4433 | 1378 | 0.0096        |
+| 2.4450 | 1379 | 0.0507        |
+| 2.4468 | 1380 | 0.0248        |
+| 2.4486 | 1381 | 0.0131        |
+| 2.4504 | 1382 | 0.0123        |
+| 2.4521 | 1383 | 0.0303        |
+| 2.4539 | 1384 | 0.0013        |
+| 2.4557 | 1385 | 0.0902        |
+| 2.4574 | 1386 | 0.0375        |
+| 2.4592 | 1387 | 0.0978        |
+| 2.4610 | 1388 | 0.0151        |
+| 2.4628 | 1389 | 0.0139        |
+| 2.4645 | 1390 | 0.1327        |
+| 2.4663 | 1391 | 0.0248        |
+| 2.4681 | 1392 | 0.0086        |
+| 2.4699 | 1393 | 0.0006        |
+| 2.4716 | 1394 | 0.0153        |
+| 2.4734 | 1395 | 0.3766        |
+| 2.4752 | 1396 | 0.0252        |
+| 2.4770 | 1397 | 0.1675        |
+| 2.4787 | 1398 | 0.0018        |
+| 2.4805 | 1399 | 0.0526        |
+| 2.4823 | 1400 | 0.0191        |
+| 2.4840 | 1401 | 0.0077        |
+| 2.4858 | 1402 | 0.0011        |
+| 2.4876 | 1403 | 0.0261        |
+| 2.4894 | 1404 | 0.0028        |
+| 2.4911 | 1405 | 0.0012        |
+| 2.4929 | 1406 | 0.0011        |
+| 2.4947 | 1407 | 0.0015        |
+| 2.4965 | 1408 | 0.0183        |
+| 2.4982 | 1409 | 0.0376        |
+| 2.5    | 1410 | 0.0343        |
+| 2.5018 | 1411 | 0.0184        |
+| 2.5035 | 1412 | 0.0068        |
+| 2.5053 | 1413 | 0.0044        |
+| 2.5071 | 1414 | 0.04          |
+| 2.5089 | 1415 | 0.1035        |
+| 2.5106 | 1416 | 0.0018        |
+| 2.5124 | 1417 | 0.0578        |
+| 2.5142 | 1418 | 0.0039        |
+| 2.5160 | 1419 | 0.0002        |
+| 2.5177 | 1420 | 0.0022        |
+| 2.5195 | 1421 | 0.0005        |
+| 2.5213 | 1422 | 0.0064        |
+| 2.5230 | 1423 | 0.0239        |
+| 2.5248 | 1424 | 0.0209        |
+| 2.5266 | 1425 | 0.0026        |
+| 2.5284 | 1426 | 0.0019        |
+| 2.5301 | 1427 | 0.1177        |
+| 2.5319 | 1428 | 0.0007        |
+| 2.5337 | 1429 | 0.0173        |
+| 2.5355 | 1430 | 0.0744        |
+| 2.5372 | 1431 | 0.0078        |
+| 2.5390 | 1432 | 0.0025        |
+| 2.5408 | 1433 | 0.003         |
+| 2.5426 | 1434 | 0.0116        |
+| 2.5443 | 1435 | 0.0016        |
+| 2.5461 | 1436 | 0.0018        |
+| 2.5479 | 1437 | 0.0636        |
+| 2.5496 | 1438 | 0.0021        |
+| 2.5514 | 1439 | 0.0008        |
+| 2.5532 | 1440 | 0.0048        |
+| 2.5550 | 1441 | 0.0116        |
+| 2.5567 | 1442 | 0.0701        |
+| 2.5585 | 1443 | 0.003         |
+| 2.5603 | 1444 | 0.0051        |
+| 2.5621 | 1445 | 0.0265        |
+| 2.5638 | 1446 | 0.0297        |
+| 2.5656 | 1447 | 0.0062        |
+| 2.5674 | 1448 | 0.0193        |
+| 2.5691 | 1449 | 0.0042        |
+| 2.5709 | 1450 | 0.0075        |
+| 2.5727 | 1451 | 0.0033        |
+| 2.5745 | 1452 | 0.0078        |
+| 2.5762 | 1453 | 0.0662        |
+| 2.5780 | 1454 | 0.0103        |
+| 2.5798 | 1455 | 0.0138        |
+| 2.5816 | 1456 | 0.0049        |
+| 2.5833 | 1457 | 0.0023        |
+| 2.5851 | 1458 | 0.0463        |
+| 2.5869 | 1459 | 0.0539        |
+| 2.5887 | 1460 | 0.0112        |
