---
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)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### 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-64")
# 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, 0.4847, -0.0572],
# [ 0.4847, 1.0000, -0.0541],
# [-0.0572, -0.0541, 1.0000]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 9,020 training samples
* Columns: anchor and positive
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 6 tokens
- mean: 9.67 tokens
- max: 21 tokens
| - min: 40 tokens
- mean: 86.17 tokens
- max: 256 tokens
|
* Samples:
| anchor | positive |
|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1d array in char datatype in python | def _convert_to_array(array_like, dtype):
"""
Convert Matrix attributes which are array-like or buffer to array.
"""
if isinstance(array_like, bytes):
return np.frombuffer(array_like, dtype=dtype)
return np.asarray(array_like, dtype=dtype) |
| python condition non none | def _not(condition=None, **kwargs):
"""
Return the opposite of input condition.
:param condition: condition to process.
:result: not condition.
:rtype: bool
"""
result = True
if condition is not None:
result = not run(condition, **kwargs)
return result |
| accessing a column from a matrix in python | def get_column(self, X, column):
"""Return a column of the given matrix.
Args:
X: `numpy.ndarray` or `pandas.DataFrame`.
column: `int` or `str`.
Returns:
np.ndarray: Selected column.
"""
if isinstance(X, pd.DataFrame):
return X[column].values
return X[:, column] |
* Loss: [MultipleNegativesRankingLoss](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`: 64
- `fp16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 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`: {}
Click to expand
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0071 | 1 | 0.4603 |
| 0.0142 | 2 | 0.3179 |
| 0.0213 | 3 | 0.1802 |
| 0.0284 | 4 | 0.2268 |
| 0.0355 | 5 | 0.2288 |
| 0.0426 | 6 | 0.1769 |
| 0.0496 | 7 | 0.1555 |
| 0.0567 | 8 | 0.2626 |
| 0.0638 | 9 | 0.3319 |
| 0.0709 | 10 | 0.28 |
| 0.0780 | 11 | 0.3356 |
| 0.0851 | 12 | 0.3241 |
| 0.0922 | 13 | 0.2933 |
| 0.0993 | 14 | 0.3929 |
| 0.1064 | 15 | 0.1861 |
| 0.1135 | 16 | 0.1983 |
| 0.1206 | 17 | 0.1605 |
| 0.1277 | 18 | 0.0918 |
| 0.1348 | 19 | 0.2831 |
| 0.1418 | 20 | 0.1709 |
| 0.1489 | 21 | 0.1984 |
| 0.1560 | 22 | 0.2657 |
| 0.1631 | 23 | 0.1619 |
| 0.1702 | 24 | 0.1728 |
| 0.1773 | 25 | 0.1791 |
| 0.1844 | 26 | 0.2429 |
| 0.1915 | 27 | 0.2743 |
| 0.1986 | 28 | 0.2813 |
| 0.2057 | 29 | 0.2192 |
