Delete loading script

tweet_eval.py

@@ -1,249 +0,0 @@
-# coding=utf-8
-# 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.
-"""The Tweet Eval Datasets"""
-
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{barbieri2020tweeteval,
-  title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
-  author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
-  booktitle={Proceedings of Findings of EMNLP},
-  year={2020}
-}
-"""
-
-_DESCRIPTION = """\
-TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.
-"""
-
-_HOMEPAGE = "https://github.com/cardiffnlp/tweeteval"
-
-_LICENSE = ""
-
-URL = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/"
-
-_URLs = {
-    "emoji": {
-        "train_text": URL + "emoji/train_text.txt",
-        "train_labels": URL + "emoji/train_labels.txt",
-        "test_text": URL + "emoji/test_text.txt",
-        "test_labels": URL + "emoji/test_labels.txt",
-        "val_text": URL + "emoji/val_text.txt",
-        "val_labels": URL + "emoji/val_labels.txt",
-    },
-    "emotion": {
-        "train_text": URL + "emotion/train_text.txt",
-        "train_labels": URL + "emotion/train_labels.txt",
-        "test_text": URL + "emotion/test_text.txt",
-        "test_labels": URL + "emotion/test_labels.txt",
-        "val_text": URL + "emotion/val_text.txt",
-        "val_labels": URL + "emotion/val_labels.txt",
-    },
-    "hate": {
-        "train_text": URL + "hate/train_text.txt",
-        "train_labels": URL + "hate/train_labels.txt",
-        "test_text": URL + "hate/test_text.txt",
-        "test_labels": URL + "hate/test_labels.txt",
-        "val_text": URL + "hate/val_text.txt",
-        "val_labels": URL + "hate/val_labels.txt",
-    },
-    "irony": {
-        "train_text": URL + "irony/train_text.txt",
-        "train_labels": URL + "irony/train_labels.txt",
-        "test_text": URL + "irony/test_text.txt",
-        "test_labels": URL + "irony/test_labels.txt",
-        "val_text": URL + "irony/val_text.txt",
-        "val_labels": URL + "irony/val_labels.txt",
-    },
-    "offensive": {
-        "train_text": URL + "offensive/train_text.txt",
-        "train_labels": URL + "offensive/train_labels.txt",
-        "test_text": URL + "offensive/test_text.txt",
-        "test_labels": URL + "offensive/test_labels.txt",
-        "val_text": URL + "offensive/val_text.txt",
-        "val_labels": URL + "offensive/val_labels.txt",
-    },
-    "sentiment": {
-        "train_text": URL + "sentiment/train_text.txt",
-        "train_labels": URL + "sentiment/train_labels.txt",
-        "test_text": URL + "sentiment/test_text.txt",
-        "test_labels": URL + "sentiment/test_labels.txt",
-        "val_text": URL + "sentiment/val_text.txt",
-        "val_labels": URL + "sentiment/val_labels.txt",
-    },
-    "stance": {
-        "abortion": {
-            "train_text": URL + "stance/abortion/train_text.txt",
-            "train_labels": URL + "stance/abortion/train_labels.txt",
-            "test_text": URL + "stance/abortion/test_text.txt",
-            "test_labels": URL + "stance/abortion/test_labels.txt",
-            "val_text": URL + "stance/abortion/val_text.txt",
-            "val_labels": URL + "stance/abortion/val_labels.txt",
-        },
-        "atheism": {
-            "train_text": URL + "stance/atheism/train_text.txt",
-            "train_labels": URL + "stance/atheism/train_labels.txt",
-            "test_text": URL + "stance/atheism/test_text.txt",
-            "test_labels": URL + "stance/atheism/test_labels.txt",
-            "val_text": URL + "stance/atheism/val_text.txt",
-            "val_labels": URL + "stance/atheism/val_labels.txt",
-        },
-        "climate": {
-            "train_text": URL + "stance/climate/train_text.txt",
-            "train_labels": URL + "stance/climate/train_labels.txt",
-            "test_text": URL + "stance/climate/test_text.txt",
-            "test_labels": URL + "stance/climate/test_labels.txt",
-            "val_text": URL + "stance/climate/val_text.txt",
-            "val_labels": URL + "stance/climate/val_labels.txt",
-        },
