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Runtime error
Runtime error
Commit
·
a84678a
1
Parent(s):
b519cf9
add evaluation
Browse files- .gitignore +171 -0
- app.py +10 -0
- custom_metric/custom_metric.py +203 -0
- custom_metric/metric.yml +10 -0
.gitignore
ADDED
@@ -0,0 +1,171 @@
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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*.conll
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*.pt
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*.onnx
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# C extensions
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lib64/
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parts/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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*.cover
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*.py,cover
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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.python-version
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env/
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venv
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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split.py
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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*.log.*
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*/logs
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*/var/run
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*/*/*/*/run/*
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*/*/*/*/logs/*
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# OS generated files
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.DS_Store*
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ehthumbs.db
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Icon?
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Thumbs.db
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# Editor Files
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*~
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*.swp
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cli/meta
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# IDE Files
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.vscode/
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# model file
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*.pkl
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catboost_info/
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app.py
ADDED
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import evaluate
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from evaluate.utils import launch_gradio_widget
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# Define the path to your custom metric directory
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metric_path = "./custom_metric"
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module = evaluate.load(metric_path)
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launch_gradio_widget(module)
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custom_metric/custom_metric.py
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import evaluate
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import datasets
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import numpy as np
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_CITATION = """\
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@InProceedings{huggingface:module,
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title = {A great new module},
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authors={huggingface, Inc.},
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year={2020}
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}
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"""
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+
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# TODO: Add description of the module here
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_DESCRIPTION = """\
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This new module is designed to solve this great ML task and is crafted with a lot of care.
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"""
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# TODO: Add description of the arguments of the module here
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_KWARGS_DESCRIPTION = """
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Calculates how good are predictions given some references, using certain scores
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+
Args:
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predictions: list of predictions to score. Each predictions
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should be a string with tokens separated by spaces.
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references: list of reference for each prediction. Each
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reference should be a string with tokens separated by spaces.
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Returns:
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accuracy: description of the first score,
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another_score: description of the second score,
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Examples:
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+
Examples should be written in doctest format, and should illustrate how
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to use the function.
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>>> my_new_module = evaluate.load("my_new_module")
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>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
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>>> print(results)
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{'accuracy': 1.0}
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"""
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+
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# TODO: Define external resources urls if needed
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BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
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+
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def convert_format(data:list):
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"""
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Args:
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data (list) : list of dictionaries with different entity elements
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+
e.g
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[
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{'head': ['phipigments', 'tinadaviespigments'...],
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'head_type': ['product', 'brand'...],
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'type': ['sell', 'sell'...],
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'tail': ['國際認證之色乳', '國際認證之色乳'...],
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'tail_type': ['product', 'product'...]},
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+
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{'head': ['SABONTAIWAN', 'SNTAIWAN'...],
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'head_type': ['brand', 'brand'...],
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'type': ['sell', 'sell'...],
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'tail': ['大馬士革玫瑰有機光燦系列', '大馬士革玫瑰有機光燦系列'...],
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'tail_type': ['product', 'product'...]}
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...
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]
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"""
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predictions = []
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for item in data:
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prediction_group = []
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for i in range(len(item['head'])):
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prediction = {
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'head': item['head'][i],
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'head_type': item['head_type'][i],
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'type': item['type'][i],
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'tail': item['tail'][i],
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'tail_type': item['tail_type'][i],
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}
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prediction_group.append(prediction)
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predictions.append(prediction_group)
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return predictions
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class relation_extraction(evaluate.Metric):
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"""TODO: Short description of my evaluation module."""
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+
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def _info(self):
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# TODO: Specifies the evaluate.EvaluationModuleInfo object
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return evaluate.MetricInfo(
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# This is the description that will appear on the modules page.
