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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dense |
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- generated_from_trainer |
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- dataset_size:9020 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: python multiprocessing show cpu count |
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sentences: |
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- "def unique(seq):\n \"\"\"Return the unique elements of a collection even if\ |
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\ those elements are\n unhashable and unsortable, like dicts and sets\"\"\ |
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\"\n cleaned = []\n for each in seq:\n if each not in cleaned:\n\ |
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\ cleaned.append(each)\n return cleaned" |
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- "def is_in(self, point_x, point_y):\n \"\"\" Test if a point is within\ |
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\ this polygonal region \"\"\"\n\n point_array = array(((point_x, point_y),))\n\ |
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\ vertices = array(self.points)\n winding = self.inside_rule ==\ |
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\ \"winding\"\n result = points_in_polygon(point_array, vertices, winding)\n\ |
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\ return result[0]" |
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- "def machine_info():\n \"\"\"Retrieve core and memory information for the current\ |
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\ machine.\n \"\"\"\n import psutil\n BYTES_IN_GIG = 1073741824.0\n \ |
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\ free_bytes = psutil.virtual_memory().total\n return [{\"memory\": float(\"\ |
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%.1f\" % (free_bytes / BYTES_IN_GIG)), \"cores\": multiprocessing.cpu_count(),\n\ |
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\ \"name\": socket.gethostname()}]" |
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- source_sentence: python subplot set the whole title |
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sentences: |
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- "def set_title(self, title, **kwargs):\n \"\"\"Sets the title on the underlying\ |
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\ matplotlib AxesSubplot.\"\"\"\n ax = self.get_axes()\n ax.set_title(title,\ |
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\ **kwargs)" |
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- "def moving_average(array, n=3):\n \"\"\"\n Calculates the moving average\ |
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\ of an array.\n\n Parameters\n ----------\n array : array\n The\ |
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\ array to have the moving average taken of\n n : int\n The number of\ |
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\ points of moving average to take\n \n Returns\n -------\n MovingAverageArray\ |
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\ : array\n The n-point moving average of the input array\n \"\"\"\n\ |
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\ ret = _np.cumsum(array, dtype=float)\n ret[n:] = ret[n:] - ret[:-n]\n\ |
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\ return ret[n - 1:] / n" |
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- "def to_query_parameters(parameters):\n \"\"\"Converts DB-API parameter values\ |
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\ into query parameters.\n\n :type parameters: Mapping[str, Any] or Sequence[Any]\n\ |
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\ :param parameters: A dictionary or sequence of query parameter values.\n\n\ |
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\ :rtype: List[google.cloud.bigquery.query._AbstractQueryParameter]\n :returns:\ |
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\ A list of query parameters.\n \"\"\"\n if parameters is None:\n \ |
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\ return []\n\n if isinstance(parameters, collections_abc.Mapping):\n \ |
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\ return to_query_parameters_dict(parameters)\n\n return to_query_parameters_list(parameters)" |
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- source_sentence: python merge two set to dict |
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sentences: |
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- "def make_regex(separator):\n \"\"\"Utility function to create regexp for matching\ |
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\ escaped separators\n in strings.\n\n \"\"\"\n return re.compile(r'(?:'\ |
