Update ner.py

ner.py

@@ -1,100 +1 @@
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/train.txt"
-
-class indian_namesConfig(datasets.BuilderConfig):
-    """The WNUT 17 Emerging Entities Dataset."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for WNUT 17.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(indian_namesConfig, self).__init__(**kwargs)
-
-class indian_names(datasets.GeneratorBasedBuilder):
-    """The WNUT 17 Emerging Entities Dataset."""
-
-    BUILDER_CONFIGS = [
-        indian_namesConfig(
-            name="indian_names", version=datasets.Version("1.0.0"), description="The WNUT 17 Emerging Entities Dataset"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-corporation",
-                                "I-corporation",
-                                "B-person",
-                                "I-person"
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-        ]
-            
-    def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            current_tokens = []
-            current_labels = []
-            sentence_counter = 0
-            for row in f:
-                row = row.rstrip()
-                if row:
-                    if "\t" in row:
-                        token, label = row.split("\t")
-                        current_tokens.append(token)
-                        current_labels.append(label)
-                    else:
-                        # Handle cases where the delimiter is missing
-                        # You can choose to skip these rows or handle them differently
-                        logger.warning(f"Delimiter missing in row: {row}")
-                else:
-                    # New sentence
-                    if not current_tokens:
-                        # Consecutive empty lines will cause empty sentences
-                        continue
-                    assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
-                    sentence = (
-                        sentence_counter,
-                        {
-                            "id": str(sentence_counter),
-                            "tokens": current_tokens,
-                            "ner_tags": current_labels,
-                        },
-                    )
-                    sentence_counter += 1
-                    current_tokens = []
-                    current_labels = []
-                    yield sentence
-            # Don't forget the last sentence in the dataset 🧐
-            if current_tokens:
-                yield sentence_counter, {
-                    "id": str(sentence_counter),
-                    "tokens": current_tokens,
-                    "ner_tags": current_labels,
-                }
+import datasets logger = datasets.logging.get_logger(__name__) _URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/" _TRAINING_FILE = "Indian_dataset_wnut_train.conll" # _DEV_FILE = "indian_dataset.conll" _TEST_FILE = "emerging.test.annotated" class indian_namesConfig(datasets.BuilderConfig): """The WNUT 17 Emerging Entities Dataset.""" def __init__(self, **kwargs): """BuilderConfig for WNUT 17. Args: **kwargs: keyword arguments forwarded to super. """ super(indian_namesConfig, self).__init__(**kwargs) class indian_names(datasets.GeneratorBasedBuilder): """The WNUT 17 Emerging Entities Dataset.""" BUILDER_CONFIGS = [ indian_namesConfig( name="indian_names", version=datasets.Version("1.0.0"), description="The WNUT 17 Emerging Entities Dataset" ), ] def _info(self): return datasets.DatasetInfo( features=datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-corporation", "I-corporation", "B-creative-work", "I-creative-work", "B-group", "I-group", "B-location", "I-location", "B-person", "I-person", "B-product", "I-product", ] ) ), } ), supervised_keys=None, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" urls_to_download = { "train": f"{_URL}{_TRAINING_FILE}", # "dev": f"{_URL}{_DEV_FILE}", "test": f"{_URL}{_TEST_FILE}", } downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), # datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), ] def _generate_examples(self, filepath): logger.info("⏳ Generating examples from = %s", filepath) with open(filepath, encoding="utf-8") as f: current_tokens = [] current_labels = [] sentence_counter = 0 for row in f: row = row.rstrip() if row: if "\t" in row: token, label = row.split("\t") current_tokens.append(token) current_labels.append(label) else: # Handle cases where the delimiter is missing # You can choose to skip these rows or handle them differently logger.warning(f"Delimiter missing in row: {row}") else: # New sentence if not current_tokens: # Consecutive empty lines will cause empty sentences continue assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels" sentence = ( sentence_counter, { "id": str(sentence_counter), "tokens": current_tokens, "ner_tags": current_labels, }, ) sentence_counter += 1 current_tokens = [] current_labels = [] yield sentence # Don't forget the last sentence in the dataset 🧐 if current_tokens: yield sentence_counter, { "id": str(sentence_counter), "tokens": current_tokens, "ner_tags": current_labels, }