Upload wep-probes.py

wep-probes.py

@@ -0,0 +1,129 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+
+
+import csv
+import os
+import textwrap
+import numpy as np
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+Probing neural language models for understanding of words of estimative probability
+Anonymous submission
+"""
+
+_DESCRIPTION = """\
+Probing neural language models for understanding of words of estimative probability
+Anonymous submission
+"""
+
+URL = 'https://sileod.s3.eu-west-3.amazonaws.com/probability_words/'
+
+
+class WepProbeConfig(datasets.BuilderConfig):
+    """BuilderConfig for WepProbe."""
+
+    def __init__(
+        self,
+        data_dir,
+        label_classes=None,
+        process_label=lambda x: x,
+        **kwargs,
+    ):
+
+        super(WepProbeConfig, self).__init__(version=datasets.Version("1.0.5", ""), **kwargs)
+        self.text_features = {k:k for k in ['context', 'hypothesis', 'valid_hypothesis', 'invalid_hypothesis','probability_word','distractor','hypothesis_assertion']}
+        self.label_column = 'label'
+        self.label_classes = ['valid', 'invalid']
+        self.data_url = URL
+        self.url=URL
+        self.data_dir=data_dir
+        self.citation = _CITATION
+        self.process_label = process_label
+
+
+class WepProbe(datasets.GeneratorBasedBuilder):
+    """Evaluation of word estimative of probability understanding"""
+
+    BUILDER_CONFIGS = [
+        WepProbeConfig(
+            name="reasoning_1hop",
+            data_dir="reasoning_1hop"),
+         WepProbeConfig(
+            name="reasoning_2hop",
+            data_dir="reasoning_2hop"),
+        WepProbeConfig(
+            name="usnli",
+            data_dir="usnli"),
+        ]
+
+    def _info(self):
+        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
+        if self.config.label_classes:
+            features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
+        else:
+            features["label"] = datasets.Value("float32")
+        features["idx"] = datasets.Value("int32")
+        features["probability"] = datasets.Value("float32")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + _CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        
+        data_dirs=[]
+        for split in ['train','validation','test']:
+            url=f'{URL}{self.config.data_dir}_{split}.csv'
+            print(url)
+            data_dirs+=[dl_manager.download(url)]
+        print(data_dirs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": data_dirs[0],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_file": data_dirs[1],
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": data_dirs[2],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_file, split):
+        df = pd.read_csv(data_file).drop(['rnd','split','_'],axis=1,errors='ignore')
+        df['idx']=df.index
+        for idx, example in df.iterrows():
+            yield idx, dict(example)
+