ner_evaluate.py

# 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