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\ + re.escape(separator) + r')?((?:[^' +\n re.escape(separator)\ |
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\ + r'\\\\]|\\\\.)+)')" |
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- "def csvtolist(inputstr):\n \"\"\" converts a csv string into a list \"\"\"\ |
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\n reader = csv.reader([inputstr], skipinitialspace=True)\n output = []\n\ |
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\ for r in reader:\n output += r\n return output" |
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- "def dict_merge(set1, set2):\n \"\"\"Joins two dictionaries.\"\"\"\n return\ |
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\ dict(list(set1.items()) + list(set2.items()))" |
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- source_sentence: python string % substitution float |
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sentences: |
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- "def _configure_logger():\n \"\"\"Configure the logging module.\"\"\"\n \ |
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\ if not app.debug:\n _configure_logger_for_production(logging.getLogger())\n\ |
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\ elif not app.testing:\n _configure_logger_for_debugging(logging.getLogger())" |
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- "def __set__(self, instance, value):\n \"\"\" Set a related object for\ |
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\ an instance. \"\"\"\n\n self.map[id(instance)] = (weakref.ref(instance),\ |
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\ value)" |
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- "def format_float(value): # not used\n \"\"\"Modified form of the 'g' format\ |
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\ specifier.\n \"\"\"\n string = \"{:g}\".format(value).replace(\"e+\",\ |
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\ \"e\")\n string = re.sub(\"e(-?)0*(\\d+)\", r\"e\\1\\2\", string)\n return\ |
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\ string" |
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- source_sentence: bottom 5 rows in python |
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sentences: |
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- "def refresh(self, document):\n\t\t\"\"\" Load a new copy of a document from the\ |
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\ database. does not\n\t\t\treplace the old one \"\"\"\n\t\ttry:\n\t\t\told_cache_size\ |
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\ = 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\ |
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\t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\ |
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\t\treturn obj" |
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- "def table_top_abs(self):\n \"\"\"Returns the absolute position of table\ |
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\ top\"\"\"\n table_height = np.array([0, 0, self.table_full_size[2]])\n\ |
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\ return string_to_array(self.floor.get(\"pos\")) + table_height" |
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- "def get_dimension_array(array):\n \"\"\"\n Get dimension of an array getting\ |
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\ the number of rows and the max num of\n columns.\n \"\"\"\n if all(isinstance(el,\ |
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\ list) for el in array):\n result = [len(array), len(max([x for x in array],\ |
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\ key=len,))]\n\n # elif array and isinstance(array, list):\n else:\n \ |
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\ result = [len(array), 1]\n\n return result" |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Devy1/MiniLM-cosqa-32") |
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# Run inference |
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sentences = [ |
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'bottom 5 rows in python', |
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'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', |
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'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', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[ 1.0000, 0.4728, -0.0350], |
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# [ 0.4728, 1.0000, -0.0494], |
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# [-0.0350, -0.0494, 1.0000]]) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 9,020 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | |
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| 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> | |
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* Samples: |
