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| # coding=utf-8 | |
| # Copyright 2019 HuggingFace Inc. | |
| # | |
| # 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. | |
| import json | |
| import os | |
| import tempfile | |
| import unittest | |
| from transformers.modelcard import ModelCard | |
| class ModelCardTester(unittest.TestCase): | |
| def setUp(self): | |
| self.inputs_dict = { | |
| "model_details": { | |
| "Organization": "testing", | |
| "Model date": "today", | |
| "Model version": "v2.1, Developed by Test Corp in 2019.", | |
| "Architecture": "Convolutional Neural Network.", | |
| }, | |
| "metrics": "BLEU and ROUGE-1", | |
| "evaluation_data": { | |
| "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"}, | |
| "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf", | |
| }, | |
| "training_data": { | |
| "Dataset": "English Wikipedia dump dated 2018-12-01", | |
| "Preprocessing": ( | |
| "Using SentencePiece vocabulary of size 52k tokens. See details on" | |
| " https://arxiv.org/pdf/1810.03993.pdf" | |
| ), | |
| }, | |
| "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76}, | |
| } | |
| def test_model_card_common_properties(self): | |
| modelcard = ModelCard.from_dict(self.inputs_dict) | |
| self.assertTrue(hasattr(modelcard, "model_details")) | |
| self.assertTrue(hasattr(modelcard, "intended_use")) | |
| self.assertTrue(hasattr(modelcard, "factors")) | |
| self.assertTrue(hasattr(modelcard, "metrics")) | |
| self.assertTrue(hasattr(modelcard, "evaluation_data")) | |
| self.assertTrue(hasattr(modelcard, "training_data")) | |
| self.assertTrue(hasattr(modelcard, "quantitative_analyses")) | |
| self.assertTrue(hasattr(modelcard, "ethical_considerations")) | |
| self.assertTrue(hasattr(modelcard, "caveats_and_recommendations")) | |
| def test_model_card_to_json_string(self): | |
| modelcard = ModelCard.from_dict(self.inputs_dict) | |
| obj = json.loads(modelcard.to_json_string()) | |
| for key, value in self.inputs_dict.items(): | |
| self.assertEqual(obj[key], value) | |
| def test_model_card_to_json_file(self): | |
| model_card_first = ModelCard.from_dict(self.inputs_dict) | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| filename = os.path.join(tmpdirname, "modelcard.json") | |
| model_card_first.to_json_file(filename) | |
| model_card_second = ModelCard.from_json_file(filename) | |
| self.assertEqual(model_card_second.to_dict(), model_card_first.to_dict()) | |
| def test_model_card_from_and_save_pretrained(self): | |
| model_card_first = ModelCard.from_dict(self.inputs_dict) | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| model_card_first.save_pretrained(tmpdirname) | |
| model_card_second = ModelCard.from_pretrained(tmpdirname) | |
| self.assertEqual(model_card_second.to_dict(), model_card_first.to_dict()) | |