script name changed

CoarseWSD-20.py

@@ -1,5 +1,5 @@
 
-# @title Gold cwsd20
+# @title Gold cwsd20 temp
 import json
 import datasets
 
@@ -22,25 +22,15 @@ to provide an ideal setting for evaluating WSD models (e.g. no senses in test se
 from training), both quantitavely and qualitatively.
 """
 
-path = "data/apple/train.data.txt"
+path = "/content/CoarseWSD-20/data/apple/train.data.txt"
 
-_PATHS = {
-    "train_examples": "/content/train/train.data.txt",
-    "train_labels":  "/content/train/train.gold.txt",
-    "dev_examples":  "/content/dev/dev.data.txt",
-    "dev_labels":  "/content/dev/dev.gold.txt",
-    "test_examples": "/content/test/test.data.txt",
-    "test_labels": "/content/test/test.gold.txt",
-}
 
-def normalize_text(text):
-  return text.replace(' .', '.').replace(' ,', ',').replace(" '", "'").replace(" ?", "?").replace(" !", "!")
 
 class CWSD20(datasets.GeneratorBasedBuilder):
     """TODO(WiCTSV): Short description of my dataset."""
 
     # TODO(WiCTSV): Set up version.
-    VERSION = datasets.Version("3.5.8")
+    VERSION = datasets.Version("1.0.0")
 
     def _info(self):
         # TODO(WiCTSV): Specifies the datasets.DatasetInfo object
@@ -50,15 +40,15 @@ class CWSD20(datasets.GeneratorBasedBuilder):
             # datasets.features.FeatureConnectors
             features=datasets.Features(
                 {
-                    "sentence1": datasets.Value("string"),
-                    "sentence2": datasets.Value("string"),
                     "idx": datasets.Value("int32"),
-                    "word": datasets.Value("string"),
-                    "start1": datasets.Value("int32"),
-                    "start2": datasets.Value("int32"),
-                    "end1": datasets.Value("int32"),
-                    "end2": datasets.Value("int32"),
-                    "label": datasets.Value("int32")
+                    "sentence": datasets.Value("string"),
+                    # "idx": datasets.Value("int32"),
+                    # "word": datasets.Value("string"),
+                    # "start1": datasets.Value("int32"),
+                    # "start2": datasets.Value("int32"),
+                    # "end1": datasets.Value("int32"),
+                    # "end2": datasets.Value("int32"),
+                    # "label": datasets.Value("int32")
                 }
             ),
             # If there's a common (input, target) tuple from the features,
@@ -76,41 +66,21 @@ class CWSD20(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to
         # download and extract URLs
         # urls_to_download = _URLS
-        dl = _PATHS
+        dl = path
 
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN,
-                                    gen_kwargs={"ex": dl["train_examples"], "lb":dl["train_labels"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, 
-                                    gen_kwargs={"ex": dl["test_examples"], "lb":dl["test_labels"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, 
-                                    gen_kwargs={"ex": dl["dev_examples"], "lb":dl["dev_labels"]}),
-        ]
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, 
+                gen_kwargs={"ex": dl})]
 
-    def _generate_examples(self, ex, lb):
+    def _generate_examples(self, ex):
       """Yields examples."""
       with open(ex, encoding="utf-8") as exf:
-        with open(lb, encoding="utf-8") as lbf:
-          for id_, (exi, lbi) in enumerate(zip(exf, lbf)):
+          for id_, exi in enumerate(exf):
             example = {}
             # 'word', 'sentence1', 'sentence2', 'start1', 'start2', 'end1', 'end2', 'idx', 'label'
-
-            word, _, positions, sent1, sent2 = exi.strip().split('\t')
-            tokens1 = sent1.split(' ')
-            tokens2 = sent2.split(' ')
-            pos1, _, pos2 = positions.partition('-')
-            pos1, pos2 = int(pos1), int(pos2)
-            sentence1b = normalize_text(' '.join(tokens1[:pos1]))
-            sentence1a =  normalize_text(' '.join(tokens1[pos1+1:]))
-            example["sentence1"] = normalize_text(' '.join(tokens1))
-            sentence2b = normalize_text(' '.join(tokens2[:pos2]))
-            sentence2a = normalize_text(' '.join(tokens2[pos2+1:]))
-            example["sentence2"] = normalize_text(' '.join(tokens2))
-            example["start1"] = 0 if pos1 == 0 else len(sentence1b) + 1
-            example["start2"] = 0 if pos2 == 0 else len(sentence2b) + 1
-            example["end1"] = example["start1"] + len(tokens1[pos1])
-            example["end2"] = example["start2"] + len(tokens2[pos2])
-            example["idx"] = id_
-            example["word"] = word
-            example["label"] = 1 if lbi.strip() == 'T' else 0
+            parts = exi.split("\t")
+            idx = parts[0]
+            sent = parts[1]
+            example["sentence"] = sent
+            example["idx"] = idx
+            
             yield id_, example

coarsewsd_20.py

@@ -1,86 +0,0 @@
-
-# @title Gold cwsd20 temp
-import json
-import datasets
-
-
-_CITATION = """\
-@misc{loureiro2021analysis,
-      title={Analysis and Evaluation of Language Models for Word Sense Disambiguation}, 
-      author={Daniel Loureiro and Kiamehr Rezaee and Mohammad Taher Pilehvar and Jose Camacho-Collados},
-      year={2021},
-      eprint={2008.11608},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = """\
-The CoarseWSD-20 dataset is a coarse-grained sense disambiguation built from Wikipedia 
-(nouns only) targetting 2 to 5 senses of 20 ambiguous words. It was specifically designed 
-to provide an ideal setting for evaluating WSD models (e.g. no senses in test sets missing 
-from training), both quantitavely and qualitatively.
-"""
-
-path = "/content/CoarseWSD-20/data/apple/train.data.txt"
-
-
-
-class CWSD20(datasets.GeneratorBasedBuilder):
-    """TODO(WiCTSV): Short description of my dataset."""
-
-    # TODO(WiCTSV): Set up version.
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        # TODO(WiCTSV): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "idx": datasets.Value("int32"),
-                    "sentence": datasets.Value("string"),
-                    # "idx": datasets.Value("int32"),
-                    # "word": datasets.Value("string"),
-                    # "start1": datasets.Value("int32"),
-                    # "start2": datasets.Value("int32"),
-                    # "end1": datasets.Value("int32"),
-                    # "end2": datasets.Value("int32"),
-                    # "label": datasets.Value("int32")
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://github.com/google-research-datasets/boolean-questions",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(WiCTSV): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        # urls_to_download = _URLS
-        dl = path
-
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, 
-                gen_kwargs={"ex": dl})]
-
-    def _generate_examples(self, ex):
-      """Yields examples."""
-      with open(ex, encoding="utf-8") as exf:
-          for id_, exi in enumerate(exf):
-            example = {}
-            # 'word', 'sentence1', 'sentence2', 'start1', 'start2', 'end1', 'end2', 'idx', 'label'
-            parts = exi.split("\t")
-            idx = parts[0]
-            sent = parts[1]
-            example["sentence"] = sent
-            example["idx"] = idx
-            
-            yield id_, example