| 0.2128 | 30 | 0.166 |
| 0.2199 | 31 | 0.2557 |
| 0.2270 | 32 | 0.3556 |
| 0.2340 | 33 | 0.2238 |
| 0.2411 | 34 | 0.2552 |
| 0.2482 | 35 | 0.2266 |
| 0.2553 | 36 | 0.4347 |
| 0.2624 | 37 | 0.2803 |
| 0.2695 | 38 | 0.1219 |
| 0.2766 | 39 | 0.1989 |
| 0.2837 | 40 | 0.2364 |
| 0.2908 | 41 | 0.2237 |
| 0.2979 | 42 | 0.1567 |
| 0.3050 | 43 | 0.2509 |
| 0.3121 | 44 | 0.16 |
| 0.3191 | 45 | 0.2148 |
| 0.3262 | 46 | 0.1953 |
| 0.3333 | 47 | 0.2447 |
| 0.3404 | 48 | 0.2001 |
| 0.3475 | 49 | 0.283 |
| 0.3546 | 50 | 0.1505 |
| 0.3617 | 51 | 0.2825 |
| 0.3688 | 52 | 0.2137 |
| 0.3759 | 53 | 0.1376 |
| 0.3830 | 54 | 0.3898 |
| 0.3901 | 55 | 0.1873 |
| 0.3972 | 56 | 0.2226 |
| 0.4043 | 57 | 0.3129 |
| 0.4113 | 58 | 0.2127 |
| 0.4184 | 59 | 0.3474 |
| 0.4255 | 60 | 0.0971 |
| 0.4326 | 61 | 0.1728 |
| 0.4397 | 62 | 0.2851 |
| 0.4468 | 63 | 0.2608 |
| 0.4539 | 64 | 0.3269 |
| 0.4610 | 65 | 0.4905 |
| 0.4681 | 66 | 0.1886 |
| 0.4752 | 67 | 0.1465 |
| 0.4823 | 68 | 0.2342 |
| 0.4894 | 69 | 0.1915 |
| 0.4965 | 70 | 0.2291 |
| 0.5035 | 71 | 0.3232 |
| 0.5106 | 72 | 0.1633 |
| 0.5177 | 73 | 0.2039 |
| 0.5248 | 74 | 0.2441 |
| 0.5319 | 75 | 0.2336 |
| 0.5390 | 76 | 0.139 |
| 0.5461 | 77 | 0.4471 |
| 0.5532 | 78 | 0.1989 |
| 0.5603 | 79 | 0.2112 |
| 0.5674 | 80 | 0.1862 |
| 0.5745 | 81 | 0.2353 |
| 0.5816 | 82 | 0.2326 |
| 0.5887 | 83 | 0.3223 |
| 0.5957 | 84 | 0.2055 |
| 0.6028 | 85 | 0.2968 |
| 0.6099 | 86 | 0.2531 |
| 0.6170 | 87 | 0.2401 |
| 0.6241 | 88 | 0.1632 |
| 0.6312 | 89 | 0.4203 |
| 0.6383 | 90 | 0.1959 |
| 0.6454 | 91 | 0.2309 |
| 0.6525 | 92 | 0.3729 |
| 0.6596 | 93 | 0.2488 |
| 0.6667 | 94 | 0.1698 |
| 0.6738 | 95 | 0.267 |
| 0.6809 | 96 | 0.1658 |
| 0.6879 | 97 | 0.2158 |
| 0.6950 | 98 | 0.1665 |
| 0.7021 | 99 | 0.1897 |
| 0.7092 | 100 | 0.2159 |
| 0.7163 | 101 | 0.1932 |
| 0.7234 | 102 | 0.2236 |
| 0.7305 | 103 | 0.1287 |
| 0.7376 | 104 | 0.1917 |
| 0.7447 | 105 | 0.4039 |
| 0.7518 | 106 | 0.388 |
| 0.7589 | 107 | 0.1267 |
| 0.7660 | 108 | 0.1851 |
| 0.7730 | 109 | 0.1916 |
| 0.7801 | 110 | 0.1893 |
| 0.7872 | 111 | 0.1702 |
| 0.7943 | 112 | 0.1552 |
| 0.8014 | 113 | 0.1529 |
| 0.8085 | 114 | 0.1634 |
| 0.8156 | 115 | 0.2136 |
| 0.8227 | 116 | 0.1719 |
| 0.8298 | 117 | 0.2529 |
| 0.8369 | 118 | 0.2329 |
| 0.8440 | 119 | 0.2483 |
| 0.8511 | 120 | 0.132 |
| 0.8582 | 121 | 0.182 |
| 0.8652 | 122 | 0.127 |
| 0.8723 | 123 | 0.3685 |
| 0.8794 | 124 | 0.4202 |
| 0.8865 | 125 | 0.2173 |
| 0.8936 | 126 | 0.0657 |
| 0.9007 | 127 | 0.0838 |
| 0.9078 | 128 | 0.1592 |
| 0.9149 | 129 | 0.2506 |
