Update from sam

argument_mining_dataloader.py

@@ -0,0 +1,132 @@
+# 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
+"""
+Arguement Mining Dataset created by Stab , Gurevych et. al. CL 2017 
+"""
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{stab2017parsing,
+  title={Parsing argumentation structures in persuasive essays},
+  author={Stab, Christian and Gurevych, Iryna},
+  journal={Computational Linguistics},
+  volume={43},
+  number={3},
+  pages={619--659},
+  year={2017},
+  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
+}
+"""
+
+_DESCRIPTION = """\
+tokens along with chunk id. Begining of arguement denoted by Arg_B,inside arguement
+denoted by Arg_I, other chunks are O
+Orginial train,test split as used by the paper is provided
+"""
+
+_URL = "https://raw.githubusercontent.com/Sam131112/Argument-Mining-Dataset/main/"
+_TRAINING_FILE = "train.txt"
+_TEST_FILE = "test.txt"
+
+
+class ArguementMiningCL2017Config(datasets.BuilderConfig):
+    """BuilderConfig for CL2017"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forCl2017.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Conll2003Config, self).__init__(**kwargs)
+
+
+class Conll2003(datasets.GeneratorBasedBuilder):
+    """Conll2003 dataset."""
+
+    BUILDER_CONFIGS = [
+        Conll2003Config(name="cl2017", version=datasets.Version("1.0.0"), description="Cl2017 dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "chunk_tags":datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "Arg_B",
+                                "Arg_I",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://direct.mit.edu/coli/article/43/3/619/1573/Parsing-Argumentation-Structures-in-Persuasive",
+            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:
+            guid = 0
+            tokens = []
+            pos_tags = []
+            chunk_tags = []
+            ner_tags = []
+            for line in f:
+                if line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "chunk_tags": chunk_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        chunk_tags = []
+                else:
+                    # conll2003 tokens are space separated
+                    splits = line.split("\t")
+                    tokens.append(splits[0])
+                    chunk_tags.append(splits[1])
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "chunk_tags": chunk_tags,
+            }