Create hal-summarization.py

hal-summarization.py

@@ -0,0 +1,134 @@
+import json
+import os
+import datasets
+from tqdm import tqdm
+
+
+_ARTICLE_ID = "article_id"
+_ARTICLE_WORDS = "article_words"
+_ARTICLE_BBOXES = "article_bboxes"
+_ARTICLE_NORM_BBOXES = "article_norm_bboxes"
+_ABSTRACT = "abstract"
+_ARTICLE_PDF_URL = "article_pdf_url"
+
+def normalize_bbox(bbox, size):
+    return [
+        int(1000 * bbox[0] / size[0]),
+        int(1000 * bbox[1] / size[1]),
+        int(1000 * bbox[2] / size[0]),
+        int(1000 * bbox[3] / size[1]),
+    ]
+
+
+class HALSummarizationConfig(datasets.BuilderConfig):
+    """BuilderConfig for HALSummarization."""
+    def __init__(self, **kwargs):
+        """BuilderConfig for ArxivSummarization.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(HALSummarizationConfig, self).__init__(**kwargs)
+        
+
+class HALSummarizationDataset(datasets.GeneratorBasedBuilder):
+    """HALSummarization Dataset."""
+
+    _TRAIN_ARCHIVE = "train.zip"
+    _VAL_ARCHIVE = "val.zip"
+    _TEST_ARCHIVE = "test.zip"
+    _TRAIN_ABSTRACTS = "train.txt"
+    _VAL_ABSTRACTS = "validation.txt"
+    _TEST_ABSTRACTS = "test.txt"
+    
+    BUILDER_CONFIGS = [
+        HALSummarizationConfig(
+            name="hal",
+            version=datasets.Version("1.0.0"),
+            description="HAL dataset for summarization",
+        ),
+    ]
+
+    
+    def _info(self):
+        # Should return a datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    _ARTICLE_ID: datasets.Value("string"),
+                    _ARTICLE_WORDS: datasets.Sequence(datasets.Value("string")),
+                    _ARTICLE_BBOXES: datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
+                    _ARTICLE_NORM_BBOXES: datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
+                    _ABSTRACT: datasets.Value("string"),
+                    _ARTICLE_PDF_URL: datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        train_dir = os.path.join(dl_manager.download_and_extract(self._TRAIN_ARCHIVE), "train")
+        val_dir = os.path.join(dl_manager.download_and_extract(self._VAL_ARCHIVE), "val")
+        test_dir = os.path.join(dl_manager.download_and_extract(self._TEST_ARCHIVE), "test")
+
+        train_abstracts = dl_manager.download_and_extract(self._TRAIN_ABSTRACTS)
+        val_abstracts = dl_manager.download_and_extract(self._VAL_ABSTRACTS)
+        test_abstracts = dl_manager.download_and_extract(self._TEST_ABSTRACTS)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, 
+                gen_kwargs={"data_path": train_dir, "abstract_path": train_abstracts}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, 
+                gen_kwargs={"data_path": val_dir, "abstract_path": val_abstracts}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, 
+                gen_kwargs={"data_path": test_dir, "abstract_path": test_abstracts}
+            ),
+        ]
+
+    def _generate_examples(self, data_path, abstract_path):
+        """Generate HALSummarization examples."""
+        filenames = sorted(os.listdir(data_path))
+
+        guid = 0
+        with open(abstract_path, 'r') as abstract_file:
+            for line in tqdm(abstract_file, total=len(filenames), desc=f"Reading files in {data_path}"):
+                guid += 1
+                item = json.loads(line)
+                fname = item["id"] + ".txt"
+                filepath = os.path.join(data_path, fname)
+                
+                words  = []
+                bboxes = []
+                norm_bboxes = []
+
+                with open(filepath, encoding="utf-8") as f:
+                    for line in f:
+                        splits = line.split("\t")
+                        word = splits[0]
+                        bbox = splits[1:5]
+                        bbox = [int(b) for b in bbox]
+                        page_width, page_height = int(splits[5]), int(splits[6])
+                        norm_bbox = normalize_bbox(bbox, (page_width, page_height))
+
+                        words.append(word)
+                        bboxes.append(bbox)
+                        norm_bboxes.append(norm_bbox)
+
+                assert len(words) == len(bboxes)
+                assert len(bboxes) == len(norm_bboxes)
+
+                yield guid, {
+                        _ARTICLE_ID: item["id"],
+                        _ARTICLE_WORDS: words, 
+                        _ARTICLE_BBOXES: bboxes, 
+                        _ARTICLE_NORM_BBOXES: norm_bboxes,
+                        _ABSTRACT: item["abstract"],
+                        _ARTICLE_PDF_URL: item["pdf_url"],
+                    }
+
+