| 0.9220 | 130 | 0.1624 |
| 0.9291 | 131 | 0.1511 |
| 0.9362 | 132 | 0.138 |
| 0.9433 | 133 | 0.2187 |
| 0.9504 | 134 | 0.2891 |
| 0.9574 | 135 | 0.158 |
| 0.9645 | 136 | 0.2595 |
| 0.9716 | 137 | 0.2911 |
| 0.9787 | 138 | 0.2141 |
| 0.9858 | 139 | 0.1723 |
| 0.9929 | 140 | 0.1847 |
| 1.0 | 141 | 0.2606 |
| 1.0071 | 142 | 0.1283 |
| 1.0142 | 143 | 0.1626 |
| 1.0213 | 144 | 0.2121 |
| 1.0284 | 145 | 0.142 |
| 1.0355 | 146 | 0.1335 |
| 1.0426 | 147 | 0.1084 |
| 1.0496 | 148 | 0.15 |
| 1.0567 | 149 | 0.1459 |
| 1.0638 | 150 | 0.0674 |
| 1.0709 | 151 | 0.1393 |
| 1.0780 | 152 | 0.1582 |
| 1.0851 | 153 | 0.1295 |
| 1.0922 | 154 | 0.1402 |
| 1.0993 | 155 | 0.2266 |
| 1.1064 | 156 | 0.1025 |
| 1.1135 | 157 | 0.1616 |
| 1.1206 | 158 | 0.1795 |
| 1.1277 | 159 | 0.1583 |
| 1.1348 | 160 | 0.1624 |
| 1.1418 | 161 | 0.1068 |
| 1.1489 | 162 | 0.1301 |
| 1.1560 | 163 | 0.1792 |
| 1.1631 | 164 | 0.1656 |
| 1.1702 | 165 | 0.1666 |
| 1.1773 | 166 | 0.1031 |
| 1.1844 | 167 | 0.1092 |
| 1.1915 | 168 | 0.1668 |
| 1.1986 | 169 | 0.1218 |
| 1.2057 | 170 | 0.146 |
| 1.2128 | 171 | 0.1041 |
| 1.2199 | 172 | 0.2275 |
| 1.2270 | 173 | 0.1017 |
| 1.2340 | 174 | 0.1025 |
| 1.2411 | 175 | 0.1385 |
| 1.2482 | 176 | 0.1024 |
| 1.2553 | 177 | 0.1073 |
| 1.2624 | 178 | 0.0802 |
| 1.2695 | 179 | 0.1985 |
| 1.2766 | 180 | 0.1918 |
| 1.2837 | 181 | 0.092 |
| 1.2908 | 182 | 0.1591 |
| 1.2979 | 183 | 0.2512 |
| 1.3050 | 184 | 0.2213 |
| 1.3121 | 185 | 0.129 |
| 1.3191 | 186 | 0.0759 |
| 1.3262 | 187 | 0.243 |
| 1.3333 | 188 | 0.1759 |
| 1.3404 | 189 | 0.126 |
| 1.3475 | 190 | 0.1105 |
| 1.3546 | 191 | 0.1789 |
| 1.3617 | 192 | 0.1841 |
| 1.3688 | 193 | 0.1074 |
| 1.3759 | 194 | 0.1293 |
| 1.3830 | 195 | 0.1228 |
| 1.3901 | 196 | 0.1574 |
| 1.3972 | 197 | 0.1073 |
| 1.4043 | 198 | 0.1305 |
| 1.4113 | 199 | 0.1911 |
| 1.4184 | 200 | 0.1088 |
| 1.4255 | 201 | 0.111 |
| 1.4326 | 202 | 0.1639 |
| 1.4397 | 203 | 0.0944 |
| 1.4468 | 204 | 0.2008 |
| 1.4539 | 205 | 0.136 |
| 1.4610 | 206 | 0.1981 |
| 1.4681 | 207 | 0.0848 |
| 1.4752 | 208 | 0.0771 |
| 1.4823 | 209 | 0.0933 |
| 1.4894 | 210 | 0.1794 |
| 1.4965 | 211 | 0.1533 |
| 1.5035 | 212 | 0.1841 |
| 1.5106 | 213 | 0.1724 |
| 1.5177 | 214 | 0.1205 |
| 1.5248 | 215 | 0.1118 |
| 1.5319 | 216 | 0.16 |
| 1.5390 | 217 | 0.2911 |
| 1.5461 | 218 | 0.1678 |
| 1.5532 | 219 | 0.1032 |
| 1.5603 | 220 | 0.1438 |
| 1.5674 | 221 | 0.1581 |
| 1.5745 | 222 | 0.1143 |
| 1.5816 | 223 | 0.1782 |
| 1.5887 | 224 | 0.2768 |
| 1.5957 | 225 | 0.1127 |
| 1.6028 | 226 | 0.1719 |
| 1.6099 | 227 | 0.2252 |