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| anchor | positive | |
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|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim", |
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"gather_across_devices": false |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 32 |
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- `fp16`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 3 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `parallelism_config`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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- `gradient_checkpointing`: False |
|
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
|
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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|
- `full_determinism`: False |
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- `torchdynamo`: None |
|
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
|
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- `torch_compile`: False |
|
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
|
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- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
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- `use_liger_kernel`: False |
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- `liger_kernel_config`: None |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
|
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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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|
|
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</details> |
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|
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### Training Logs |
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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 0.0035 | 1 | 0.5705 | |
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| 0.0071 | 2 | 0.1217 | |
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| 0.0106 | 3 | 0.1985 | |
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| 0.0142 | 4 | 0.2742 | |
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| 0.0177 | 5 | 0.0782 | |
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| 0.0213 | 6 | 0.1748 | |
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| 0.0248 | 7 | 0.1914 | |
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| 0.0284 | 8 | 0.0911 | |
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| 0.0319 | 9 | 0.0368 | |
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| 0.0355 | 10 | 0.219 | |
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| 0.0390 | 11 | 0.1571 | |
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| 0.0426 | 12 | 0.081 | |
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| 0.0461 | 13 | 0.1152 | |
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| 0.0496 | 14 | 0.0556 | |
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| 0.0532 | 15 | 0.1375 | |
|
|
| 0.0567 | 16 | 0.1844 | |
|
|
| 0.0603 | 17 | 0.3164 | |
|
|
| 0.0638 | 18 | 0.2312 | |
|
|
| 0.0674 | 19 | 0.1767 | |
|
|
| 0.0709 | 20 | 0.0975 | |
|
|
| 0.0745 | 21 | 0.2848 | |
|
|
| 0.0780 | 22 | 0.0972 | |
|
|
| 0.0816 | 23 | 0.3153 | |
|
|
| 0.0851 | 24 | 0.1087 | |
|
|
| 0.0887 | 25 | 0.1673 | |
|
|
| 0.0922 | 26 | 0.2074 | |
|
|
| 0.0957 | 27 | 0.2197 | |
|
|
| 0.0993 | 28 | 0.2571 | |
|
|
| 0.1028 | 29 | 0.1873 | |
|
|
| 0.1064 | 30 | 0.0657 | |
|
|
| 0.1099 | 31 | 0.0675 | |
|
|
| 0.1135 | 32 | 0.0749 | |
|
|
| 0.1170 | 33 | 0.0948 | |
|
|
| 0.1206 | 34 | 0.0849 | |
|
|
| 0.1241 | 35 | 0.0882 | |
|
|
| 0.1277 | 36 | 0.0436 | |
|
|
| 0.1312 | 37 | 0.1173 | |
|
|
| 0.1348 | 38 | 0.1512 | |
|
|
| 0.1383 | 39 | 0.1062 | |
|
|
| 0.1418 | 40 | 0.0384 | |
|
|
| 0.1454 | 41 | 0.148 | |
|
|
| 0.1489 | 42 | 0.0432 | |
|
|
| 0.1525 | 43 | 0.1027 | |
|
|
| 0.1560 | 44 | 0.4193 | |
|
|
| 0.1596 | 45 | 0.1003 | |
|
|
| 0.1631 | 46 | 0.113 | |
|
|
| 0.1667 | 47 | 0.0846 | |
|
|
| 0.1702 | 48 | 0.0899 | |
|
|
| 0.1738 | 49 | 0.0952 | |
|
|
| 0.1773 | 50 | 0.0553 | |
|
|
| 0.1809 | 51 | 0.11 | |
|
|
| 0.1844 | 52 | 0.1955 | |
|
|
| 0.1879 | 53 | 0.1103 | |
|
|
| 0.1915 | 54 | 0.0738 | |
|
|
| 0.1950 | 55 | 0.1217 | |
|
|
| 0.1986 | 56 | 0.274 | |
|
|
| 0.2021 | 57 | 0.1471 | |
|
|
| 0.2057 | 58 | 0.0727 | |
|
|
| 0.2092 | 59 | 0.0438 | |
|
|
| 0.2128 | 60 | 0.1521 | |
|
|
| 0.2163 | 61 | 0.1359 | |
|
|
| 0.2199 | 62 | 0.1217 | |
|
|
| 0.2234 | 63 | 0.2226 | |
|
|
| 0.2270 | 64 | 0.2676 | |
|
|
| 0.2305 | 65 | 0.1649 | |
|
|
| 0.2340 | 66 | 0.1675 | |
|
|
| 0.2376 | 67 | 0.1278 | |
|
|
| 0.2411 | 68 | 0.1627 | |
|
|
| 0.2447 | 69 | 0.108 | |
|
|
| 0.2482 | 70 | 0.1327 | |
|
|
| 0.2518 | 71 | 0.1762 | |
|
|
| 0.2553 | 72 | 0.41 | |
|
|
| 0.2589 | 73 | 0.1551 | |
|
|
| 0.2624 | 74 | 0.1893 | |
|
|
| 0.2660 | 75 | 0.0847 | |
|
|
