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README.md

@@ -1,96 +1,81 @@
 ---
-license: apache-2.0
-task_categories:
-- token-classification
-language:
-- en
+dataset_info:
+- config_name: Seth
+  features:
+  - name: id
+    dtype: int32
+  - name: tokens
+    sequence: string
+  - name: labels
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-Gene
+          '2': B-SNP
+          '3': I-SNP
+          '4': I-Gene
+          '5': B-RS
+          '6': I-RS
+  splits:
+  - name: train
+    num_bytes: 1812838
+    num_examples: 504
+  - name: test
+    num_bytes: 438476
+    num_examples: 126
+  download_size: 0
+  dataset_size: 2251314
+- config_name: Seth2003
+  features:
+  - name: id
+    dtype: int32
+  - name: tokens
+    sequence: string
+  - name: labels
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-Gene
+          '2': B-SNP
+          '3': I-SNP
+          '4': I-Gene
+          '5': B-RS
+          '6': I-RS
+  splits:
+  - name: train
+    num_bytes: 1812838
+    num_examples: 504
+  - name: test
+    num_bytes: 438476
+    num_examples: 126
+  download_size: 0
+  dataset_size: 2251314
+- config_name: conll2003
+  features:
+  - name: id
+    dtype: int32
+  - name: tokens
+    sequence: string
+  - name: labels
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-Gene
+          '2': B-SNP
+          '3': I-SNP
+          '4': I-Gene
+          '5': B-RS
+          '6': I-RS
+  splits:
+  - name: train
+    num_bytes: 1812838
+    num_examples: 504
+  - name: test
+    num_bytes: 438476
+    num_examples: 126
+  download_size: 0
+  dataset_size: 2251314
 ---
-# Dataset Card for SETH Dataset
-
-<!-- Provide a quick summary of the dataset. -->
-
-This is a dataset used to apply the DistilBert for NER Task in the Advanced Machine Learing and XAI course of the DHBW CAS in Heilbronn
-
-## Dataset Details
-
-### Dataset Description
-
-<!-- Provide a longer summary of what this dataset is. -->
-The Dataset is based on the Data that is Provided by the [Github Repositoriy](https://github.com/Erechtheus/mutationCorpora)
-
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [Source of the Dataset](https://github.com/Erechtheus/mutationCorpora/tree/master/corpora/IOB)
-- **Information about Dataset:** [Datset Information](https://rockt.github.io/SETH/)
-
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-Used for the Advanced Machine Learining and XAI course of DHBW CAS in Heilbronn
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-tbd
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-tbd
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-tbd
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
-Easy Loading for further uses when executing the training
-
-[More Information Needed]
-
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-tbd
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-tbd
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-Source Data is produced by [Github Repositoriy](https://github.com/Erechtheus/mutationCorpora) as described in the Github Repository
-[More Information Needed]
-
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-tbd
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-tbd
-
-**BibTeX:**
-

seth.py

@@ -0,0 +1,158 @@
+# 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
+"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
+
+import re
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@Article{SETH2016,
+  Title= {SETH detects and normalizes genetic variants in text.},
+  Author= {Thomas, Philippe and Rockt{\"{a}}schel, Tim and Hakenberg, J{\"{o}}rg and Lichtblau, Yvonne and Leser, Ulf},
+  Journal= {Bioinformatics},
+  Year= {2016},
+  Month= {Jun},
+  Doi= {10.1093/bioinformatics/btw234},  
+  Language = {eng},
+  Medline-pst = {aheadofprint},
+  Pmid = {27256315},
+  Url = {http://dx.doi.org/10.1093/bioinformatics/btw234} Titel anhand dieser DOI in Citavi-Projekt übernehmen
+}
+"""
+
+_DESCRIPTION = """\
+This Dataset is used to for the Advanced Machine Learning and XAI course of the DHBW CAS in Heilbronn
+"""
+
+
+
+class SethConfig(datasets.BuilderConfig):
+    """BuilderConfig for Seth Dataset"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Seth.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SethConfig, self).__init__(**kwargs)
+
+
+class Seth(datasets.GeneratorBasedBuilder):
+    """Seth dataset."""
+
+    BUILDER_CONFIGS = [
+        SethConfig(name="Seth", version=datasets.Version("1.0.0"), description="Seth dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "labels": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-Gene",
+                                "B-SNP",
+                                "I-SNP",
+                                "I-Gene",
+                                "B-RS",
+                                "I-RS"
+                            ]
+                        )
+                    )
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://rockt.github.io/SETH/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        #downloaded_file = dl_manager.download_and_extract(_URL)
+        data_files = {
+            "train": "./SETH-train.iob",
+            "test": "./SETH-test.iob",
+        }
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            document = {"id":None,
+            "tokens":[],
+            "labels":[]
+            }
+            documents = [] # Wird befüllt mit den Documented aus der Datei. Besteht aus einem Key "tokens" und "labels"
+            pattern = r"#\d+" # Reg Experassion um eine Documented ID zu detektieren
+            for idx, line in enumerate(f):
+                match = re.match(pattern, line)
+        #Überspringe erste Zeile weil das ein Header ist
+                if idx == 0:
+                    continue
+                # Here ist die Dokumenten ID
+                if match:
+                    if document["id"] != None:
+                        # Speichere altes Dokument bevor ein neues angelegt wird
+                        documents.append(document)
+                        yield guid,document
+                        guid+=1
+                        document = {"id":int(line[1:]), # Speichere nur die Nummer ohne die Raute
+                                    "tokens":[],
+                                    "labels":[]
+                                    }
+                    else:
+                        #Initialisiere neues DOkument
+                        document = {"id":int(line[1:]), # Speichere nur die Nummer ohne die Raute
+                                    "tokens":[],
+                                    "labels":[]
+                                    }
+                # Hier handeln wir die Sonderfälle ab
+                elif len(line.split(",")) >2:
+                    # Sonderfall 1: ,,Label
+                    if(line.split(",")[0] == "" and line.split(",")[1]==""):
+                        document["tokens"].append(",")
+                        document["labels"].append(line.split(",")[2].split("\n")[0])
+                    # Sonderfall 2:Text,Text,Test,Label -> Label steht immer am schluss
+                    else:
+                        document["tokens"].append(",".join(line.split(",")[0:-1])) # Bringe die Splits wieder zusammen ohne das Label
+                        document["labels"].append(line.split(",")[-1].split("\n")[0])     
+                # Sonst gehen wir einfach vom standard aus Word sowie Tag
+                else:
+                    word_tag = line.split(",")
+                # Hier erkennen wir den Ende eines Satzes dieser besteht aus " , "
+                    if word_tag[0] == " " and word_tag[1] == " \n":
+                        continue
+                    document["tokens"].append(word_tag[0])
+                    document["labels"].append(word_tag[1].split("\n")[0])
+            documents.append(document)
+            yield guid,document