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README.md

@@ -1,341 +0,0 @@
----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- found
-license:
-- other
-multilinguality:
-- monolingual
-paperswithcode_id: acronym-identification
-pretty_name: >-
-  Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction
-  and Classification in Scientific Papers
-size_categories:
-- 1K<n<10K
-source_datasets: []
-tags:
-- Relation Classification
-- Relation extraction
-- Scientific papers
-- Research papers
-task_categories:
-- text-classification
-task_ids:
-- entity-linking-classification
-train-eval-index:
-- col_mapping:
-    labels: tags
-    tokens: tokens
-  config: default
-  splits:
-    eval_split: test
-  task: text-classification
-  task_id: entity_extraction
----
-# Dataset Card for SemEval2018Task7
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://lipn.univ-paris13.fr/~gabor/semeval2018task7/](https://lipn.univ-paris13.fr/~gabor/semeval2018task7/)
-- **Repository:** [https://github.com/gkata/SemEval2018Task7/tree/testing](https://github.com/gkata/SemEval2018Task7/tree/testing)
-- **Paper:** [SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers](https://aclanthology.org/S18-1111/)
-- **Leaderboard:** [https://competitions.codalab.org/competitions/17422#learn_the_details-overview](https://competitions.codalab.org/competitions/17422#learn_the_details-overview)
-- **Size of downloaded dataset files:** 1.93 MB
-
-### Dataset Summary
-
-Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction and Classification in Scientific Papers.
-The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.
-
-The three subtasks are:
-
-- Subtask 1.1: Relation classification on clean data
-  - In the training data, semantic relations are manually annotated between entities. 
-  - In the test data, only entity annotations and unlabeled relation instances are given.
-  - Given a scientific publication, The task is to predict the semantic relation between the entities.
-  
-- Subtask 1.2: Relation classification on noisy data
-  - Entity occurrences are automatically annotated in both the training and the test data.
-  - The task is to predict the semantic
-relation between the entities. 
-  
-- Subtask 2: Metrics for the extraction and classification scenario 
-  - Evaluation of relation extraction
-  - Evaluation of relation classification
-  
-The Relations types are USAGE, RESULT, MODEL, PART_WHOLE, TOPIC, COMPARISION.
-
-The following example shows a text snippet with the information provided in the test data:
-    Korean, a \<entity id=”H01-1041.10”>verb final language\</entity>with\<entity id=”H01-1041.11”>overt case markers\</entity>(...)
-  - A relation instance is identified by the unique identifier of the entities in the pair, e.g.(H01-1041.10, H01-1041.11)
-  - The information to be predicted is the relation class label: MODEL-FEATURE(H01-1041.10, H01-1041.11).
-For details, see the paper https://aclanthology.org/S18-1111/. 
-
-### Supported Tasks and Leaderboards
-
-- **Tasks:** Relation extraction and classification in scientific papers
-- **Leaderboards:** [https://competitions.codalab.org/competitions/17422#learn_the_details-overview](https://competitions.codalab.org/competitions/17422#learn_the_details-overview)
-
-### Languages
-
-The language in the dataset is English.
-
-## Dataset Structure
-
-### Data Instances
-
-#### subtask_1.1
-- **Size of downloaded dataset files:** 714 KB
-  
-An example of 'train' looks as follows:
-```json
-{
-  "id": "H01-1041", 
-  "title": "'Interlingua-Based Broad-Coverage Korean-to-English Translation in CCLING'",
-  "abstract": 'At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory) . The CCLINC Korean-to-English translation system consists of two core modules , language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame . The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers , relatively free word order , and frequent omissions of arguments ). (ii) High quality translation via word sense disambiguation and accurate word order generation of the target language . (iii) Rapid system development and porting to new domains via knowledge-based automated acquisition of grammars . Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces the translation output sufficient for content understanding of the original document.