| 0.2695 | 76 | 0.0949 | |
|
|
| 0.2730 | 77 | 0.2214 | |
|
|
| 0.2766 | 78 | 0.0439 | |
|
|
| 0.2801 | 79 | 0.1355 | |
|
|
| 0.2837 | 80 | 0.1951 | |
|
|
| 0.2872 | 81 | 0.068 | |
|
|
| 0.2908 | 82 | 0.1032 | |
|
|
| 0.2943 | 83 | 0.1131 | |
|
|
| 0.2979 | 84 | 0.2245 | |
|
|
| 0.3014 | 85 | 0.2323 | |
|
|
| 0.3050 | 86 | 0.1512 | |
|
|
| 0.3085 | 87 | 0.1686 | |
|
|
| 0.3121 | 88 | 0.0797 | |
|
|
| 0.3156 | 89 | 0.2182 | |
|
|
| 0.3191 | 90 | 0.2181 | |
|
|
| 0.3227 | 91 | 0.0944 | |
|
|
| 0.3262 | 92 | 0.083 | |
|
|
| 0.3298 | 93 | 0.1554 | |
|
|
| 0.3333 | 94 | 0.0999 | |
|
|
| 0.3369 | 95 | 0.1948 | |
|
|
| 0.3404 | 96 | 0.1446 | |
|
|
| 0.3440 | 97 | 0.2856 | |
|
|
| 0.3475 | 98 | 0.0786 | |
|
|
| 0.3511 | 99 | 0.1112 | |
|
|
| 0.3546 | 100 | 0.0571 | |
|
|
| 0.3582 | 101 | 0.2553 | |
|
|
| 0.3617 | 102 | 0.0546 | |
|
|
| 0.3652 | 103 | 0.1948 | |
|
|
| 0.3688 | 104 | 0.0945 | |
|
|
| 0.3723 | 105 | 0.0973 | |
|
|
| 0.3759 | 106 | 0.0478 | |
|
|
| 0.3794 | 107 | 0.3652 | |
|
|
| 0.3830 | 108 | 0.2676 | |
|
|
| 0.3865 | 109 | 0.1216 | |
|
|
| 0.3901 | 110 | 0.0701 | |
|
|
| 0.3936 | 111 | 0.0918 | |
|
|
| 0.3972 | 112 | 0.1813 | |
|
|
| 0.4007 | 113 | 0.1243 | |
|
|
| 0.4043 | 114 | 0.2819 | |
|
|
| 0.4078 | 115 | 0.0103 | |
|
|
| 0.4113 | 116 | 0.2099 | |
|
|
| 0.4149 | 117 | 0.0879 | |
|
|
| 0.4184 | 118 | 0.1614 | |
|
|
| 0.4220 | 119 | 0.0869 | |
|
|
| 0.4255 | 120 | 0.0942 | |
|
|
| 0.4291 | 121 | 0.0592 | |
|
|
| 0.4326 | 122 | 0.1387 | |
|
|
| 0.4362 | 123 | 0.0805 | |
|
|
| 0.4397 | 124 | 0.1844 | |
|
|
| 0.4433 | 125 | 0.0292 | |
|
|
| 0.4468 | 126 | 0.3999 | |
|
|
| 0.4504 | 127 | 0.1031 | |
|
|
| 0.4539 | 128 | 0.3445 | |
|
|
| 0.4574 | 129 | 0.2309 | |
|
|
| 0.4610 | 130 | 0.1887 | |
|
|
| 0.4645 | 131 | 0.2472 | |
|
|
| 0.4681 | 132 | 0.1128 | |
|
|
| 0.4716 | 133 | 0.1276 | |
|
|
| 0.4752 | 134 | 0.1141 | |
|
|
| 0.4787 | 135 | 0.1117 | |
|
|
| 0.4823 | 136 | 0.1593 | |
|
|
| 0.4858 | 137 | 0.0363 | |
|
|
| 0.4894 | 138 | 0.1564 | |
|
|
| 0.4929 | 139 | 0.21 | |
|
|
| 0.4965 | 140 | 0.2024 | |
|
|
| 0.5 | 141 | 0.1785 | |
|
|
| 0.5035 | 142 | 0.1456 | |
|
|
| 0.5071 | 143 | 0.0986 | |
|
|
| 0.5106 | 144 | 0.1947 | |
|
|
| 0.5142 | 145 | 0.1733 | |
|
|
| 0.5177 | 146 | 0.1656 | |
|
|
| 0.5213 | 147 | 0.0951 | |
|
|
| 0.5248 | 148 | 0.1216 | |
|
|
| 0.5284 | 149 | 0.0875 | |
|
|
| 0.5319 | 150 | 0.1284 | |
|
|
| 0.5355 | 151 | 0.1066 | |
|
|
| 0.5390 | 152 | 0.0692 | |
|
|
| 0.5426 | 153 | 0.2287 | |
|
|
| 0.5461 | 154 | 0.233 | |
|
|
| 0.5496 | 155 | 0.1066 | |
|
|
| 0.5532 | 156 | 0.0862 | |
|
|
| 0.5567 | 157 | 0.0877 | |
|
|
| 0.5603 | 158 | 0.3095 | |
|
|
| 0.5638 | 159 | 0.1237 | |
|
|
| 0.5674 | 160 | 0.0682 | |
|
|
| 0.5709 | 161 | 0.0741 | |
|
|
| 0.5745 | 162 | 0.2003 | |
|
|
| 0.5780 | 163 | 0.1392 | |
|
|
| 0.5816 | 164 | 0.0493 | |
|
|
| 0.5851 | 165 | 0.3129 | |
|
|
| 0.5887 | 166 | 0.1186 | |
|
|
| 0.5922 | 167 | 0.0369 | |
|
|
| 0.5957 | 168 | 0.1224 | |
|
|
| 0.5993 | 169 | 0.2212 | |
|
|
| 0.6028 | 170 | 0.0809 | |
|
|
| 0.6064 | 171 | 0.116 | |
|
|
| 0.6099 | 172 | 0.2251 | |
|
|
| 0.6135 | 173 | 0.0195 | |
|
|
| 0.6170 | 174 | 0.0476 | |
|
|
| 0.6206 | 175 | 0.0818 | |
|
|
| 0.6241 | 176 | 0.0313 | |
|
|
| 0.6277 | 177 | 0.188 | |
|
|
| 0.6312 | 178 | 0.2736 | |
|
|
| 0.6348 | 179 | 0.1444 | |
|
|
| 0.6383 | 180 | 0.0924 | |
|
|
| 0.6418 | 181 | 0.0895 | |
|
|
| 0.6454 | 182 | 0.2116 | |
|
|
| 0.6489 | 183 | 0.3288 | |
|
|
| 0.6525 | 184 | 0.1659 | |
|
|
| 0.6560 | 185 | 0.1367 | |
|
|
| 0.6596 | 186 | 0.1834 | |
|
|
| 0.6631 | 187 | 0.0822 | |
|
|
| 0.6667 | 188 | 0.1384 | |
|
|
| 0.6702 | 189 | 0.1602 | |
|
|
| 0.6738 | 190 | 0.1325 | |
|
|
| 0.6773 | 191 | 0.1033 | |
|
|
| 0.6809 | 192 | 0.1102 | |
|
|
| 0.6844 | 193 | 0.0786 | |
|
|
| 0.6879 | 194 | 0.1158 | |
|
|
| 0.6915 | 195 | 0.0639 | |
|
|
| 0.6950 | 196 | 0.18 | |
|
|
| 0.6986 | 197 | 0.0512 | |
|
|
| 0.7021 | 198 | 0.1271 | |
|
|
| 0.7057 | 199 | 0.0839 | |
|
|
| 0.7092 | 200 | 0.0838 | |
|
|
| 0.7128 | 201 | 0.0691 | |
|
|
| 0.7163 | 202 | 0.1457 | |
|
|
| 0.7199 | 203 | 0.1363 | |
|
|
| 0.7234 | 204 | 0.1059 | |
|
|
| 0.7270 | 205 | 0.1051 | |
|
|
| 0.7305 | 206 | 0.0541 | |
|
|
| 0.7340 | 207 | 0.1409 | |
|
|
| 0.7376 | 208 | 0.0911 | |
|
|
| 0.7411 | 209 | 0.2823 | |
|
|
| 0.7447 | 210 | 0.156 | |
|
|
| 0.7482 | 211 | 0.394 | |
|
|
| 0.7518 | 212 | 0.1946 | |
|
|
| 0.7553 | 213 | 0.0282 | |
|
|
| 0.7589 | 214 | 0.1497 | |
|
|
| 0.7624 | 215 | 0.1643 | |
|
|
| 0.7660 | 216 | 0.0236 | |
|
|
| 0.7695 | 217 | 0.0654 | |
|
|
| 0.7730 | 218 | 0.0537 | |
|
|
| 0.7766 | 219 | 0.1068 | |
|
|
| 0.7801 | 220 | 0.051 | |
|
|
| 0.7837 | 221 | 0.072 | |
|
|
| 0.7872 | 222 | 0.0413 | |
|
|
| 0.7908 | 223 | 0.0918 | |
|
|
| 0.7943 | 224 | 0.1308 | |
|
|
| 0.7979 | 225 | 0.0694 | |
|
|
| 0.8014 | 226 | 0.0852 | |
|
|
| 0.8050 | 227 | 0.0321 | |
|
|
| 0.8085 | 228 | 0.1497 | |
|
|
| 0.8121 | 229 | 0.0959 | |
|
|
| 0.8156 | 230 | 0.226 | |
|
|
| 0.8191 | 231 | 0.1129 | |