+| 2.5904 | 1461 | 0.0088        |
+| 2.5922 | 1462 | 0.0096        |
+| 2.5940 | 1463 | 0.0063        |
+| 2.5957 | 1464 | 0.004         |
+| 2.5975 | 1465 | 0.0753        |
+| 2.5993 | 1466 | 0.0013        |
+| 2.6011 | 1467 | 0.0052        |
+| 2.6028 | 1468 | 0.0162        |
+| 2.6046 | 1469 | 0.0015        |
+| 2.6064 | 1470 | 0.0194        |
+| 2.6082 | 1471 | 0.0166        |
+| 2.6099 | 1472 | 0.0015        |
+| 2.6117 | 1473 | 0.0045        |
+| 2.6135 | 1474 | 0.0275        |
+| 2.6152 | 1475 | 0.192         |
+| 2.6170 | 1476 | 0.0113        |
+| 2.6188 | 1477 | 0.0165        |
+| 2.6206 | 1478 | 0.0037        |
+| 2.6223 | 1479 | 0.0031        |
+| 2.6241 | 1480 | 0.0522        |
+| 2.6259 | 1481 | 0.0251        |
+| 2.6277 | 1482 | 0.0531        |
+| 2.6294 | 1483 | 0.0165        |
+| 2.6312 | 1484 | 0.0087        |
+| 2.6330 | 1485 | 0.0982        |
+| 2.6348 | 1486 | 0.0813        |
+| 2.6365 | 1487 | 0.0023        |
+| 2.6383 | 1488 | 0.0656        |
+| 2.6401 | 1489 | 0.0128        |
+| 2.6418 | 1490 | 0.053         |
+| 2.6436 | 1491 | 0.0023        |
+| 2.6454 | 1492 | 0.0314        |
+| 2.6472 | 1493 | 0.0018        |
+| 2.6489 | 1494 | 0.2133        |
+| 2.6507 | 1495 | 0.02          |
+| 2.6525 | 1496 | 0.0149        |
+| 2.6543 | 1497 | 0.0045        |
+| 2.6560 | 1498 | 0.2646        |
+| 2.6578 | 1499 | 0.007         |
+| 2.6596 | 1500 | 0.0031        |
+| 2.6613 | 1501 | 0.0681        |
+| 2.6631 | 1502 | 0.0075        |
+| 2.6649 | 1503 | 0.0009        |
+| 2.6667 | 1504 | 0.0212        |
+| 2.6684 | 1505 | 0.0013        |
+| 2.6702 | 1506 | 0.0118        |
+| 2.6720 | 1507 | 0.0002        |
+| 2.6738 | 1508 | 0.0069        |
+| 2.6755 | 1509 | 0.0119        |
+| 2.6773 | 1510 | 0.0193        |
+| 2.6791 | 1511 | 0.0015        |
+| 2.6809 | 1512 | 0.0486        |
+| 2.6826 | 1513 | 0.156         |
+| 2.6844 | 1514 | 0.02          |
+| 2.6862 | 1515 | 0.0225        |
+| 2.6879 | 1516 | 0.0024        |
+| 2.6897 | 1517 | 0.0272        |
+| 2.6915 | 1518 | 0.0115        |
+| 2.6933 | 1519 | 0.0141        |
+| 2.6950 | 1520 | 0.0155        |
+| 2.6968 | 1521 | 0.0239        |
+| 2.6986 | 1522 | 0.0088        |
+| 2.7004 | 1523 | 0.0131        |
+| 2.7021 | 1524 | 0.0035        |
+| 2.7039 | 1525 | 0.3601        |
+| 2.7057 | 1526 | 0.0384        |
+| 2.7074 | 1527 | 0.0054        |
+| 2.7092 | 1528 | 0.0023        |
+| 2.7110 | 1529 | 0.0008        |
+| 2.7128 | 1530 | 0.0622        |
+| 2.7145 | 1531 | 0.0068        |
+| 2.7163 | 1532 | 0.005         |
+| 2.7181 | 1533 | 0.0466        |
+| 2.7199 | 1534 | 0.0025        |
+| 2.7216 | 1535 | 0.0124        |
+| 2.7234 | 1536 | 0.0059        |
+| 2.7252 | 1537 | 0.0068        |
+| 2.7270 | 1538 | 0.0418        |
+| 2.7287 | 1539 | 0.0108        |
+| 2.7305 | 1540 | 0.0112        |
+| 2.7323 | 1541 | 0.0085        |
+| 2.7340 | 1542 | 0.0032        |
