Create MININER.py

MININER.py

@@ -0,0 +1,126 @@
+# coding=utf-8
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_URL = "https://huggingface.co/datasets/adalbertojunior/MININER/resolve/main/"
+_TRAIN_FILE = "train.conll"
+_TEST_FILE = "test.conll"
+
+
+class Harem(datasets.GeneratorBasedBuilder):
+    """Harem dataset."""
+    
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name='MININER',version=VERSION,description="MININER dataset"),
+    ]
+
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                            ]
+                        )
+                    ),
+                    "chunk_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                            ]
+                        )
+                    ),      
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-PESSOA",
+                                "I-PESSOA",
+                                "B-TEMPO",
+                                "I-TEMPO",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        #
+        urls_to_download = {
+            "train": f"{_URL}{_TRAIN_FILE}",
+            "dev": f"{_URL}{_TRAIN_FILE}",
+            "test": f"{_URL}{_TRAIN_FILE}",
+        }
+        
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_files["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloaded_files["dev"], "split": "dev"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": downloaded_files["test"], "split": "test"},
+            ),            
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        
+        logger.info("⏳ Generating examples from = %s", filepath)
+        
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            pos_tags = []
+            chunk_tags = []
+            ner_tags = []
+            
+            for line in f:
+                if line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "pos_tags": pos_tags,
+                            "chunk_tags": chunk_tags,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        pos_tags = []
+                        chunk_tags = []
+                        ner_tags = []
+                        
+                else:
+                     splits = line.split(" ")
+                     tokens.append(splits[0])
+                     pos_tags.append(splits[1])
+                     chunk_tags.append(splits[2])
+                     ner_tags.append(splits[-1].rstrip())                         
+                         
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "pos_tags": pos_tags,
+                "chunk_tags": chunk_tags,
+                "ner_tags": ner_tags,
+            }