|
|
| 0.8227 | 232 | 0.0831 | |
|
|
| 0.8262 | 233 | 0.2181 | |
|
|
| 0.8298 | 234 | 0.1054 | |
|
|
| 0.8333 | 235 | 0.1812 | |
|
|
| 0.8369 | 236 | 0.0455 | |
|
|
| 0.8404 | 237 | 0.1413 | |
|
|
| 0.8440 | 238 | 0.0801 | |
|
|
| 0.8475 | 239 | 0.0301 | |
|
|
| 0.8511 | 240 | 0.0846 | |
|
|
| 0.8546 | 241 | 0.1862 | |
|
|
| 0.8582 | 242 | 0.1015 | |
|
|
| 0.8617 | 243 | 0.0459 | |
|
|
| 0.8652 | 244 | 0.0774 | |
|
|
| 0.8688 | 245 | 0.1444 | |
|
|
| 0.8723 | 246 | 0.2849 | |
|
|
| 0.8759 | 247 | 0.3935 | |
|
|
| 0.8794 | 248 | 0.2126 | |
|
|
| 0.8830 | 249 | 0.0845 | |
|
|
| 0.8865 | 250 | 0.1429 | |
|
|
| 0.8901 | 251 | 0.0107 | |
|
|
| 0.8936 | 252 | 0.0599 | |
|
|
| 0.8972 | 253 | 0.1192 | |
|
|
| 0.9007 | 254 | 0.1369 | |
|
|
| 0.9043 | 255 | 0.1246 | |
|
|
| 0.9078 | 256 | 0.0163 | |
|
|
| 0.9113 | 257 | 0.1844 | |
|
|
| 0.9149 | 258 | 0.1017 | |
|
|
| 0.9184 | 259 | 0.0415 | |
|
|
| 0.9220 | 260 | 0.1658 | |
|
|
| 0.9255 | 261 | 0.0755 | |
|
|
| 0.9291 | 262 | 0.086 | |
|
|
| 0.9326 | 263 | 0.081 | |
|
|
| 0.9362 | 264 | 0.2776 | |
|
|
| 0.9397 | 265 | 0.1284 | |
|
|
| 0.9433 | 266 | 0.1591 | |
|
|
| 0.9468 | 267 | 0.1397 | |
|
|
| 0.9504 | 268 | 0.0334 | |
|
|
| 0.9539 | 269 | 0.0449 | |
|
|
| 0.9574 | 270 | 0.1382 | |
|
|
| 0.9610 | 271 | 0.1736 | |
|
|
| 0.9645 | 272 | 0.236 | |
|
|
| 0.9681 | 273 | 0.225 | |
|
|
| 0.9716 | 274 | 0.2444 | |
|
|
| 0.9752 | 275 | 0.0497 | |
|
|
| 0.9787 | 276 | 0.1212 | |
|
|
| 0.9823 | 277 | 0.1405 | |
|
|
| 0.9858 | 278 | 0.1116 | |
|
|
| 0.9894 | 279 | 0.0369 | |
|
|
| 0.9929 | 280 | 0.0321 | |
|
|
| 0.9965 | 281 | 0.1481 | |
|
|
| 1.0 | 282 | 0.1046 | |
|
|
| 1.0035 | 283 | 0.0673 | |
|
|
| 1.0071 | 284 | 0.078 | |
|
|
| 1.0106 | 285 | 0.0723 | |
|
|
| 1.0142 | 286 | 0.1328 | |
|
|
| 1.0177 | 287 | 0.1399 | |
|
|
| 1.0213 | 288 | 0.186 | |
|
|
| 1.0248 | 289 | 0.0747 | |
|
|
| 1.0284 | 290 | 0.0291 | |
|
|
| 1.0319 | 291 | 0.0427 | |
|
|
| 1.0355 | 292 | 0.0288 | |
|
|
| 1.0390 | 293 | 0.1552 | |
|
|
| 1.0426 | 294 | 0.0123 | |
|
|
| 1.0461 | 295 | 0.0617 | |
|
|
| 1.0496 | 296 | 0.0646 | |
|
|
| 1.0532 | 297 | 0.2001 | |
|
|
| 1.0567 | 298 | 0.068 | |
|
|
| 1.0603 | 299 | 0.0108 | |
|
|
| 1.0638 | 300 | 0.0776 | |
|
|
| 1.0674 | 301 | 0.1037 | |
|
|
| 1.0709 | 302 | 0.0087 | |
|
|
| 1.0745 | 303 | 0.1564 | |
|
|
| 1.0780 | 304 | 0.0665 | |
|
|
| 1.0816 | 305 | 0.0246 | |
|
|
| 1.0851 | 306 | 0.061 | |
|
|
| 1.0887 | 307 | 0.038 | |
|
|
| 1.0922 | 308 | 0.1016 | |
|
|
| 1.0957 | 309 | 0.0434 | |
|
|
| 1.0993 | 310 | 0.1178 | |
|
|
| 1.1028 | 311 | 0.1235 | |
|
|
| 1.1064 | 312 | 0.0164 | |
|
|
| 1.1099 | 313 | 0.0838 | |
|
|
| 1.1135 | 314 | 0.0516 | |
|
|
| 1.1170 | 315 | 0.1195 | |
|
|
| 1.1206 | 316 | 0.1026 | |
|
|
| 1.1241 | 317 | 0.0387 | |
|
|
| 1.1277 | 318 | 0.1057 | |
|
|
| 1.1312 | 319 | 0.0332 | |
|
|
| 1.1348 | 320 | 0.033 | |
|
|
| 1.1383 | 321 | 0.0648 | |
|
|
| 1.1418 | 322 | 0.0067 | |
|
|
| 1.1454 | 323 | 0.0402 | |
|
|
| 1.1489 | 324 | 0.1376 | |
|
|
| 1.1525 | 325 | 0.0852 | |
|
|
| 1.1560 | 326 | 0.0245 | |
|
|
| 1.1596 | 327 | 0.087 | |
|
|
| 1.1631 | 328 | 0.0403 | |
|
|
| 1.1667 | 329 | 0.0998 | |
|
|
| 1.1702 | 330 | 0.0634 | |
|
|
| 1.1738 | 331 | 0.0218 | |
|
|
| 1.1773 | 332 | 0.1244 | |
|
|
| 1.1809 | 333 | 0.1178 | |
|
|
| 1.1844 | 334 | 0.1135 | |
|
|
| 1.1879 | 335 | 0.0721 | |
|
|
| 1.1915 | 336 | 0.0427 | |
|
|
| 1.1950 | 337 | 0.0314 | |
|
|
| 1.1986 | 338 | 0.0577 | |
|
|
| 1.2021 | 339 | 0.0337 | |
|
|
| 1.2057 | 340 | 0.0312 | |
|
|
| 1.2092 | 341 | 0.0336 | |
|
|
| 1.2128 | 342 | 0.0289 | |
|
|
| 1.2163 | 343 | 0.0946 | |
|
|
| 1.2199 | 344 | 0.2581 | |
|
|
| 1.2234 | 345 | 0.1359 | |
|
|
| 1.2270 | 346 | 0.0223 | |
|
|
| 1.2305 | 347 | 0.055 | |
|
|
| 1.2340 | 348 | 0.0591 | |
|
|
| 1.2376 | 349 | 0.0286 | |
|
|
| 1.2411 | 350 | 0.0128 | |
|
|
| 1.2447 | 351 | 0.0676 | |
|
|
| 1.2482 | 352 | 0.0744 | |
|
|
| 1.2518 | 353 | 0.0208 | |
|
|
| 1.2553 | 354 | 0.0877 | |
|
|
| 1.2589 | 355 | 0.0759 | |
|
|
| 1.2624 | 356 | 0.052 | |
|
|
| 1.2660 | 357 | 0.2666 | |
|
|
| 1.2695 | 358 | 0.0455 | |
|
|
| 1.2730 | 359 | 0.0893 | |
|
|
| 1.2766 | 360 | 0.1706 | |
|
|
| 1.2801 | 361 | 0.059 | |
|
|
| 1.2837 | 362 | 0.049 | |
|
|
| 1.2872 | 363 | 0.1249 | |
|
|
| 1.2908 | 364 | 0.0229 | |
|
|
| 1.2943 | 365 | 0.1088 | |
|
|
| 1.2979 | 366 | 0.198 | |
|
|
| 1.3014 | 367 | 0.2119 | |
|
|
| 1.3050 | 368 | 0.0397 | |
|
|
| 1.3085 | 369 | 0.1772 | |
|
|
| 1.3121 | 370 | 0.1251 | |
|
|
| 1.3156 | 371 | 0.0286 | |
|
|
| 1.3191 | 372 | 0.0273 | |
|
|
| 1.3227 | 373 | 0.1161 | |
|
|
| 1.3262 | 374 | 0.1128 | |
|
|
| 1.3298 | 375 | 0.1323 | |
|
|
| 1.3333 | 376 | 0.0245 | |
|
|
| 1.3369 | 377 | 0.0342 | |
|
|
| 1.3404 | 378 | 0.1177 | |
|
|
| 1.3440 | 379 | 0.0584 | |
|
|
| 1.3475 | 380 | 0.0164 | |
|
|
| 1.3511 | 381 | 0.1174 | |
|
|
| 1.3546 | 382 | 0.043 | |
|
|
| 1.3582 | 383 | 0.0706 | |
|
|
| 1.3617 | 384 | 0.0862 | |
|
|
| 1.3652 | 385 | 0.1093 | |
|
|
| 1.3688 | 386 | 0.0849 | |