| 1.6170 | 228 | 0.2182 |
| 1.6241 | 229 | 0.287 |
| 1.6312 | 230 | 0.1314 |
| 1.6383 | 231 | 0.1951 |
| 1.6454 | 232 | 0.13 |
| 1.6525 | 233 | 0.0677 |
| 1.6596 | 234 | 0.1188 |
| 1.6667 | 235 | 0.1214 |
| 1.6738 | 236 | 0.1219 |
| 1.6809 | 237 | 0.1646 |
| 1.6879 | 238 | 0.1079 |
| 1.6950 | 239 | 0.1624 |
| 1.7021 | 240 | 0.0994 |
| 1.7092 | 241 | 0.194 |
| 1.7163 | 242 | 0.1104 |
| 1.7234 | 243 | 0.1223 |
| 1.7305 | 244 | 0.0918 |
| 1.7376 | 245 | 0.0835 |
| 1.7447 | 246 | 0.0994 |
| 1.7518 | 247 | 0.1375 |
| 1.7589 | 248 | 0.1004 |
| 1.7660 | 249 | 0.1164 |
| 1.7730 | 250 | 0.1151 |
| 1.7801 | 251 | 0.0868 |
| 1.7872 | 252 | 0.2498 |
| 1.7943 | 253 | 0.0741 |
| 1.8014 | 254 | 0.1417 |
| 1.8085 | 255 | 0.0514 |
| 1.8156 | 256 | 0.2346 |
| 1.8227 | 257 | 0.2383 |
| 1.8298 | 258 | 0.1432 |
| 1.8369 | 259 | 0.1563 |
| 1.8440 | 260 | 0.1267 |
| 1.8511 | 261 | 0.1331 |
| 1.8582 | 262 | 0.1904 |
| 1.8652 | 263 | 0.0912 |
| 1.8723 | 264 | 0.214 |
| 1.8794 | 265 | 0.1846 |
| 1.8865 | 266 | 0.1378 |
| 1.8936 | 267 | 0.1012 |
| 1.9007 | 268 | 0.1468 |
| 1.9078 | 269 | 0.109 |
| 1.9149 | 270 | 0.1136 |
| 1.9220 | 271 | 0.1734 |
| 1.9291 | 272 | 0.0785 |
| 1.9362 | 273 | 0.0388 |
| 1.9433 | 274 | 0.1138 |
| 1.9504 | 275 | 0.0806 |
| 1.9574 | 276 | 0.2819 |
| 1.9645 | 277 | 0.1719 |
| 1.9716 | 278 | 0.0479 |
| 1.9787 | 279 | 0.1038 |
| 1.9858 | 280 | 0.1401 |
| 1.9929 | 281 | 0.1961 |
| 2.0 | 282 | 0.1072 |
| 2.0071 | 283 | 0.1005 |
| 2.0142 | 284 | 0.147 |
| 2.0213 | 285 | 0.1011 |
| 2.0284 | 286 | 0.1304 |
| 2.0355 | 287 | 0.073 |
| 2.0426 | 288 | 0.0952 |
| 2.0496 | 289 | 0.0956 |
| 2.0567 | 290 | 0.1083 |
| 2.0638 | 291 | 0.1101 |
| 2.0709 | 292 | 0.0534 |
| 2.0780 | 293 | 0.0837 |
| 2.0851 | 294 | 0.0966 |
| 2.0922 | 295 | 0.195 |
| 2.0993 | 296 | 0.0608 |
| 2.1064 | 297 | 0.0999 |
| 2.1135 | 298 | 0.1588 |
| 2.1206 | 299 | 0.1283 |
| 2.1277 | 300 | 0.0962 |
| 2.1348 | 301 | 0.0872 |
| 2.1418 | 302 | 0.0793 |
| 2.1489 | 303 | 0.1209 |
| 2.1560 | 304 | 0.1346 |
| 2.1631 | 305 | 0.131 |
| 2.1702 | 306 | 0.1081 |
| 2.1773 | 307 | 0.1109 |
| 2.1844 | 308 | 0.197 |
| 2.1915 | 309 | 0.108 |
| 2.1986 | 310 | 0.1715 |
| 2.2057 | 311 | 0.0654 |
| 2.2128 | 312 | 0.1374 |
| 2.2199 | 313 | 0.0929 |
| 2.2270 | 314 | 0.033 |
| 2.2340 | 315 | 0.0903 |
| 2.2411 | 316 | 0.1417 |
| 2.2482 | 317 | 0.134 |
| 2.2553 | 318 | 0.041 |
| 2.2624 | 319 | 0.0947 |
| 2.2695 | 320 | 0.0655 |
| 2.2766 | 321 | 0.0525 |
| 2.2837 | 322 | 0.0547 |
| 2.2908 | 323 | 0.1342 |
| 2.2979 | 324 | 0.1088 |
| 2.3050 | 325 | 0.162 |
