Delete sescore.py

sescore.py

@@ -1,139 +0,0 @@
-# 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.
-"""SEScore: a text generation evaluation metric """
-
-import evaluate
-import datasets
-
-import comet
-from typing import Dict
-import torch
-from comet.encoders.base import Encoder
-from comet.encoders.bert import BERTEncoder
-from transformers import AutoModel, AutoTokenizer
-
-class robertaEncoder(BERTEncoder):
-    def __init__(self, pretrained_model: str) -> None:
-        super(Encoder, self).__init__()
-        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
-        self.model = AutoModel.from_pretrained(
-            pretrained_model, add_pooling_layer=False
-        )
-        self.model.encoder.output_hidden_states = True
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model: str) -> Encoder:
-        return robertaEncoder(pretrained_model)
-
-    def forward(
-        self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **kwargs
-    ) -> Dict[str, torch.Tensor]:
-        last_hidden_states, _, all_layers = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-            return_dict=False,
-        )
-        return {
-            "sentemb": last_hidden_states[:, 0, :],
-            "wordemb": last_hidden_states,
-            "all_layers": all_layers,
-            "attention_mask": attention_mask,
-        }
-
-
-# TODO: Add BibTeX citation
-_CITATION = """\
-@inproceedings{xu-etal-2022-not,
-  title={Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis},
-  author={Xu, Wenda and Tuan, Yi-lin and Lu, Yujie and Saxon, Michael and Li, Lei and Wang, William Yang},
-  booktitle ={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
-  month={dec},
-  year={2022},
-  url={https://arxiv.org/abs/2210.05035}
-}
-"""
-
-_DESCRIPTION = """\
-SEScore is an evaluation metric that trys to compute an overall score to measure text generation quality.
-"""
-
-_KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references
-Args:
-    predictions: list of candidate outputs
-    references: list of references 
-Returns:
-    {"mean_score": mean_score, "scores": scores}
-    
-Examples:
-    >>> import evaluate
-    >>> sescore = evaluate.load("xu1998hz/sescore")
-    >>> score = sescore.compute(
-            references=['sescore is a simple but effective next-generation text evaluation metric'],
-            predictions=['sescore is simple effective text evaluation metric for next generation']
-        )
-"""
-
-# TODO: Define external resources urls if needed
-BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class SEScore(evaluate.Metric):
-    """SEScore"""
-
-    def _info(self):
-        # TODO: Specifies the evaluate.EvaluationModuleInfo object
-        return evaluate.MetricInfo(
-            # This is the description that will appear on the modules page.
-            module_type="metric",
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            # This defines the format of each prediction and reference
-            features=datasets.Features({
-                'predictions': datasets.Value("string", id="sequence"),
-                'references': datasets.Value("string", id="sequence"),
-            }),
-            # Homepage of the module for documentation
-            homepage="http://module.homepage",
-            # Additional links to the codebase or references
-            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
-        )
-
-    def _download_and_prepare(self, dl_manager):
-        """download SEScore checkpoints to compute the scores"""
-        # Download SEScore checkpoint
-        from comet import load_from_checkpoint
-        import os
-        from huggingface_hub import snapshot_download
-        # initialize roberta into str2encoder
-        comet.encoders.str2encoder['RoBERTa'] = robertaEncoder
-        print("config name: ", self.config_name)
-        if self.config_name == "default":
-            destination = snapshot_download(repo_id="xu1998hz/sescore_english_mt", revision="main")
-            self.scorer = load_from_checkpoint(f'{destination}/checkpoint/sescore_english_mt.ckpt')
-        else:
-            print("Config name is not supported!")
-
-    def _compute(self, predictions, references, gpus=None, progress_bar=False):
-        if gpus is None:
-            gpus = 1 if torch.cuda.is_available() else 0
-
-        data = {"src": references, "mt": predictions}
-        data = [dict(zip(data, t)) for t in zip(*data.values())]
-        scores, mean_score = self.scorer.predict(data, gpus=gpus, progress_bar=progress_bar)
-        return {"mean_score": mean_score, "scores": scores}