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28ccb9d 700ed22 28ccb9d b68f5a1 28ccb9d 18c5e41 28ccb9d b68f5a1 28ccb9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from nervaluate import Evaluator
import datasets
import evaluate
_DESCRIPTION = """
Add a very nice description!
"""
_CITATION = """\
@misc{nereval,
title={{NER-Evaluation}: Named Entity Evaluation as in SemEval 2013 task 9.1},
url={https://github.com/davidsbatista/NER-Evaluation},
note={Software available from https://github.com/davidsbatista/NER-Evaluation},
author={Batista David},
year={2018},
}
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Add descrition on parameters!
"""
class Nervaluate(evaluate.Metric):
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Sequence(
datasets.Value("string", id="label"), id="sequence"
),
"references": datasets.Sequence(
datasets.Value("string", id="label"), id="sequence"
),
}
),
reference_urls=["https://github.com/MantisAI/nervaluate"],
)
def _compute(self, predictions, references):
metrics_result = {}
# todo: read from model file
entities_list = ['TIM', 'KV', 'IP']
evaluator = Evaluator(references, predictions,
tags=entities_list)
results, results_per_tag = evaluator.evaluate()
metrics_result['Global Strict F1'] = \
round(results['strict']['f1'], 2)
metrics_result['results Partial F1'] = \
round(results['ent_type']['f1'], 2)
for ent in results_per_tag:
metrics_result[ent + ' Strict F1'] = \
round(results_per_tag[ent]['strict']['f1'], 2)
metrics_result[ent + ' Partial F1'] = \
round(results_per_tag[ent]['ent_type']['f1'], 2)
return metrics_result
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