-  "entities": [{'id': 'H01-1041.1', 'char_start': 54, 'char_end': 97},
-  {'id': 'H01-1041.2', 'char_start': 99, 'char_end': 161},
-  {'id': 'H01-1041.3', 'char_start': 169, 'char_end': 211},
-  {'id': 'H01-1041.4', 'char_start': 229, 'char_end': 240},
-  {'id': 'H01-1041.5', 'char_start': 244, 'char_end': 288},
-  {'id': 'H01-1041.6', 'char_start': 304, 'char_end': 342},
-  {'id': 'H01-1041.7', 'char_start': 353, 'char_end': 366},
-  {'id': 'H01-1041.8', 'char_start': 431, 'char_end': 437},
-  {'id': 'H01-1041.9', 'char_start': 442, 'char_end': 447},
-  {'id': 'H01-1041.10', 'char_start': 452, 'char_end': 470},
-  {'id': 'H01-1041.11', 'char_start': 477, 'char_end': 494},
-  {'id': 'H01-1041.12', 'char_start': 509, 'char_end': 523},
-  {'id': 'H01-1041.13', 'char_start': 553, 'char_end': 561},
-  {'id': 'H01-1041.14', 'char_start': 584, 'char_end': 594},
-  {'id': 'H01-1041.15', 'char_start': 600, 'char_end': 624},
-  {'id': 'H01-1041.16', 'char_start': 639, 'char_end': 659},
-  {'id': 'H01-1041.17', 'char_start': 668, 'char_end': 682},
-  {'id': 'H01-1041.18', 'char_start': 692, 'char_end': 715},
-  {'id': 'H01-1041.19', 'char_start': 736, 'char_end': 742},
-  {'id': 'H01-1041.20', 'char_start': 748, 'char_end': 796},
-  {'id': 'H01-1041.21', 'char_start': 823, 'char_end': 847},
-  {'id': 'H01-1041.22', 'char_start': 918, 'char_end': 935},
-  {'id': 'H01-1041.23', 'char_start': 981, 'char_end': 997}],
-}
-  "relation": [{'label': 3, 'arg1': 'H01-1041.3', 'arg2': 'H01-1041.4', 'reverse': True},
-  {'label': 0, 'arg1': 'H01-1041.8', 'arg2': 'H01-1041.9', 'reverse': False},
-  {'label': 2, 'arg1': 'H01-1041.10', 'arg2': 'H01-1041.11', 'reverse': True},
-  {'label': 0, 'arg1': 'H01-1041.14', 'arg2': 'H01-1041.15', 'reverse': True}]
-
-```
-#### Subtask_1.2
-- **Size of downloaded dataset files:** 1.00 MB
-  
-An example of 'train' looks as follows:
-```json
-{'id': 'L08-1450',
- 'title': '\nA LAF/GrAF based Encoding Scheme for underspecified Representations of syntactic Annotations.\n',
- 'abstract': 'Data models and encoding formats for syntactically annotated text corpora need to deal with syntactic ambiguity; underspecified representations are particularly well suited for the representation of ambiguousdata because they allow for high informational efficiency. We discuss the issue of being informationally efficient, and the trade-off between efficient encoding of linguistic annotations and complete documentation of linguistic analyses. The main topic of this article is adata model and an encoding scheme based on LAF/GrAF ( Ide and  Romary, 2006 ; Ide and  Suderman, 2007 ) which provides a flexible framework for encoding underspecified representations. We show how a set of dependency structures and a set of TiGer graphs ( Brants et al., 2002 ) representing the readings of an ambiguous sentence can be encoded, and we discuss basic issues in querying corpora which are encoded using the framework presented here.\n',
- 'entities': [{'id': 'L08-1450.4', 'char_start': 0, 'char_end': 3},
-  {'id': 'L08-1450.5', 'char_start': 5, 'char_end': 10},
-  {'id': 'L08-1450.6', 'char_start': 25, 'char_end': 31},
-  {'id': 'L08-1450.7', 'char_start': 61, 'char_end': 64},
-  {'id': 'L08-1450.8', 'char_start': 66, 'char_end': 72},
-  {'id': 'L08-1450.9', 'char_start': 82, 'char_end': 85},
-  {'id': 'L08-1450.10', 'char_start': 92, 'char_end': 100},
-  {'id': 'L08-1450.11', 'char_start': 102, 'char_end': 110},
-  {'id': 'L08-1450.12', 'char_start': 128, 'char_end': 142},
-  {'id': 'L08-1450.13', 'char_start': 181, 'char_end': 194},
-  {'id': 'L08-1450.14', 'char_start': 208, 'char_end': 211},
-  {'id': 'L08-1450.15', 'char_start': 255, 'char_end': 264},
-  {'id': 'L08-1450.16', 'char_start': 282, 'char_end': 286},
-  {'id': 'L08-1450.17', 'char_start': 408, 'char_end': 420},
-  {'id': 'L08-1450.18', 'char_start': 425, 'char_end': 443},
-  {'id': 'L08-1450.19', 'char_start': 450, 'char_end': 453},
-  {'id': 'L08-1450.20', 'char_start': 455, 'char_end': 459},
-  {'id': 'L08-1450.21', 'char_start': 481, 'char_end': 484},
-  {'id': 'L08-1450.22', 'char_start': 486, 'char_end': 490},
-  {'id': 'L08-1450.23', 'char_start': 508, 'char_end': 513},
-  {'id': 'L08-1450.24', 'char_start': 515, 'char_end': 519},
-  {'id': 'L08-1450.25', 'char_start': 535, 'char_end': 537},
-  {'id': 'L08-1450.26', 'char_start': 559, 'char_end': 561},
