Create shipping_label_ner.py

shipping_label_ner.py

@@ -0,0 +1,172 @@
+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""The WNUT 17 Emerging Entities Dataset."""
+
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{derczynski-etal-2017-results,
+    title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
+    author = "Derczynski, Leon  and
+      Nichols, Eric  and
+      van Erp, Marieke  and
+      Limsopatham, Nut",
+    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
+    month = sep,
+    year = "2017",
+    address = "Copenhagen, Denmark",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/W17-4418",
+    doi = "10.18653/v1/W17-4418",
+    pages = "140--147",
+    abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
+                Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization),
+                but recall on them is a real problem in noisy text - even among annotators.
+                This drop tends to be due to novel entities and surface forms.
+                Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'}
+                hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities,
+                and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the
+                ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
+}
+"""
+
+_DESCRIPTION = """\
+WNUT 17: Emerging and Rare entity recognition
+
+This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
+Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation),
+but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms.
+Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve.
+This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.
+
+The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.
+"""
+
+_URL = "https://raw.githubusercontent.com/SanghaviHarshPankajkumar/shipping_label_project/main/"
+_TRAINING_FILE = "train.txt"
+_TEST_FILE = "test.txt"
+
+
+class WNUT_17Config(datasets.BuilderConfig):
+    """The WNUT 17 Emerging Entities Dataset."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for WNUT 17.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WNUT_17Config, self).__init__(**kwargs)
+
+
+class WNUT_17(datasets.GeneratorBasedBuilder):
+    """The WNUT 17 Emerging Entities Dataset."""
+
+    BUILDER_CONFIGS = [
+        WNUT_17Config(
+            name="wnut_17", version=datasets.Version("1.0.0"), description="The WNUT 17 Emerging Entities Dataset"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            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,
+            homepage="http://noisy-text.github.io/2017/emerging-rare-entities.html",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_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.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:
+                    token, label = row.split("\t")
+                    current_tokens.append(token)
+                    current_labels.append(label)
+                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 last sentence in dataset 🧐
+            if current_tokens:
+                yield sentence_counter, {
+                    "id": str(sentence_counter),
+                    "tokens": current_tokens,
+                    "ner_tags": current_labels,
+                }