|
|
| 1.3723 | 387 | 0.0252 | |
|
|
| 1.3759 | 388 | 0.0517 | |
|
|
| 1.3794 | 389 | 0.0634 | |
|
|
| 1.3830 | 390 | 0.0526 | |
|
|
| 1.3865 | 391 | 0.1388 | |
|
|
| 1.3901 | 392 | 0.0747 | |
|
|
| 1.3936 | 393 | 0.0362 | |
|
|
| 1.3972 | 394 | 0.1148 | |
|
|
| 1.4007 | 395 | 0.0208 | |
|
|
| 1.4043 | 396 | 0.1426 | |
|
|
| 1.4078 | 397 | 0.1611 | |
|
|
| 1.4113 | 398 | 0.0302 | |
|
|
| 1.4149 | 399 | 0.0446 | |
|
|
| 1.4184 | 400 | 0.0182 | |
|
|
| 1.4220 | 401 | 0.089 | |
|
|
| 1.4255 | 402 | 0.1423 | |
|
|
| 1.4291 | 403 | 0.1599 | |
|
|
| 1.4326 | 404 | 0.0438 | |
|
|
| 1.4362 | 405 | 0.0103 | |
|
|
| 1.4397 | 406 | 0.083 | |
|
|
| 1.4433 | 407 | 0.0914 | |
|
|
| 1.4468 | 408 | 0.0436 | |
|
|
| 1.4504 | 409 | 0.124 | |
|
|
| 1.4539 | 410 | 0.0896 | |
|
|
| 1.4574 | 411 | 0.256 | |
|
|
| 1.4610 | 412 | 0.0061 | |
|
|
| 1.4645 | 413 | 0.0529 | |
|
|
| 1.4681 | 414 | 0.0851 | |
|
|
| 1.4716 | 415 | 0.08 | |
|
|
| 1.4752 | 416 | 0.0115 | |
|
|
| 1.4787 | 417 | 0.0784 | |
|
|
| 1.4823 | 418 | 0.0321 | |
|
|
| 1.4858 | 419 | 0.0976 | |
|
|
| 1.4894 | 420 | 0.0725 | |
|
|
| 1.4929 | 421 | 0.0834 | |
|
|
| 1.4965 | 422 | 0.122 | |
|
|
| 1.5 | 423 | 0.1294 | |
|
|
| 1.5035 | 424 | 0.2754 | |
|
|
| 1.5071 | 425 | 0.0884 | |
|
|
| 1.5106 | 426 | 0.076 | |
|
|
| 1.5142 | 427 | 0.0799 | |
|
|
| 1.5177 | 428 | 0.0439 | |
|
|
| 1.5213 | 429 | 0.0943 | |
|
|
| 1.5248 | 430 | 0.077 | |
|
|
| 1.5284 | 431 | 0.0696 | |
|
|
| 1.5319 | 432 | 0.0251 | |
|
|
| 1.5355 | 433 | 0.1715 | |
|
|
| 1.5390 | 434 | 0.0913 | |
|
|
| 1.5426 | 435 | 0.0251 | |
|
|
| 1.5461 | 436 | 0.0642 | |
|
|
| 1.5496 | 437 | 0.0375 | |
|
|
| 1.5532 | 438 | 0.0381 | |
|
|
| 1.5567 | 439 | 0.0628 | |
|
|
| 1.5603 | 440 | 0.095 | |
|
|
| 1.5638 | 441 | 0.0441 | |
|
|
| 1.5674 | 442 | 0.0496 | |
|
|
| 1.5709 | 443 | 0.0531 | |
|
|
| 1.5745 | 444 | 0.0304 | |
|
|
| 1.5780 | 445 | 0.2032 | |
|
|
| 1.5816 | 446 | 0.109 | |
|
|
| 1.5851 | 447 | 0.1481 | |
|
|
| 1.5887 | 448 | 0.0706 | |
|
|
| 1.5922 | 449 | 0.0346 | |
|
|
| 1.5957 | 450 | 0.0364 | |
|
|
| 1.5993 | 451 | 0.0513 | |
|
|
| 1.6028 | 452 | 0.3153 | |
|
|
| 1.6064 | 453 | 0.1135 | |
|
|
| 1.6099 | 454 | 0.1034 | |
|
|
| 1.6135 | 455 | 0.0566 | |
|
|
| 1.6170 | 456 | 0.0707 | |
|
|
| 1.6206 | 457 | 0.1564 | |
|
|
| 1.6241 | 458 | 0.1602 | |
|
|
| 1.6277 | 459 | 0.0149 | |
|
|
| 1.6312 | 460 | 0.1243 | |
|
|
| 1.6348 | 461 | 0.0579 | |
|
|
| 1.6383 | 462 | 0.1693 | |
|
|
| 1.6418 | 463 | 0.0911 | |
|
|
| 1.6454 | 464 | 0.0278 | |
|
|
| 1.6489 | 465 | 0.0315 | |
|
|
| 1.6525 | 466 | 0.0176 | |
|
|
| 1.6560 | 467 | 0.1197 | |
|
|
| 1.6596 | 468 | 0.0162 | |
|
|
| 1.6631 | 469 | 0.0492 | |
|
|
| 1.6667 | 470 | 0.0495 | |
|
|
| 1.6702 | 471 | 0.0318 | |
|
|
| 1.6738 | 472 | 0.0703 | |
|
|
| 1.6773 | 473 | 0.0175 | |
|
|
| 1.6809 | 474 | 0.1457 | |
|
|
| 1.6844 | 475 | 0.026 | |
|
|
| 1.6879 | 476 | 0.067 | |
|
|
| 1.6915 | 477 | 0.0657 | |
|
|
| 1.6950 | 478 | 0.1421 | |
|
|
| 1.6986 | 479 | 0.0341 | |
|
|
| 1.7021 | 480 | 0.022 | |
|
|
| 1.7057 | 481 | 0.0641 | |
|
|
| 1.7092 | 482 | 0.1315 | |
|
|
| 1.7128 | 483 | 0.0328 | |
|
|
| 1.7163 | 484 | 0.0489 | |
|
|
| 1.7199 | 485 | 0.0199 | |
|
|
| 1.7234 | 486 | 0.0475 | |
|
|
| 1.7270 | 487 | 0.0662 | |
|
|
| 1.7305 | 488 | 0.0133 | |
|
|
| 1.7340 | 489 | 0.0081 | |
|
|
| 1.7376 | 490 | 0.0356 | |
|
|
| 1.7411 | 491 | 0.092 | |
|
|
| 1.7447 | 492 | 0.0653 | |
|
|
| 1.7482 | 493 | 0.0457 | |
|
|
| 1.7518 | 494 | 0.0949 | |
|
|
| 1.7553 | 495 | 0.0108 | |
|
|
| 1.7589 | 496 | 0.0287 | |
|
|
| 1.7624 | 497 | 0.1043 | |
|
|
| 1.7660 | 498 | 0.0166 | |
|
|
| 1.7695 | 499 | 0.0068 | |
|
|
| 1.7730 | 500 | 0.1521 | |
|
|
| 1.7766 | 501 | 0.0356 | |
|
|
| 1.7801 | 502 | 0.0083 | |
|
|
| 1.7837 | 503 | 0.1221 | |
|
|
| 1.7872 | 504 | 0.046 | |
|
|
| 1.7908 | 505 | 0.0339 | |
|
|
| 1.7943 | 506 | 0.021 | |
|
|
| 1.7979 | 507 | 0.1706 | |
|
|
| 1.8014 | 508 | 0.0176 | |
|
|
| 1.8050 | 509 | 0.0275 | |
|
|
| 1.8085 | 510 | 0.0521 | |
|
|
| 1.8121 | 511 | 0.1083 | |
|
|
| 1.8156 | 512 | 0.098 | |
|
|
| 1.8191 | 513 | 0.0746 | |
|
|
| 1.8227 | 514 | 0.0944 | |
|
|
| 1.8262 | 515 | 0.075 | |
|
|
| 1.8298 | 516 | 0.0997 | |
|
|
| 1.8333 | 517 | 0.0416 | |
|
|
| 1.8369 | 518 | 0.154 | |
|
|
| 1.8404 | 519 | 0.1534 | |
|
|
| 1.8440 | 520 | 0.0387 | |
|
|
| 1.8475 | 521 | 0.0957 | |
|
|
| 1.8511 | 522 | 0.0136 | |
|
|
| 1.8546 | 523 | 0.0426 | |
|
|
| 1.8582 | 524 | 0.1499 | |
|
|
| 1.8617 | 525 | 0.0111 | |
|
|
| 1.8652 | 526 | 0.122 | |
|
|
| 1.8688 | 527 | 0.2204 | |
|
|
| 1.8723 | 528 | 0.1677 | |
|
|
| 1.8759 | 529 | 0.0298 | |
|
|
| 1.8794 | 530 | 0.0873 | |
|
|
| 1.8830 | 531 | 0.0747 | |
|
|
| 1.8865 | 532 | 0.0849 | |
|
|
| 1.8901 | 533 | 0.0525 | |
|
|
| 1.8936 | 534 | 0.0233 | |
|
|
| 1.8972 | 535 | 0.0805 | |
|
|
| 1.9007 | 536 | 0.0236 | |
|
|
| 1.9043 | 537 | 0.142 | |
|
|
| 1.9078 | 538 | 0.0585 | |
|
|
| 1.9113 | 539 | 0.0271 | |
|
|
| 1.9149 | 540 | 0.1606 | |
|
|
