Update climate-evaluation.py

climate-evaluation.py

@@ -89,7 +89,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
             text_features={"text": "text"},
             label_classes=[0, 1, 2],
             label_column="label",
-            data_dir="ClimateStance",
+            data_dir="climate-evaluation/ClimateStance",
             citation=textwrap.dedent(
                 """\
              @inproceedings{vaid-etal-2022-towards,
@@ -120,7 +120,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
             text_features={"text": "text"},
             label_classes=["0", "1", "2", "3", "4"],
             label_column="label",
-            data_dir="ClimateEng",
+            data_dir="climate-evaluation/ClimateEng",
             citation=textwrap.dedent(
                 """\
             @inproceedings{vaid-etal-2022-towards,
@@ -151,7 +151,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
             text_features={"text": "sentence"},
             label_classes=["0", "1"],
             label_column="label",
-            data_dir="ClimaText",
+            data_dir="climate-evaluation/ClimaText",
             citation=textwrap.dedent(
                 """\
                 @misc{varini2021climatext,
@@ -172,7 +172,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
                 """\
                 CDP-QA is a dataset compiled from the questionnaires of the Carbon Disclosure Project, where cities, corporations, and states disclose their environmental information. The dataset presents pairs of questions and answers, and the objective is to predict whether a given answer is valid for the corresponding question. We benchmarked ClimateGPT on the questionnaires from the Combined split. """
             ),
-            data_dir="CDP",
+            data_dir="climate-evaluation/CDP",
             text_features={"question": "question", "answer": "answer"},
             label_classes=["0", "1"],
             label_column="label",
@@ -196,7 +196,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
                 """\
                 The Exeter Climate Claims dataset contains textual data from 33 influential climate contrarian blogs and the climate-change-related content from 20 conservative think tanks spanning the years 1998 to 2020. Annotation of the dataset was done manually using a thorough three-layer taxonomy of (climate-change related) contrarian claims, which was developed by the authors. We utilize this dataset specifically for the binary classification task of discerning whether a given text contains a contrarian claim pertaining to climate change or not.  """
             ),
-            data_dir="exeter",
+            data_dir="climate-evaluation/exeter",
             text_features={"text": "text"},
             label_classes=["0", "1"],
             label_column="label",
@@ -223,7 +223,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
                 EXAMS is a multiple choice question answering collected from high school examinations. To evaluate ClimateGPT on the cascaded machine translation approach, we evaluate on the English translation of the Arabic subset of this dataset. The Arabic subset covers questions from biology, physics, science, social science and Islamic studies.
                 """
             ),
-            data_dir="exams/translated",
+            data_dir="climate-evaluation/exams/translated",
             text_features={"subject": "subject", "question_stem": "question_stem", "choices": "choices"},
             label_classes=["A", "B", "C", "D"],
             label_column="answerKey",
@@ -261,7 +261,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
                 EXAMS is a multiple choice question answering collected from high school examinations. To evaluate ClimateGPT on the cascaded machine translation approach, we evaluate on the Arabic subset of this dataset. The Arabic subset covers questions from biology, physics, science, social science and Islamic studies. Note, this dataset is in arabic.  
                 """
             ),
-            data_dir="exams/",
+            data_dir="climate-evaluation/exams/",
             text_features={"subject": "subject", "question_stem": "question_stem", "choices": "choices"},
             label_classes=["A", "B", "C", "D"],
             label_column="answerKey",
@@ -300,16 +300,13 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
                 {
                     "subject": datasets.Value("string"),
                     "question_stem": datasets.Value("string"),
-                    "choices": 
-                        {
-                            "text": datasets.features.Sequence(datasets.Value("string")),
-                            "label": datasets.features.Sequence(datasets.Value("string")),
-                        },
+                    "choices": datasets.Value("string"),
                     "answerKey": datasets.ClassLabel(
                         names=["A", "B", "C", "D"]
                     ),
                 }
             )
+            features["idx"] = datasets.Value("int32")
         else:
             if self.config.name == "cdp_qa":
                 features = {
@@ -338,6 +335,7 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         data_dir = self.config.data_dir
+        print(f"self.config.data_dir: {self.config.data_dir}")
 
         if self.config.name == "exams" or self.config.name == "translated_exams":
             return [
@@ -375,23 +373,11 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
             ),
             ]
 
-        if self.config.name == "climate_fever":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "data_file": os.path.join(
-                            data_dir or "", "climate-fever-dataset-r1.jsonl"
-                        ),
-                        "split": "test",
-                    },
-                ),
-            ]
-
         if self.config.name == "climatext":
             files = {
                 "train": [
-                    "train-data/Wiki-Doc-Train.tsv",
+                    # "train-data/AL-10Ks.tsv : 3000 (58 positives, 2942 negatives) (TSV, 127138 KB).tsv",
+                    "train-data/AL-Wiki (train).tsv",
                 ],
                 "valid": ["dev-data/Wikipedia (dev).tsv"],
                 "test": [
@@ -524,12 +510,14 @@ class ClimateEvaluation(datasets.GeneratorBasedBuilder):
                     example["category"] = category
 
                 if self.config.label_column in row:
+                    # print(f"self.config.label_column: {self.config.label_column}")
                     label = row[self.config.label_column]
-                    # For some tasks, the label is represented as 0 and 1 in the tsv
-                    # files and needs to be cast to integer to work with the feature.
-                    if label_classes and label not in label_classes:
-                        label = int(label) if label else None
-                    example["label"] = process_label(label)
+                    if self.config.name in ["exams", "translated_exams"]:
+                        example["answerKey"] = process_label(label)
+                    else:
+                        if label_classes and label not in label_classes:
+                            label = int(label) if label else None
+                        example["label"] = process_label(label)
                 else:
                     example["label"] = process_label(-1)