-        "feminist": {
-            "train_text": URL + "stance/feminist/train_text.txt",
-            "train_labels": URL + "stance/feminist/train_labels.txt",
-            "test_text": URL + "stance/feminist/test_text.txt",
-            "test_labels": URL + "stance/feminist/test_labels.txt",
-            "val_text": URL + "stance/feminist/val_text.txt",
-            "val_labels": URL + "stance/feminist/val_labels.txt",
-        },
-        "hillary": {
-            "train_text": URL + "stance/hillary/train_text.txt",
-            "train_labels": URL + "stance/hillary/train_labels.txt",
-            "test_text": URL + "stance/hillary/test_text.txt",
-            "test_labels": URL + "stance/hillary/test_labels.txt",
-            "val_text": URL + "stance/hillary/val_text.txt",
-            "val_labels": URL + "stance/hillary/val_labels.txt",
-        },
-    },
-}
-
-
-class TweetEvalConfig(datasets.BuilderConfig):
-    def __init__(self, *args, type=None, sub_type=None, **kwargs):
-        super().__init__(
-            *args,
-            name=f"{type}" if type != "stance" else f"{type}_{sub_type}",
-            **kwargs,
-        )
-        self.type = type
-        self.sub_type = sub_type
-
-
-class TweetEval(datasets.GeneratorBasedBuilder):
-    """TweetEval Dataset."""
-
-    BUILDER_CONFIGS = [
-        TweetEvalConfig(
-            type=key,
-            sub_type=None,
-            version=datasets.Version("1.1.0"),
-            description=f"This part of my dataset covers {key} part of TweetEval Dataset.",
-        )
-        for key in list(_URLs.keys())
-        if key != "stance"
-    ] + [
-        TweetEvalConfig(
-            type="stance",
-            sub_type=key,
-            version=datasets.Version("1.1.0"),
-            description=f"This part of my dataset covers stance_{key} part of TweetEval Dataset.",
-        )
-        for key in list(_URLs["stance"].keys())
-    ]
-
-    def _info(self):
-        if self.config.type == "stance":
-            names = ["none", "against", "favor"]
-        elif self.config.type == "sentiment":
-            names = ["negative", "neutral", "positive"]
-        elif self.config.type == "offensive":
-            names = ["non-offensive", "offensive"]
-        elif self.config.type == "irony":
-            names = ["non_irony", "irony"]
-        elif self.config.type == "hate":
-            names = ["non-hate", "hate"]
-        elif self.config.type == "emoji":
-            names = [
-                "❤",
-                "😍",
-                "😂",
-                "💕",
-                "🔥",
-                "😊",
-                "😎",
-                "✨",
-                "💙",
-                "😘",
-                "📷",
-                "🇺🇸",
-                "☀",
-                "💜",
-                "😉",
-                "💯",
-                "😁",
-                "🎄",
-                "📸",
-                "😜",
-            ]
-
-        else:
-            names = ["anger", "joy", "optimism", "sadness"]
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=names)}
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        if self.config.type != "stance":
-            my_urls = _URLs[self.config.type]
-        else:
-            my_urls = _URLs[self.config.type][self.config.sub_type]
-        data_dir = dl_manager.download_and_extract(my_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["train_text"], "labels_path": data_dir["train_labels"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["test_text"], "labels_path": data_dir["test_labels"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["val_text"], "labels_path": data_dir["val_labels"]},
-            ),
-        ]
-
-    def _generate_examples(self, text_path, labels_path):
-        """Yields examples."""
-
-        with open(text_path, encoding="utf-8") as f:
-            texts = f.readlines()
-        with open(labels_path, encoding="utf-8") as f:
-            labels = f.readlines()
-        for i, text in enumerate(texts):
-            yield i, {"text": text.strip(), "label": int(labels[i].strip())}