Update testaunic23.py

testaunic23.py

@@ -1,159 +1,159 @@
-# 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
-"""test testaunic23"""
-
-import logging
-
-import datasets
-
-
-_CITATION = """\
-test testaunic23
-"""
-
-_DESCRIPTION = """\
-test testaunic23
-"""
-
-_URL = "https://github.com/andreyokamura-unicamp/test_dataset/"
-_TRAINING_FILE = "train.txt"
-_DEV_FILE = "dev.txt"
-_TEST_FILE = "test.txt"
-
-
-class testaunic23Config(datasets.BuilderConfig):
-    """BuilderConfig for testaunic23"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for testaunic23.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(testaunic23Config, self).__init__(**kwargs)
-
-
-class testaunic23(datasets.GeneratorBasedBuilder):
-    """testaunic23 dataset."""
-
-    BUILDER_CONFIGS = [
-        testaunic23Config(name="testaunic23", version=datasets.Version("1.0.0"), description="testaunic23 dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document_id": datasets.Value("int32"),
-                    "sentence_id": datasets.Value("int32"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "pos_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "-X-"
-                            ]
-                        )
-                    ),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-ENTITY",
-                                "I-ENTITY",
-                                "B-SYSTEM",
-                                "I-SYSTEM",
-                                "B-DOCUMENT",
-                                "I-DOCUMENT",
-                                "B-ORG",
-                                "I-ORG",
-                                "B-LOC",
-                                "I-LOC"
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="",
-            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):
-        logging.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            guid = 0
-            document_id = 0
-            sentence_id = 0
-            tokens = []
-            pos_tags = []
-            chunk_tags = []
-            ner_tags = []
-            for line in f:
-                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                    if line.startswith("-DOCSTART-"):
-                        document_id += 1
-                        sentence_id = 0
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "document_id": document_id,
-                            "sentence_id": sentence_id,
-                            "tokens": tokens,
-                            "pos_tags": pos_tags,
-                            "chunk_tags": chunk_tags,
-                            "ner_tags": ner_tags,
-                        }
-                        sentence_id += 1
-                        guid += 1
-                        tokens = []
-                        pos_tags = []
-                        chunk_tags = []
-                        ner_tags = []
-                else:
-                    # conll2003 tokens are space separated
-                    splits = line.split(" ")
-                    tokens.append(splits[0])
-                    pos_tags.append(splits[1])
-                    chunk_tags.append(splits[2])
-                    ner_tags.append(splits[3].rstrip())
-            # last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "document_id": document_id,
-                    "sentence_id": sentence_id,
-                    "tokens": tokens,
-                    "pos_tags": pos_tags,
-                    "chunk_tags": chunk_tags,
-                    "ner_tags": ner_tags,
+# 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
+"""test testaunic23"""
+
+import logging
+
+import datasets
+
+
+_CITATION = """\
+test testaunic23
+"""
+
+_DESCRIPTION = """\
+test testaunic23
+"""
+
+_URL = "https://raw.githubusercontent.com/andreyokamura-unicamp/test_dataset/refs/heads/main/"
+_TRAINING_FILE = "train.txt"
+_DEV_FILE = "dev.txt"
+_TEST_FILE = "test.txt"
+
+
+class testaunic23Config(datasets.BuilderConfig):
+    """BuilderConfig for testaunic23"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for testaunic23.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(testaunic23Config, self).__init__(**kwargs)
+
+
+class testaunic23(datasets.GeneratorBasedBuilder):
+    """testaunic23 dataset."""
+
+    BUILDER_CONFIGS = [
+        testaunic23Config(name="testaunic23", version=datasets.Version("1.0.0"), description="testaunic23 dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("int32"),
+                    "sentence_id": datasets.Value("int32"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "-X-"
+                            ]
+                        )
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-ENTITY",
+                                "I-ENTITY",
+                                "B-SYSTEM",
+                                "I-SYSTEM",
+                                "B-DOCUMENT",
+                                "I-DOCUMENT",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-LOC",
+                                "I-LOC"
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="",
+            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):
+        logging.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            document_id = 0
+            sentence_id = 0
+            tokens = []
+            pos_tags = []
+            chunk_tags = []
+            ner_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if line.startswith("-DOCSTART-"):
+                        document_id += 1
+                        sentence_id = 0
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "document_id": document_id,
+                            "sentence_id": sentence_id,
+                            "tokens": tokens,
+                            "pos_tags": pos_tags,
+                            "chunk_tags": chunk_tags,
+                            "ner_tags": ner_tags,
+                        }
+                        sentence_id += 1
+                        guid += 1
+                        tokens = []
+                        pos_tags = []
+                        chunk_tags = []
+                        ner_tags = []
+                else:
+                    # conll2003 tokens are space separated
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    pos_tags.append(splits[1])
+                    chunk_tags.append(splits[2])
+                    ner_tags.append(splits[3].rstrip())
+            # last example
+            if tokens:
+                yield guid, {
+                    "id": str(guid),
+                    "document_id": document_id,
+                    "sentence_id": sentence_id,
+                    "tokens": tokens,
+                    "pos_tags": pos_tags,
+                    "chunk_tags": chunk_tags,
+                    "ner_tags": ner_tags,
                 }