-  {'id': 'L08-1450.27', 'char_start': 591, 'char_end': 598},
-  {'id': 'L08-1450.28', 'char_start': 611, 'char_end': 619},
-  {'id': 'L08-1450.29', 'char_start': 649, 'char_end': 663},
-  {'id': 'L08-1450.30', 'char_start': 687, 'char_end': 707},
-  {'id': 'L08-1450.31', 'char_start': 722, 'char_end': 726},
-  {'id': 'L08-1450.32', 'char_start': 801, 'char_end': 808},
-  {'id': 'L08-1450.33', 'char_start': 841, 'char_end': 845},
-  {'id': 'L08-1450.34', 'char_start': 847, 'char_end': 852},
-  {'id': 'L08-1450.35', 'char_start': 857, 'char_end': 864},
-  {'id': 'L08-1450.36', 'char_start': 866, 'char_end': 872},
-  {'id': 'L08-1450.37', 'char_start': 902, 'char_end': 910},
-  {'id': 'L08-1450.1', 'char_start': 12, 'char_end': 16},
-  {'id': 'L08-1450.2', 'char_start': 27, 'char_end': 32},
-  {'id': 'L08-1450.3', 'char_start': 72, 'char_end': 80}],
- 'relation': [{'label': 1,
-   'arg1': 'L08-1450.12',
-   'arg2': 'L08-1450.13',
-   'reverse': False},
-  {'label': 5, 'arg1': 'L08-1450.17', 'arg2': 'L08-1450.18', 'reverse': False},
-  {'label': 1, 'arg1': 'L08-1450.28', 'arg2': 'L08-1450.29', 'reverse': False},
-  {'label': 3, 'arg1': 'L08-1450.30', 'arg2': 'L08-1450.32', 'reverse': False},
-  {'label': 3, 'arg1': 'L08-1450.34', 'arg2': 'L08-1450.35', 'reverse': False},
-  {'label': 3, 'arg1': 'L08-1450.36', 'arg2': 'L08-1450.37', 'reverse': True}]}
-[ ]
-
-```
-
-
-### Data Fields
-
-#### subtask_1_1
-- `id`: the instance id of this abstract, a `string` feature.
-- `title`: the title of this abstract, a `string` feature
-- `abstract`: the abstract from the scientific papers, a `string` feature
-- `entities`: the entity id's for the key phrases, a `list` of entity id's.
-    - `id`: the instance id of this sentence, a `string` feature.
-    - `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
-    - `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
-- `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
-    - `label`: the list of relations between the key phrases, a `list` of classification labels.
-    - `arg1`: the entity id of this key phrase, a `string` feature.
-    - `arg2`: the entity id of the related key phrase, a `string` feature.
-    - `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
-
-```python
-RELATIONS
-{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
-```
-
-#### subtask_1_2
-- `id`: the instance id of this abstract, a `string` feature.
-- `title`: the title of this abstract, a `string` feature
-- `abstract`: the abstract from the scientific papers, a `string` feature
-- `entities`: the entity id's for the key phrases, a `list` of entity id's.
-    - `id`: the instance id of this sentence, a `string` feature.
-    - `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
-    - `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
-- `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
-    - `label`: the list of relations between the key phrases, a `list` of classification labels.
-    - `arg1`: the entity id of this key phrase, a `string` feature.
-    - `arg2`: the entity id of the related key phrase, a `string` feature.
-    - `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
-
-```python
-RELATIONS
-{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
-```
-
-
-### Data Splits
-
-|             |           | Train| Test |
-|-------------|-----------|------|------|
-| subtask_1_1 | text      | 2807 | 3326 |
-|             | relations | 1228 | 1248 |
-| subtask_1_2 | text      | 1196 | 1193 |
-|             | relations |  335 |  355 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@inproceedings{gabor-etal-2018-semeval,
-    title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
-    author = {G{\'a}bor, Kata  and
-      Buscaldi, Davide  and
-      Schumann, Anne-Kathrin  and
-      QasemiZadeh, Behrang  and
-      Zargayouna, Ha{\"\i}fa  and
-      Charnois, Thierry},
-    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
-    month = jun,
-    year = "2018",
-    address = "New Orleans, Louisiana",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/S18-1111",
-    doi = "10.18653/v1/S18-1111",
-    pages = "679--688",
-    abstract = "This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.",
-}
-```
-### Contributions
-
-Thanks to [@basvoju](https://github.com/basvoju) for adding this dataset.