| 2.3121 | 326 | 0.0962 |
| 2.3191 | 327 | 0.154 |
| 2.3262 | 328 | 0.0935 |
| 2.3333 | 329 | 0.1186 |
| 2.3404 | 330 | 0.1192 |
| 2.3475 | 331 | 0.1075 |
| 2.3546 | 332 | 0.12 |
| 2.3617 | 333 | 0.0679 |
| 2.3688 | 334 | 0.1087 |
| 2.3759 | 335 | 0.1493 |
| 2.3830 | 336 | 0.085 |
| 2.3901 | 337 | 0.1784 |
| 2.3972 | 338 | 0.0567 |
| 2.4043 | 339 | 0.1842 |
| 2.4113 | 340 | 0.183 |
| 2.4184 | 341 | 0.1108 |
| 2.4255 | 342 | 0.1405 |
| 2.4326 | 343 | 0.2477 |
| 2.4397 | 344 | 0.2376 |
| 2.4468 | 345 | 0.1469 |
| 2.4539 | 346 | 0.1048 |
| 2.4610 | 347 | 0.1153 |
| 2.4681 | 348 | 0.1167 |
| 2.4752 | 349 | 0.1605 |
| 2.4823 | 350 | 0.1479 |
| 2.4894 | 351 | 0.0684 |
| 2.4965 | 352 | 0.0515 |
| 2.5035 | 353 | 0.1035 |
| 2.5106 | 354 | 0.1488 |
| 2.5177 | 355 | 0.0274 |
| 2.5248 | 356 | 0.0706 |
| 2.5319 | 357 | 0.1541 |
| 2.5390 | 358 | 0.1331 |
| 2.5461 | 359 | 0.0911 |
| 2.5532 | 360 | 0.0606 |
| 2.5603 | 361 | 0.1612 |
| 2.5674 | 362 | 0.2752 |
| 2.5745 | 363 | 0.1436 |
| 2.5816 | 364 | 0.1257 |
| 2.5887 | 365 | 0.1174 |
| 2.5957 | 366 | 0.0415 |
| 2.6028 | 367 | 0.0918 |
| 2.6099 | 368 | 0.0899 |
| 2.6170 | 369 | 0.1136 |
| 2.6241 | 370 | 0.1337 |
| 2.6312 | 371 | 0.1948 |
| 2.6383 | 372 | 0.1482 |
| 2.6454 | 373 | 0.1209 |
| 2.6525 | 374 | 0.1082 |
| 2.6596 | 375 | 0.1948 |
| 2.6667 | 376 | 0.1029 |
| 2.6738 | 377 | 0.0783 |
| 2.6809 | 378 | 0.0844 |
| 2.6879 | 379 | 0.1045 |
| 2.6950 | 380 | 0.0982 |
| 2.7021 | 381 | 0.075 |
| 2.7092 | 382 | 0.15 |
| 2.7163 | 383 | 0.1155 |
| 2.7234 | 384 | 0.1334 |
| 2.7305 | 385 | 0.0767 |
| 2.7376 | 386 | 0.0476 |
| 2.7447 | 387 | 0.068 |
| 2.7518 | 388 | 0.0967 |
| 2.7589 | 389 | 0.0953 |
| 2.7660 | 390 | 0.1307 |
| 2.7730 | 391 | 0.0923 |
| 2.7801 | 392 | 0.1159 |
| 2.7872 | 393 | 0.0769 |
| 2.7943 | 394 | 0.0993 |
| 2.8014 | 395 | 0.1018 |
| 2.8085 | 396 | 0.0783 |
| 2.8156 | 397 | 0.0792 |
| 2.8227 | 398 | 0.0914 |
| 2.8298 | 399 | 0.0821 |
| 2.8369 | 400 | 0.0947 |
| 2.8440 | 401 | 0.0622 |
| 2.8511 | 402 | 0.1858 |
| 2.8582 | 403 | 0.1977 |
| 2.8652 | 404 | 0.0398 |
| 2.8723 | 405 | 0.0784 |
| 2.8794 | 406 | 0.1622 |
| 2.8865 | 407 | 0.1213 |
| 2.8936 | 408 | 0.1867 |
| 2.9007 | 409 | 0.1257 |
| 2.9078 | 410 | 0.1366 |
| 2.9149 | 411 | 0.0983 |
| 2.9220 | 412 | 0.0967 |
| 2.9291 | 413 | 0.0398 |
| 2.9362 | 414 | 0.1582 |
| 2.9433 | 415 | 0.123 |
| 2.9504 | 416 | 0.1768 |
| 2.9574 | 417 | 0.131 |
| 2.9645 | 418 | 0.0731 |
| 2.9716 | 419 | 0.074 |
| 2.9787 | 420 | 0.1176 |
| 2.9858 | 421 | 0.0984 |
| 2.9929 | 422 | 0.0834 |
| 3.0 | 423 | 0.1985 |