| 1.9184 | 541 | 0.2148 | |
|
|
| 1.9220 | 542 | 0.0568 | |
|
|
| 1.9255 | 543 | 0.0248 | |
|
|
| 1.9291 | 544 | 0.0878 | |
|
|
| 1.9326 | 545 | 0.0044 | |
|
|
| 1.9362 | 546 | 0.0354 | |
|
|
| 1.9397 | 547 | 0.0638 | |
|
|
| 1.9433 | 548 | 0.1875 | |
|
|
| 1.9468 | 549 | 0.031 | |
|
|
| 1.9504 | 550 | 0.0547 | |
|
|
| 1.9539 | 551 | 0.1292 | |
|
|
| 1.9574 | 552 | 0.23 | |
|
|
| 1.9610 | 553 | 0.0913 | |
|
|
| 1.9645 | 554 | 0.0561 | |
|
|
| 1.9681 | 555 | 0.0189 | |
|
|
| 1.9716 | 556 | 0.0177 | |
|
|
| 1.9752 | 557 | 0.0195 | |
|
|
| 1.9787 | 558 | 0.1032 | |
|
|
| 1.9823 | 559 | 0.1502 | |
|
|
| 1.9858 | 560 | 0.0457 | |
|
|
| 1.9894 | 561 | 0.0577 | |
|
|
| 1.9929 | 562 | 0.1172 | |
|
|
| 1.9965 | 563 | 0.0504 | |
|
|
| 2.0 | 564 | 0.0374 | |
|
|
| 2.0035 | 565 | 0.1079 | |
|
|
| 2.0071 | 566 | 0.0609 | |
|
|
| 2.0106 | 567 | 0.0366 | |
|
|
| 2.0142 | 568 | 0.0674 | |
|
|
| 2.0177 | 569 | 0.1084 | |
|
|
| 2.0213 | 570 | 0.066 | |
|
|
| 2.0248 | 571 | 0.0102 | |
|
|
| 2.0284 | 572 | 0.0876 | |
|
|
| 2.0319 | 573 | 0.0407 | |
|
|
| 2.0355 | 574 | 0.0581 | |
|
|
| 2.0390 | 575 | 0.1215 | |
|
|
| 2.0426 | 576 | 0.0068 | |
|
|
| 2.0461 | 577 | 0.1015 | |
|
|
| 2.0496 | 578 | 0.0047 | |
|
|
| 2.0532 | 579 | 0.0925 | |
|
|
| 2.0567 | 580 | 0.0836 | |
|
|
| 2.0603 | 581 | 0.021 | |
|
|
| 2.0638 | 582 | 0.0209 | |
|
|
| 2.0674 | 583 | 0.0702 | |
|
|
| 2.0709 | 584 | 0.0117 | |
|
|
| 2.0745 | 585 | 0.0517 | |
|
|
| 2.0780 | 586 | 0.061 | |
|
|
| 2.0816 | 587 | 0.0207 | |
|
|
| 2.0851 | 588 | 0.034 | |
|
|
| 2.0887 | 589 | 0.1045 | |
|
|
| 2.0922 | 590 | 0.03 | |
|
|
| 2.0957 | 591 | 0.0081 | |
|
|
| 2.0993 | 592 | 0.0234 | |
|
|
| 2.1028 | 593 | 0.073 | |
|
|
| 2.1064 | 594 | 0.0074 | |
|
|
| 2.1099 | 595 | 0.0655 | |
|
|
| 2.1135 | 596 | 0.079 | |
|
|
| 2.1170 | 597 | 0.0358 | |
|
|
| 2.1206 | 598 | 0.1006 | |
|
|
| 2.1241 | 599 | 0.0624 | |
|
|
| 2.1277 | 600 | 0.0479 | |
|
|
| 2.1312 | 601 | 0.0105 | |
|
|
| 2.1348 | 602 | 0.0448 | |
|
|
| 2.1383 | 603 | 0.0305 | |
|
|
| 2.1418 | 604 | 0.0432 | |
|
|
| 2.1454 | 605 | 0.0771 | |
|
|
| 2.1489 | 606 | 0.0545 | |
|
|
| 2.1525 | 607 | 0.0299 | |
|
|
| 2.1560 | 608 | 0.0712 | |
|
|
| 2.1596 | 609 | 0.1006 | |
|
|
| 2.1631 | 610 | 0.0117 | |
|
|
| 2.1667 | 611 | 0.0462 | |
|
|
| 2.1702 | 612 | 0.0576 | |
|
|
| 2.1738 | 613 | 0.0696 | |
|
|
| 2.1773 | 614 | 0.0685 | |
|
|
| 2.1809 | 615 | 0.0596 | |
|
|
| 2.1844 | 616 | 0.0127 | |
|
|
| 2.1879 | 617 | 0.0089 | |
|
|
| 2.1915 | 618 | 0.0135 | |
|
|
| 2.1950 | 619 | 0.2405 | |
|
|
| 2.1986 | 620 | 0.0212 | |
|
|
| 2.2021 | 621 | 0.0637 | |
|
|
| 2.2057 | 622 | 0.1356 | |
|
|
| 2.2092 | 623 | 0.0943 | |
|
|
| 2.2128 | 624 | 0.0147 | |
|
|
| 2.2163 | 625 | 0.0038 | |
|
|
| 2.2199 | 626 | 0.0624 | |
|
|
| 2.2234 | 627 | 0.016 | |
|
|
| 2.2270 | 628 | 0.032 | |
|
|
| 2.2305 | 629 | 0.0154 | |
|
|
| 2.2340 | 630 | 0.0724 | |
|
|
| 2.2376 | 631 | 0.008 | |
|
|
| 2.2411 | 632 | 0.0877 | |
|
|
| 2.2447 | 633 | 0.0228 | |
|
|
| 2.2482 | 634 | 0.1929 | |
|
|
| 2.2518 | 635 | 0.026 | |
|
|
| 2.2553 | 636 | 0.0117 | |
|
|
| 2.2589 | 637 | 0.0325 | |
|
|
| 2.2624 | 638 | 0.0127 | |
|
|
| 2.2660 | 639 | 0.0054 | |
|
|
| 2.2695 | 640 | 0.0909 | |
|
|
| 2.2730 | 641 | 0.0326 | |
|
|
| 2.2766 | 642 | 0.0291 | |
|
|
| 2.2801 | 643 | 0.0499 | |
|
|
| 2.2837 | 644 | 0.0394 | |
|
|
| 2.2872 | 645 | 0.0422 | |
|
|
| 2.2908 | 646 | 0.0156 | |
|
|
| 2.2943 | 647 | 0.0626 | |
|
|
| 2.2979 | 648 | 0.0143 | |
|
|
| 2.3014 | 649 | 0.0707 | |
|
|
| 2.3050 | 650 | 0.0474 | |
|
|
| 2.3085 | 651 | 0.0387 | |
|
|
| 2.3121 | 652 | 0.104 | |
|
|
| 2.3156 | 653 | 0.0981 | |
|
|
| 2.3191 | 654 | 0.0284 | |
|
|
| 2.3227 | 655 | 0.0123 | |
|
|
| 2.3262 | 656 | 0.1346 | |
|
|
| 2.3298 | 657 | 0.0157 | |
|
|
| 2.3333 | 658 | 0.1276 | |
|
|
| 2.3369 | 659 | 0.0634 | |
|
|
| 2.3404 | 660 | 0.0327 | |
|
|
| 2.3440 | 661 | 0.0633 | |
|
|
| 2.3475 | 662 | 0.0618 | |
|
|
| 2.3511 | 663 | 0.0171 | |
|
|
| 2.3546 | 664 | 0.141 | |
|
|
| 2.3582 | 665 | 0.0626 | |
|
|
| 2.3617 | 666 | 0.0149 | |
|
|
| 2.3652 | 667 | 0.0455 | |
|
|
| 2.3688 | 668 | 0.0507 | |
|
|
| 2.3723 | 669 | 0.0492 | |
|
|
| 2.3759 | 670 | 0.1528 | |
|
|
| 2.3794 | 671 | 0.0484 | |
|
|
| 2.3830 | 672 | 0.0826 | |
|
|
| 2.3865 | 673 | 0.044 | |
|
|
| 2.3901 | 674 | 0.2045 | |
|
|
| 2.3936 | 675 | 0.0083 | |
|
|
| 2.3972 | 676 | 0.0109 | |
|
|
| 2.4007 | 677 | 0.0262 | |
|
|
| 2.4043 | 678 | 0.0965 | |
|
|
| 2.4078 | 679 | 0.1926 | |
|
|
| 2.4113 | 680 | 0.0494 | |
|
|
| 2.4149 | 681 | 0.1212 | |
|
|
| 2.4184 | 682 | 0.0467 | |
|
|
| 2.4220 | 683 | 0.0093 | |
|
|
| 2.4255 | 684 | 0.0662 | |
|
|
| 2.4291 | 685 | 0.0487 | |
|
|
| 2.4326 | 686 | 0.1391 | |
|
|
| 2.4362 | 687 | 0.1416 | |
|
|
| 2.4397 | 688 | 0.1691 | |
|
|
| 2.4433 | 689 | 0.0936 | |
|
|
| 2.4468 | 690 | 0.1812 | |
|
|
| 2.4504 | 691 | 0.0327 | |
|
|
| 2.4539 | 692 | 0.1146 | |
|
|
| 2.4574 | 693 | 0.0711 | |
|
|
| 2.4610 | 694 | 0.0947 | |
|
|
| 2.4645 | 695 | 0.0525 | |
|
|