SemEval2018Task7.py

@@ -1,308 +0,0 @@
-# I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
-# Use the original source from https://huggingface.co/datasets/DFKI-SLT/science_ie/raw/main/science_ie.py
-
-
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""Semeval2018Task7 is a dataset that describes the first task on semantic relation extraction and classification in scientific paper abstracts"""  
-
-
-
-import glob
-import datasets
-import xml.dom.minidom
-import xml.etree.ElementTree as ET
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@inproceedings{gabor-etal-2018-semeval,
-    title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
-    author = {G{\'a}bor, Kata  and
-      Buscaldi, Davide  and
-      Schumann, Anne-Kathrin  and
-      QasemiZadeh, Behrang  and
-      Zargayouna, Ha{\"\i}fa  and
-      Charnois, Thierry},
-    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
-    month = jun,
-    year = "2018",
-    address = "New Orleans, Louisiana",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/S18-1111",
-    doi = "10.18653/v1/S18-1111",
-    pages = "679--688",
-    abstract = "This paper describes the first task on semantic relation extraction and classification in 
-    scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations 
-    and includes three different subtasks. The subtasks were designed so as to compare and quantify the 
-    effect of different pre-processing steps on the relation classification results. We expect the task to 
-    be relevant for a broad range of researchers working on extracting specialized knowledge from domain 
-    corpora, for example but not limited to scientific or bio-medical information extraction. The task 
-    attracted a total of 32 participants, with 158 submissions across different scenarios.",
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-This paper describes the first task on semantic relation extraction and classification in scientific paper
-abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three 
-different subtasks. The subtasks were designed so as to compare and quantify the effect of different
-pre-processing steps on the relation classification results. We expect the task to be relevant for a broad 
-range of researchers working on extracting specialized knowledge from domain corpora, for example but not 
-limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, 
-with 158 submissions across different scenarios.
-"""
-
-# Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"
-
-# Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "Subtask_1_1": {
-        "train": {
-            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.relations.txt",
-            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.text.xml",
-        },
-        "test": {
-            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.relations.txt",
-            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.text.xml",
-        },
-    },
-    "Subtask_1_2": {
-        "train": {
-            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.relations.txt",
-            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.text.xml",
-        },
-        "test": {
-            "relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.relations.txt",
-            "text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.text.xml",
-        },
-    },
-    
-}
-
-
-def all_text_nodes(root):
-    if root.text is not None:
-        yield root.text
-    for child in root:
-        if child.tail is not None:
-            yield child.tail
-
-
-def reading_entity_data(ET_data_to_convert):
-  parsed_data = ET.tostring(ET_data_to_convert,"utf-8")
-  parsed_data= parsed_data.decode('utf8').replace("b\'","")
-  parsed_data= parsed_data.replace("<abstract>","")
-  parsed_data= parsed_data.replace("</abstract>","")
-  parsed_data= parsed_data.replace("<title>","")
-  parsed_data= parsed_data.replace("</title>","")
-  parsed_data = parsed_data.replace("\n\n\n","")
-
-  parsing_tag = False
-  final_string = ""
-  tag_string= ""
-  current_tag_id = ""
-  current_tag_starting_pos = 0
-  current_tag_ending_pos= 0
-  entity_mapping_list=[]
-  
-  for i in parsed_data:
-    if i=='<':
-      parsing_tag = True
-      if current_tag_id!="":
-        current_tag_ending_pos = len(final_string)-1
-        entity_mapping_list.append({"id":current_tag_id,
-                                    "char_start":current_tag_starting_pos,
-                                    "char_end":current_tag_ending_pos+1})
-        current_tag_id= ""
-        tag_string=""
-
-
-    elif i=='>':
-      parsing_tag = False
-      tag_string_split = tag_string.split('"')
-      if len(tag_string_split)>1:
-        current_tag_id= tag_string.split('"')[1]
-        current_tag_starting_pos = len(final_string)
-
-    else:
-      if parsing_tag!=True:
-        final_string = final_string + i
-      else:
-        tag_string = tag_string + i
-
-  return {"text_data":final_string, "entities":entity_mapping_list}
-
-
-
-class Semeval2018Task7(datasets.GeneratorBasedBuilder):
-    """
-    Semeval2018Task7 is a dataset for semantic relation extraction and classification in scientific paper abstracts
-    """
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="Subtask_1_1", version=VERSION,
-                               description="Relation classification on clean data"),
-        datasets.BuilderConfig(name="Subtask_1_2", version=VERSION,
