dataset builder

rebel-pt.py

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+# Lint as: python3
+"""REBEL"""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+import os
+import re 
+import json
+import logging
+
+_DESCRIPTION = """\
+REBEL-Portuguese is an REBEL adaptation for Portuguese.
+"""
+
+_URL = "https://huggingface.co/datasets/ju-resplande/rebel-pt/resolve/main/pt.zip"
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
+_CITATION = """\
+    @inproceedings{huguet-cabot-navigli-2021-rebel,
+    title = "REBEL: Relation Extraction By End-to-end Language generation",
+    author = "Huguet Cabot, Pere-Llu{\'\i}s  and
+      Navigli, Roberto",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+    month = nov,
+    year = "2021",
+    address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf",
+    }
+"""
+_HOMEPAGE = "https://github.com/ju-resplande/crocodile"
+
+
+class RebelConfig(datasets.BuilderConfig):
+    """BuilderConfig for REBEL."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for REBEL.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(RebelConfig, self).__init__(**kwargs)
+
+
+class Rebel(datasets.GeneratorBasedBuilder):
+    """Rebel 1.0"""
+
+    BUILDER_CONFIGS = [
+        RebelConfig(
+            name="REBEL",
+            version=datasets.Version("1.0.0"),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "triplets": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.data_dir:
+            data_dir = self.config.data_dir
+        else:
+            data_dir = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(name='pt', gen_kwargs={"filepath": os.path.join(data_dir, "pt.jsonl")})
+            #datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "en_train.jsonl")}),
+            #datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir,"en_val.jsonl")}),
+            #datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir,"en_test.jsonl")}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logging.info("generating examples from = %s", filepath)
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                article = json.loads(row)
+                prev_len = 0
+                if len(article['triples']) == 0:
+                    continue
+                count = 0
+                for text_paragraph in article['text'].split('\n'):
+                    if len(text_paragraph) == 0:
+                        continue
+                    sentences = re.split(r'(?<=[.])\s', text_paragraph)
+                    text = ''
+                    for sentence in sentences:
+                        text += sentence + ' '
+                        if any([entity['boundaries'][0] < len(text) + prev_len < entity['boundaries'][1] for entity in article['entities']]):
+                            continue
+                        entities = sorted([entity for entity in article['entities'] if prev_len < entity['boundaries'][1] <= len(text)+prev_len], key=lambda tup: tup['boundaries'][0])
+                        decoder_output = '<triplet> '
+                        for int_ent, entity in enumerate(entities):
+                            triplets = sorted([triplet for triplet in article['triples'] if triplet['subject'] == entity and prev_len< triplet['subject']['boundaries'][1]<=len(text) + prev_len and prev_len< triplet['object']['boundaries'][1]<=len(text)+ prev_len], key=lambda tup: tup['object']['boundaries'][0])
+                            if len(triplets) == 0:
+                                continue
+                            decoder_output += entity['surfaceform'] + ' <subj> '
+                            for triplet in triplets:
+                                decoder_output += triplet['object']['surfaceform'] + ' <obj> '  + triplet['predicate']['surfaceform'] + ' <subj> '
+                            decoder_output = decoder_output[:-len(' <subj> ')]
+                            decoder_output += ' <triplet> '
+                        decoder_output = decoder_output[:-len(' <triplet> ')]
+                        count += 1
+                        prev_len += len(text)
+
+                        if len(decoder_output) == 0:
+                            text = ''
+                            continue
+
+                        text = re.sub('([\[\].,!?()])', r' \1 ', text.replace('()', ''))
+                        text = re.sub('\s{2,}', ' ', text)
+
+                        yield article['docid'] + '-' + str(count), {
+                            "title": article['title'],
+                            "context": text,
+                            "id": article['uri'] + '-' + str(count),
+                            "triplets": decoder_output,
+                        }
+                        text = ''