├── .gitignore ├── README.md ├── frugalscore.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FrugalScore 2 | FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance 3 | 4 | Paper: https://arxiv.org/abs/2110.08559?context=cs 5 | 6 | The pretrained checkpoints presented in the paper can be found on the [huggingface models hub](https://huggingface.co/moussaKam) 7 | 8 | | FrugalScore | Student | Teacher | Method | 9 | |----------------------------------------------------|-------------|----------------|------------| 10 | | moussaKam/frugalscore_tiny_bert-base_bert-score | BERT-tiny | BERT-Base | BERTScore | 11 | | moussaKam/frugalscore_small_bert-base_bert-score | BERT-small | BERT-Base | BERTScore | 12 | | moussaKam/frugalscore_medium_bert-base_bert-score | BERT-medium | BERT-Base | BERTScore | 13 | | moussaKam/frugalscore_tiny_roberta_bert-score | BERT-tiny | RoBERTa-Large | BERTScore | 14 | | moussaKam/frugalscore_small_roberta_bert-score | BERT-small | RoBERTa-Large | BERTScore | 15 | | moussaKam/frugalscore_medium_roberta_bert-score | BERT-medium | RoBERTa-Large | BERTScore | 16 | | moussaKam/frugalscore_tiny_deberta_bert-score | BERT-tiny | DeBERTa-XLarge | BERTScore | 17 | | moussaKam/frugalscore_small_deberta_bert-score | BERT-small | DeBERTa-XLarge | BERTScore | 18 | | moussaKam/frugalscore_medium_deberta_bert-score | BERT-medium | DeBERTa-XLarge | BERTScore | 19 | | moussaKam/frugalscore_tiny_bert-base_mover-score | BERT-tiny | BERT-Base | MoverScore | 20 | | moussaKam/frugalscore_small_bert-base_mover-score | BERT-small | BERT-Base | MoverScore | 21 | | moussaKam/frugalscore_medium_bert-base_mover-score | BERT-medium | BERT-Base | MoverScore | 22 | 23 | # Experiments on [BEAMetrics](https://github.com/ThomasScialom/BEAMetrics) benchmark 24 | We evaluate our three models `frugalscore_(tiny/small/medium)_bert-base_bert-score` different datasets includeded in `BEAMetrics` benchmark. The results are comparable to the second block of Table 2 reported in the [paper](https://arxiv.org/pdf/2110.09147.pdf). 25 | 26 | | | WMT | Web | Asv | MUS | Fli | ReaSum | SumE | OpQA | OkVQA | 27 | | ------------- | :--: | :--: | :--: | :--: | :--: | :----: | :--: | :--: | :---: | 28 | | BERTScore_f1 | 20.5 | 60.8 | 61.4 | 37.5 | 33.5 | 39.3 | 12.4 | 12.4 | 6.2 | 29 | | Frugal_tiny | 16.6 | 71.5 | 44.0 | 52.3 | 49.8 | 46.4 | 18.7 | 29.2 | 20.1 | 30 | | Frugal_small | 18.4 | 72.1 | 52.9 | 48.2 | 54.1 | 48.9 | 15.2 | 28.2 | 14.3 | 31 | | Frugal_medium | 19.7 | 73.4 | 58.4 | 45.9 | 54.2 | 49.4 | 16.4 | 24.7 | 16.1 | 32 | 33 | Note that `bert-base-multilingual-cased` is used to generate BERTScore. 34 | 35 | To use the metric: 36 | 37 | ```python 38 | from datasets import load_metric 39 | metric = load_metric('frugalscore.py') 40 | 41 | references = ['hello world', 'this is an example'] 42 | predictions = ['hello there', 'this is a good example'] 43 | 44 | scores = metric.compute(references=references, predictions=predictions) 45 | 46 | print(scores) 47 | ``` 48 | `{'scores': [0.631, 0.864]}` 49 | 50 | Make sure you have the latest verions of the `datasets` and `transformers` installed: 51 | ``` 52 | pip install --upgrade datasets 53 | pip install --upgrade transformers 54 | ``` 55 | -------------------------------------------------------------------------------- /frugalscore.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | """FrugalScore metric.""" 15 | 16 | from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments 17 | 18 | import datasets 19 | 20 | 21 | _CITATION = """\ 22 | @article{eddine2021frugalscore, 23 | title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metricsfor Automatic Text Generation}, 24 | author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis}, 25 | journal={arXiv preprint arXiv:2110.08559}, 26 | year={2021} 27 | } 28 | """ 29 | 30 | _DESCRIPTION = """\ 31 | FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance. 32 | """ 33 | 34 | 35 | _KWARGS_DESCRIPTION = """ 36 | Calculates how good are predictions given some references, using certain scores 37 | Args: 38 | predictions (list of str): list of predictions to score. Each predictions 39 | should be a string. 40 | references (list of str): list of reference for each prediction. Each 41 | reference should be a string. 42 | pretrained_model_name_or_path (str): the pretrained model used to generate the scores. 43 | batch_size (int): the batch size for predictions. 44 | max_length (int): maximum sequence length. 45 | Returns: 46 | scores (list of int): list of scores. 47 | Examples: 48 | >>> my_new_metric = datasets.load_metric("frugalscore") 49 | >>> results = my_new_metric.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face']) 50 | >>> print(results) 51 | {'scores': [0.631, 0.645]} 52 | """ 53 | 54 | 55 | @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) 56 | class FRUGALSCORE(datasets.Metric): 57 | def _info(self): 58 | return datasets.MetricInfo( 59 | description=_DESCRIPTION, 60 | citation=_CITATION, 61 | inputs_description=_KWARGS_DESCRIPTION, 62 | features=datasets.Features( 63 | { 64 | "predictions": datasets.Value("string"), 65 | "references": datasets.Value("string"), 66 | } 67 | ), 68 | homepage="https://github.com/moussaKam/FrugalScore", 69 | ) 70 | 71 | def _compute( 72 | self, 73 | predictions, 74 | references, 75 | pretrained_model_name_or_path="moussaKam/frugalscore_tiny_bert-base_bert-score", 76 | batch_size=32, 77 | max_length=128, 78 | ): 79 | """Returns the scores""" 80 | model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path) 81 | tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path) 82 | 83 | training_args = TrainingArguments("trainer", fp16=True, per_device_eval_batch_size=batch_size, report_to=None) 84 | dataset = {"sentence1": predictions, "sentence2": references} 85 | raw_datasets = datasets.Dataset.from_dict(dataset) 86 | 87 | def tokenize_function(data): 88 | return tokenizer( 89 | data["sentence1"], data["sentence2"], max_length=max_length, truncation=True, padding=True 90 | ) 91 | 92 | tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) 93 | tokenized_datasets.remove_columns(["sentence1", "sentence2"]) 94 | trainer = Trainer(model, training_args, tokenizer=tokenizer) 95 | predictions = trainer.predict(tokenized_datasets) 96 | return {"scores": list(predictions.predictions.squeeze(-1))} 97 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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