+| 2.7358 | 1543 | 0.052         |
+| 2.7376 | 1544 | 0.0423        |
+| 2.7394 | 1545 | 0.0096        |
+| 2.7411 | 1546 | 0.0291        |
+| 2.7429 | 1547 | 0.0444        |
+| 2.7447 | 1548 | 0.0047        |
+| 2.7465 | 1549 | 0.0273        |
+| 2.7482 | 1550 | 0.0106        |
+| 2.75   | 1551 | 0.1274        |
+| 2.7518 | 1552 | 0.0065        |
+| 2.7535 | 1553 | 0.0033        |
+| 2.7553 | 1554 | 0.0012        |
+| 2.7571 | 1555 | 0.009         |
+| 2.7589 | 1556 | 0.1048        |
+| 2.7606 | 1557 | 0.0149        |
+| 2.7624 | 1558 | 0.0807        |
+| 2.7642 | 1559 | 0.0807        |
+| 2.7660 | 1560 | 0.0103        |
+| 2.7677 | 1561 | 0.038         |
+| 2.7695 | 1562 | 0.0068        |
+| 2.7713 | 1563 | 0.0529        |
+| 2.7730 | 1564 | 0.1415        |
+| 2.7748 | 1565 | 0.0168        |
+| 2.7766 | 1566 | 0.0016        |
+| 2.7784 | 1567 | 0.0017        |
+| 2.7801 | 1568 | 0.0223        |
+| 2.7819 | 1569 | 0.0137        |
+| 2.7837 | 1570 | 0.0051        |
+| 2.7855 | 1571 | 0.0054        |
+| 2.7872 | 1572 | 0.0206        |
+| 2.7890 | 1573 | 0.0465        |
+| 2.7908 | 1574 | 0.0031        |
+| 2.7926 | 1575 | 0.0006        |
+| 2.7943 | 1576 | 0.0047        |
+| 2.7961 | 1577 | 0.0086        |
+| 2.7979 | 1578 | 0.0443        |
+| 2.7996 | 1579 | 0.0099        |
+| 2.8014 | 1580 | 0.0878        |
+| 2.8032 | 1581 | 0.0042        |
+| 2.8050 | 1582 | 0.1406        |
+| 2.8067 | 1583 | 0.0034        |
+| 2.8085 | 1584 | 0.0085        |
+| 2.8103 | 1585 | 0.0118        |
+| 2.8121 | 1586 | 0.0182        |
+| 2.8138 | 1587 | 0.0013        |
+| 2.8156 | 1588 | 0.0049        |
+| 2.8174 | 1589 | 0.0104        |
+| 2.8191 | 1590 | 0.0068        |
+| 2.8209 | 1591 | 0.0017        |
+| 2.8227 | 1592 | 0.004         |
+| 2.8245 | 1593 | 0.0048        |
+| 2.8262 | 1594 | 0.0253        |
+| 2.8280 | 1595 | 0.0672        |
+| 2.8298 | 1596 | 0.0008        |
+| 2.8316 | 1597 | 0.0086        |
+| 2.8333 | 1598 | 0.01          |
+| 2.8351 | 1599 | 0.0165        |
+| 2.8369 | 1600 | 0.1176        |
+| 2.8387 | 1601 | 0.0025        |
+| 2.8404 | 1602 | 0.0068        |
+| 2.8422 | 1603 | 0.0829        |
+| 2.8440 | 1604 | 0.0037        |
+| 2.8457 | 1605 | 0.0347        |
+| 2.8475 | 1606 | 0.0046        |
+| 2.8493 | 1607 | 0.0129        |
+| 2.8511 | 1608 | 0.0325        |
+| 2.8528 | 1609 | 0.0039        |
+| 2.8546 | 1610 | 0.0414        |
+| 2.8564 | 1611 | 0.102         |
+| 2.8582 | 1612 | 0.0935        |
+| 2.8599 | 1613 | 0.0031        |
+| 2.8617 | 1614 | 0.0125        |
+| 2.8635 | 1615 | 0.0011        |
+| 2.8652 | 1616 | 0.0041        |
+| 2.8670 | 1617 | 0.0411        |
+| 2.8688 | 1618 | 0.0029        |
+| 2.8706 | 1619 | 0.1064        |
+| 2.8723 | 1620 | 0.0229        |
+| 2.8741 | 1621 | 0.0142        |
+| 2.8759 | 1622 | 0.0847        |
+| 2.8777 | 1623 | 0.0743        |
+| 2.8794 | 1624 | 0.0019        |
+| 2.8812 | 1625 | 0.0194        |