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module_type="metric",
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description=_DESCRIPTION,
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citation=_CITATION,
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inputs_description=_KWARGS_DESCRIPTION,
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# This defines the format of each prediction and reference
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features=datasets.Features({
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'predictions': datasets.Sequence({
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"head": datasets.Value("string"),
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"head_type": datasets.Value("string"),
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"type": datasets.Value("string"),
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96 |
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"tail": datasets.Value("string"),
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"tail_type": datasets.Value("string"),
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}),
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'references': datasets.Sequence({
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"head": datasets.Value("string"),
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"head_type": datasets.Value("string"),
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"type": datasets.Value("string"),
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"tail": datasets.Value("string"),
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"tail_type": datasets.Value("string"),
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}),
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}),
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# Homepage of the module for documentation
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homepage="http://module.homepage",
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# Additional links to the codebase or references
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codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
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reference_urls=["http://path.to.reference.url/new_module"]
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)
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+
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+
def _download_and_prepare(self, dl_manager):
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115 |
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"""Optional: download external resources useful to compute the scores"""
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116 |
+
# TODO: Download external resources if needed
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117 |
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pass
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118 |
+
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119 |
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def _compute(self, predictions, references, mode="strict", relation_types=[]):
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120 |
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"""Returns the scores"""
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121 |
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# TODO: Compute the different scores of the module
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122 |
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print(predictions)
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123 |
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predictions = convert_format(predictions)
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references = convert_format(references)
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125 |
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print(predictions)
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assert mode in ["strict", "boundaries"]
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127 |
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128 |
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# construct relation_types from ground truth if not given
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129 |
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if len(relation_types) == 0:
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130 |
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for triplets in references:
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131 |
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for triplet in triplets:
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132 |
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relation = triplet["type"]
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133 |
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if relation not in relation_types:
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relation_types.append(relation)
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135 |
+
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136 |
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scores = {rel: {"tp": 0, "fp": 0, "fn": 0} for rel in relation_types + ["ALL"]}
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137 |
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138 |
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# Count GT relations and Predicted relations
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139 |
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n_sents = len(references)
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140 |
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n_rels = sum([len([rel for rel in sent]) for sent in references])
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141 |
+
n_found = sum([len([rel for rel in sent]) for sent in predictions])
|
142 |
+
|
143 |
+
# Count TP, FP and FN per type
|
144 |
+
for pred_sent, gt_sent in zip(predictions, references):
|
145 |
+
for rel_type in relation_types:
|
146 |
+
# strict mode takes argument types into account
|
147 |
+
if mode == "strict":
|
148 |
+
pred_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in pred_sent if
|
149 |
+
rel["type"] == rel_type}
|
150 |
+
gt_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in gt_sent if
|
151 |
+
rel["type"] == rel_type}
|
152 |
+
|
153 |
+
# boundaries mode only takes argument spans into account
|
154 |
+
elif mode == "boundaries":
|
155 |
+
pred_rels = {(rel["head"], rel["tail"]) for rel in pred_sent if rel["type"] == rel_type}
|
156 |
+
gt_rels = {(rel["head"], rel["tail"]) for rel in gt_sent if rel["type"] == rel_type}
|
157 |
+
|
158 |
+
scores[rel_type]["tp"] += len(pred_rels & gt_rels)
|
159 |
+
scores[rel_type]["fp"] += len(pred_rels - gt_rels)
|
160 |
+
scores[rel_type]["fn"] += len(gt_rels - pred_rels)
|
161 |
+
|
162 |
+
# Compute per entity Precision / Recall / F1
|
163 |
+
for rel_type in scores.keys():
|
164 |
+
if scores[rel_type]["tp"]:
|
165 |
+
scores[rel_type]["p"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fp"] + scores[rel_type]["tp"])
|
166 |
+
scores[rel_type]["r"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fn"] + scores[rel_type]["tp"])
|
167 |
+
else:
|
168 |
+
scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0
|
169 |
+
|
170 |
+
if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0:
|
171 |
+
scores[rel_type]["f1"] = 2 * scores[rel_type]["p"] * scores[rel_type]["r"] / (
|
172 |
+
scores[rel_type]["p"] + scores[rel_type]["r"])
|
173 |
+
else:
|
174 |
+
scores[rel_type]["f1"] = 0
|
175 |
+
|
176 |
+
# Compute micro F1 Scores
|
177 |
+
tp = sum([scores[rel_type]["tp"] for rel_type in relation_types])
|
178 |
+
fp = sum([scores[rel_type]["fp"] for rel_type in relation_types])
|
179 |
+
fn = sum([scores[rel_type]["fn"] for rel_type in relation_types])
|
180 |
+
|
181 |
+
|
182 |
+
if tp:
|
183 |
+
precision = 100 * tp / (tp + fp)
|
184 |
+
recall = 100 * tp / (tp + fn)
|
185 |
+
f1 = 2 * precision * recall / (precision + recall)
|
186 |
+
|
187 |
+
else:
|
188 |
+
precision, recall, f1 = 0, 0, 0
|
189 |
+
|
190 |
+
scores["ALL"]["p"] = precision
|
191 |
+
scores["ALL"]["r"] = recall
|
192 |
+
scores["ALL"]["f1"] = f1
|
193 |
+
scores["ALL"]["tp"] = tp
|
194 |
+
scores["ALL"]["fp"] = fp
|
195 |
+
scores["ALL"]["fn"] = fn
|
196 |
+
|
197 |
+
|
198 |
+
# Compute Macro F1 Scores
|
199 |
+
scores["ALL"]["Macro_f1"] = np.mean([scores[ent_type]["f1"] for ent_type in relation_types])
|
200 |
+
scores["ALL"]["Macro_p"] = np.mean([scores[ent_type]["p"] for ent_type in relation_types])
|
201 |
+
scores["ALL"]["Macro_r"] = np.mean([scores[ent_type]["r"] for ent_type in relation_types])
|
202 |
+
|
203 |
+
return scores
|
custom_metric/metric.yml
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
metric_name: custom_relation_extraction
|
2 |
+
description: Custom Relation Extraction Metric
|
3 |
+
inputs:
|
4 |
+
- name: predictions
|
5 |
+
type: list
|
6 |
+
required: true
|
7 |
+
- name: references
|
8 |
+
type: list
|
9 |
+
required: true
|
10 |
+
compute_function: custom_metric.relation_extraction.compute
|