Update openthreatner.py

openthreatner.py

@@ -1,146 +1,165 @@
-# 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 Open Threat dataset"""
-
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = """\
-TBD
-"""
-
-_CITATION = """\
-TBD
-"""
-
-
-# replace this url with our repository or the backend url
-_URL = "https://raw.githubusercontent.com/leondz/emerging_entities_17/master/"
-_TRAINING_FILE = "wnut17train.conll"
-_DEV_FILE = "emerging.dev.conll"
-_TEST_FILE = "emerging.test.annotated"
-
-class OurDatasetConfig(datasets.BuilderConfig):
-    """The Open NER dataset."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for Open Threat dataset.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(OurDatasetConfig, self).__init__(**kwargs)
-
-
-class OurDataset(datasets.GeneratorBasedBuilder):
-    """The Open NER dataset Entities Dataset."""
-
-    BUILDER_CONFIGS = [
-        OurDatasetConfig(
-            name="Open Threat", version=datasets.Version("1.0.0"), description="The Open Cyber Threat 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(
-                        #TODO here: replace with the apprioriate list
-                        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,
-            #TODO replace here with our website
-            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}",
-            "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:
-                    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,
+# 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 Open Threat dataset"""
+
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+TBD
+"""
+
+_CITATION = """\
+TBD
+"""
+
+
+_URL = "https://huggingface.co/datasets/priamai/openthreatner/raw/main/conll/"
+_TRAINING_FILE = "text_32.conll"
+_DEV_FILE = "text_23.conll"
+_TEST_FILE = "text_1.conll"
+
+class OurDatasetConfig(datasets.BuilderConfig):
+    """The Open NER dataset."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Open Threat dataset.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(OurDatasetConfig, self).__init__(**kwargs)
+
+
+class OurDataset(datasets.GeneratorBasedBuilder):
+    """The Open NER dataset Entities Dataset."""
+
+    BUILDER_CONFIGS = [
+        OurDatasetConfig(
+            name="Open Threat", version=datasets.Version("1.0.0"), description="The Open Cyber Threat Entities Dataset"
+        ),
+    ]
+
+    def _info(self):
+
+        names = names = [
+                "O",
+                "B-date",
+                "I-date",
+                "B-time",
+                "I-time",
+                "B-geo_location",
+                "I-geo_location",
+                "B-organization",
+                "I-organization",
+                "B-sector",
+                "I-sector",
+                "B-threat_actor",
+                "I-threat_actor",
+                "B-exploit_name",
+                "I-exploit_name",
+                "B-malware",
+                "I-malware",
+                "B-os",
+                "I-os",
+                "B-software",
+                "I-software",
+                "B-hardware",
+                "I-hardware",
+                "B-username",
+                "I-username",
+                "B-ttp",
+                "I-ttp",
+                "B-code_cmd",
+                "I-code_cmd",
+                "B-classification",
+                "I-classification",
+            ]
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        #TODO here: replace with the apprioriate list
+                        datasets.features.ClassLabel(
+                            names= list(map(str.lower,names))
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://test.cti.tools/",
+            citation=_CITATION,
+        )
+
+    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:
+                    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,
                 }