| 2.4681 | 696 | 0.0223 | |
|
|
| 2.4716 | 697 | 0.0266 | |
|
|
| 2.4752 | 698 | 0.206 | |
|
|
| 2.4787 | 699 | 0.0669 | |
|
|
| 2.4823 | 700 | 0.0421 | |
|
|
| 2.4858 | 701 | 0.0198 | |
|
|
| 2.4894 | 702 | 0.0255 | |
|
|
| 2.4929 | 703 | 0.008 | |
|
|
| 2.4965 | 704 | 0.0183 | |
|
|
| 2.5 | 705 | 0.0498 | |
|
|
| 2.5035 | 706 | 0.0839 | |
|
|
| 2.5071 | 707 | 0.0219 | |
|
|
| 2.5106 | 708 | 0.0977 | |
|
|
| 2.5142 | 709 | 0.0206 | |
|
|
| 2.5177 | 710 | 0.0051 | |
|
|
| 2.5213 | 711 | 0.0199 | |
|
|
| 2.5248 | 712 | 0.0366 | |
|
|
| 2.5284 | 713 | 0.01 | |
|
|
| 2.5319 | 714 | 0.1622 | |
|
|
| 2.5355 | 715 | 0.0452 | |
|
|
| 2.5390 | 716 | 0.0681 | |
|
|
| 2.5426 | 717 | 0.0103 | |
|
|
| 2.5461 | 718 | 0.0059 | |
|
|
| 2.5496 | 719 | 0.0493 | |
|
|
| 2.5532 | 720 | 0.016 | |
|
|
| 2.5567 | 721 | 0.134 | |
|
|
| 2.5603 | 722 | 0.0119 | |
|
|
| 2.5638 | 723 | 0.1173 | |
|
|
| 2.5674 | 724 | 0.2206 | |
|
|
| 2.5709 | 725 | 0.0368 | |
|
|
| 2.5745 | 726 | 0.0176 | |
|
|
| 2.5780 | 727 | 0.0599 | |
|
|
| 2.5816 | 728 | 0.123 | |
|
|
| 2.5851 | 729 | 0.0764 | |
|
|
| 2.5887 | 730 | 0.0695 | |
|
|
| 2.5922 | 731 | 0.0405 | |
|
|
| 2.5957 | 732 | 0.012 | |
|
|
| 2.5993 | 733 | 0.0469 | |
|
|
| 2.6028 | 734 | 0.0142 | |
|
|
| 2.6064 | 735 | 0.1236 | |
|
|
| 2.6099 | 736 | 0.0194 | |
|
|
| 2.6135 | 737 | 0.115 | |
|
|
| 2.6170 | 738 | 0.105 | |
|
|
| 2.6206 | 739 | 0.0937 | |
|
|
| 2.6241 | 740 | 0.1916 | |
|
|
| 2.6277 | 741 | 0.0903 | |
|
|
| 2.6312 | 742 | 0.1579 | |
|
|
| 2.6348 | 743 | 0.0902 | |
|
|
| 2.6383 | 744 | 0.0304 | |
|
|
| 2.6418 | 745 | 0.0881 | |
|
|
| 2.6454 | 746 | 0.0646 | |
|
|
| 2.6489 | 747 | 0.0941 | |
|
|
| 2.6525 | 748 | 0.0204 | |
|
|
| 2.6560 | 749 | 0.1679 | |
|
|
| 2.6596 | 750 | 0.028 | |
|
|
| 2.6631 | 751 | 0.0205 | |
|
|
| 2.6667 | 752 | 0.0307 | |
|
|
| 2.6702 | 753 | 0.0365 | |
|
|
| 2.6738 | 754 | 0.0141 | |
|
|
| 2.6773 | 755 | 0.0212 | |
|
|
| 2.6809 | 756 | 0.0447 | |
|
|
| 2.6844 | 757 | 0.1072 | |
|
|
| 2.6879 | 758 | 0.0332 | |
|
|
| 2.6915 | 759 | 0.0513 | |
|
|
| 2.6950 | 760 | 0.062 | |
|
|
| 2.6986 | 761 | 0.0941 | |
|
|
| 2.7021 | 762 | 0.0201 | |
|
|
| 2.7057 | 763 | 0.2132 | |
|
|
| 2.7092 | 764 | 0.0323 | |
|
|
| 2.7128 | 765 | 0.0654 | |
|
|
| 2.7163 | 766 | 0.059 | |
|
|
| 2.7199 | 767 | 0.1027 | |
|
|
| 2.7234 | 768 | 0.0091 | |
|
|
| 2.7270 | 769 | 0.0585 | |
|
|
| 2.7305 | 770 | 0.0102 | |
|
|
| 2.7340 | 771 | 0.0265 | |
|
|
| 2.7376 | 772 | 0.0403 | |
|
|
| 2.7411 | 773 | 0.0913 | |
|
|
| 2.7447 | 774 | 0.0212 | |
|
|
| 2.7482 | 775 | 0.0423 | |
|
|
| 2.7518 | 776 | 0.083 | |
|
|
| 2.7553 | 777 | 0.0073 | |
|
|
| 2.7589 | 778 | 0.0815 | |
|
|
| 2.7624 | 779 | 0.0786 | |
|
|
| 2.7660 | 780 | 0.1079 | |
|
|
| 2.7695 | 781 | 0.0477 | |
|
|
| 2.7730 | 782 | 0.116 | |
|
|
| 2.7766 | 783 | 0.0523 | |
|
|
| 2.7801 | 784 | 0.049 | |
|
|
| 2.7837 | 785 | 0.0153 | |
|
|
| 2.7872 | 786 | 0.0173 | |
|
|
| 2.7908 | 787 | 0.0656 | |
|
|
| 2.7943 | 788 | 0.0094 | |
|
|
| 2.7979 | 789 | 0.0757 | |
|
|
| 2.8014 | 790 | 0.0924 | |
|
|
| 2.8050 | 791 | 0.0717 | |
|
|
| 2.8085 | 792 | 0.011 | |
|
|
| 2.8121 | 793 | 0.0312 | |
|
|
| 2.8156 | 794 | 0.0188 | |
|
|
| 2.8191 | 795 | 0.0244 | |
|
|
| 2.8227 | 796 | 0.0138 | |
|
|
| 2.8262 | 797 | 0.0956 | |
|
|
| 2.8298 | 798 | 0.0125 | |
|
|
| 2.8333 | 799 | 0.0196 | |
|
|
| 2.8369 | 800 | 0.0766 | |
|
|
| 2.8404 | 801 | 0.0105 | |
|
|
| 2.8440 | 802 | 0.0347 | |
|
|
| 2.8475 | 803 | 0.1152 | |
|
|
| 2.8511 | 804 | 0.0745 | |
|
|
| 2.8546 | 805 | 0.0275 | |
|
|
| 2.8582 | 806 | 0.1096 | |
|
|
| 2.8617 | 807 | 0.0571 | |
|
|
| 2.8652 | 808 | 0.008 | |
|
|
| 2.8688 | 809 | 0.0428 | |
|
|
| 2.8723 | 810 | 0.0639 | |
|
|
| 2.8759 | 811 | 0.1364 | |
|
|
| 2.8794 | 812 | 0.062 | |
|
|
| 2.8830 | 813 | 0.0782 | |
|
|
| 2.8865 | 814 | 0.0311 | |
|
|
| 2.8901 | 815 | 0.1234 | |
|
|
| 2.8936 | 816 | 0.0302 | |
|
|
| 2.8972 | 817 | 0.0984 | |
|
|
| 2.9007 | 818 | 0.0141 | |
|
|
| 2.9043 | 819 | 0.1342 | |
|
|
| 2.9078 | 820 | 0.0115 | |
|
|
| 2.9113 | 821 | 0.0608 | |
|
|
| 2.9149 | 822 | 0.0246 | |
|
|
| 2.9184 | 823 | 0.0388 | |
|
|
| 2.9220 | 824 | 0.0557 | |
|
|
| 2.9255 | 825 | 0.011 | |
|
|
| 2.9291 | 826 | 0.0262 | |
|
|
| 2.9326 | 827 | 0.0655 | |
|
|
| 2.9362 | 828 | 0.0843 | |
|
|
| 2.9397 | 829 | 0.0549 | |
|
|
| 2.9433 | 830 | 0.0791 | |
|
|
| 2.9468 | 831 | 0.0254 | |
|
|
| 2.9504 | 832 | 0.1365 | |
|
|
| 2.9539 | 833 | 0.2078 | |
|
|
| 2.9574 | 834 | 0.0485 | |
|
|
| 2.9610 | 835 | 0.0309 | |
|
|
| 2.9645 | 836 | 0.0974 | |
|
|
| 2.9681 | 837 | 0.004 | |
|
|
| 2.9716 | 838 | 0.1136 | |
|
|
| 2.9752 | 839 | 0.0227 | |
|
|
| 2.9787 | 840 | 0.0458 | |
|
|
| 2.9823 | 841 | 0.016 | |
|
|
| 2.9858 | 842 | 0.1003 | |
|
|
| 2.9894 | 843 | 0.0289 | |
|
|
| 2.9929 | 844 | 0.0702 | |
|
|
| 2.9965 | 845 | 0.055 | |
|
|
| 3.0 | 846 | 0.2404 | |
|
|
|
|
|
</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} |
|
|
} |
|
|
``` |
|
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