+| 2.8830 | 1626 | 0.0019        |
+| 2.8848 | 1627 | 0.0143        |
+| 2.8865 | 1628 | 0.0011        |
+| 2.8883 | 1629 | 0.0144        |
+| 2.8901 | 1630 | 0.154         |
+| 2.8918 | 1631 | 0.0092        |
+| 2.8936 | 1632 | 0.0086        |
+| 2.8954 | 1633 | 0.0052        |
+| 2.8972 | 1634 | 0.1818        |
+| 2.8989 | 1635 | 0.0022        |
+| 2.9007 | 1636 | 0.003         |
+| 2.9025 | 1637 | 0.0021        |
+| 2.9043 | 1638 | 0.0091        |
+| 2.9060 | 1639 | 0.0369        |
+| 2.9078 | 1640 | 0.0007        |
+| 2.9096 | 1641 | 0.007         |
+| 2.9113 | 1642 | 0.0071        |
+| 2.9131 | 1643 | 0.0345        |
+| 2.9149 | 1644 | 0.0068        |
+| 2.9167 | 1645 | 0.0063        |
+| 2.9184 | 1646 | 0.0039        |
+| 2.9202 | 1647 | 0.0262        |
+| 2.9220 | 1648 | 0.0653        |
+| 2.9238 | 1649 | 0.0144        |
+| 2.9255 | 1650 | 0.014         |
+| 2.9273 | 1651 | 0.0014        |
+| 2.9291 | 1652 | 0.011         |
+| 2.9309 | 1653 | 0.0104        |
+| 2.9326 | 1654 | 0.0073        |
+| 2.9344 | 1655 | 0.0245        |
+| 2.9362 | 1656 | 0.1735        |
+| 2.9379 | 1657 | 0.0188        |
+| 2.9397 | 1658 | 0.0149        |
+| 2.9415 | 1659 | 0.0186        |
+| 2.9433 | 1660 | 0.0397        |
+| 2.9450 | 1661 | 0.0529        |
+| 2.9468 | 1662 | 0.0345        |
+| 2.9486 | 1663 | 0.0121        |
+| 2.9504 | 1664 | 0.0802        |
+| 2.9521 | 1665 | 0.0051        |
+| 2.9539 | 1666 | 0.0734        |
+| 2.9557 | 1667 | 0.0739        |
+| 2.9574 | 1668 | 0.0191        |
+| 2.9592 | 1669 | 0.0362        |
+| 2.9610 | 1670 | 0.007         |
+| 2.9628 | 1671 | 0.0064        |
+| 2.9645 | 1672 | 0.2386        |
+| 2.9663 | 1673 | 0.0224        |
+| 2.9681 | 1674 | 0.0007        |
+| 2.9699 | 1675 | 0.0019        |
+| 2.9716 | 1676 | 0.0333        |
+| 2.9734 | 1677 | 0.0067        |
+| 2.9752 | 1678 | 0.0052        |
+| 2.9770 | 1679 | 0.0028        |
+| 2.9787 | 1680 | 0.0462        |
+| 2.9805 | 1681 | 0.0072        |
+| 2.9823 | 1682 | 0.0023        |
+| 2.9840 | 1683 | 0.01          |
+| 2.9858 | 1684 | 0.0208        |
+| 2.9876 | 1685 | 0.0189        |
+| 2.9894 | 1686 | 0.002         |
+| 2.9911 | 1687 | 0.0021        |
+| 2.9929 | 1688 | 0.0479        |
+| 2.9947 | 1689 | 0.0159        |
+| 2.9965 | 1690 | 0.0618        |
+| 2.9982 | 1691 | 0.0267        |
+| 3.0    | 1692 | 0.3215        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 5.1.1
+- Transformers: 4.56.2
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.1.1
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "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",
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.56.2",
+    "pytorch": "2.8.0+cu128"
+  },
+  "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:d12e7f13e6bea43a8c077a687435bbc447f5b1dae43841c197c0974cf1104450
+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]"
+}