-                               description="Relation classification on noisy data"),
-        
-    ]
-    DEFAULT_CONFIG_NAME = "Subtask_1_1"
-
-    def _info(self):
-        class_labels = ["","USAGE", "RESULT", "MODEL-FEATURE", "PART_WHOLE", "TOPIC", "COMPARE"]
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "title": datasets.Value("string"),
-                "abstract": datasets.Value("string"),
-                "entities": [
-                    {
-                        "id": datasets.Value("string"),
-                        "char_start": datasets.Value("int32"),
-                        "char_end": datasets.Value("int32")
-                    }
-                ],
-                "relation": [
-                    {
-                        "label": datasets.ClassLabel(names=class_labels),
-                        "arg1": datasets.Value("string"),
-                        "arg2": datasets.Value("string"),
-                        "reverse": datasets.Value("bool")
-                    }
-                ]
-            }
-        )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        downloaded_files = dl_manager.download(urls)
-        print(downloaded_files)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "relation_filepath": downloaded_files['train']["relations"],
-                    "text_filepath": downloaded_files['train']["text"],
-                    
-                }
-
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "relation_filepath": downloaded_files['test']["relations"],
-                    "text_filepath": downloaded_files['test']["text"],
-                    
-                }
-
-            )]
-        
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, relation_filepath, text_filepath):
-        
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(relation_filepath, encoding="utf-8") as f:
-            relations = []
-            text_id_to_relations_map= {}
-            for key, row in enumerate(f):
-                row_split = row.strip("\n").split("(")
-                use_case = row_split[0]
-                second_half = row_split[1].strip(")")
-                second_half_splits = second_half.split(",")
-                size = len(second_half_splits)
-                
-                relation = {
-                    "label": use_case,
-                    "arg1": second_half_splits[0],
-                    "arg2": second_half_splits[1],
-                    "reverse": True if size == 3 else False
-                }
-                relations.append(relation)
-                
-                arg_id = second_half_splits[0].split(".")[0]
-                if arg_id not in text_id_to_relations_map:
-                  text_id_to_relations_map[arg_id] = [relation]
-                else:
-                  text_id_to_relations_map[arg_id].append(relation)
-                  #print("result", text_id_to_relations_map)
-
-                #for arg_id, values in text_id_to_relations_map.items():
-                  #print(f"ID: {arg_id}")
-                 # for value in values:
-                  #  (value)
-
-                
-
-        doc2 = ET.parse(text_filepath)
-        root = doc2.getroot()
-        
-        for child in root:
-          if child.find("title")==None: 
-            continue
-          text_id = child.attrib
-          #print("text_id", text_id)
-
-          if child.find("abstract")==None: 
-            continue
-          title = child.find("title").text
-          child_abstract = child.find("abstract")
-          
-          
-          abstract_text_and_entities = reading_entity_data(child.find("abstract"))
-          title_text_and_entities = reading_entity_data(child.find("title"))
-          
-          text_relations = []
-          if text_id['id'] in text_id_to_relations_map:
-            text_relations = text_id_to_relations_map[text_id['id']]
-
-          yield text_id['id'], {
-              "id": text_id['id'],
-              "title": title_text_and_entities['text_data'],
-              "abstract": abstract_text_and_entities['text_data'],
-              "entities": abstract_text_and_entities['entities'] + title_text_and_entities['entities'],
-              "relation": text_relations
-            }

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datasets_info.json

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-"features": {"id": {"dtype": "string", "id": null, "_type": "Value"},
-"title": {"dtype": "string", "id": null, "_type": "Value"}, 
-"abstract": {"dtype": "string", "id": null, "_type": "Value"}, 
-"entities": {feature:{"id": "string", "id": null, "_type": "Value"}, "char_start": {"dtype": "int32", "id": null, "_type": "Value"}, "char_end": {"dtype": "int32", "id": null, "_type": "Value"}}, 
-"relation": {"feature": {"label": {"dtype": "list", "id": null, "_type": "ClassLabel"}, 
-"arg1": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "reverse": {"dtype": "Bool", "id": null, "_type": "Bool"}}}, 
-
-
-"post_processed": null, "supervised_keys": null, "task_templates": [{"task": "relation_classification"}], 
-"builder_name": "Basvoju/SemEval2018Task7", "config_name": {"Subtask_1_1","Subtask_1_2"}, 
-"version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0},
-
-
-"splits": {"train": {"name": "train", "num_bytes": '1240.8 KB', "num_examples": 8609, "dataset_name": "Basvoju/SemEval2018Task7"}, 
-"test": {"name": "test", "num_bytes": '506.93 KB' , "num_examples": 3079, "dataset_name": "Basvoju/SemEval2018Task7"}}, 
-"download_size": '1.93 MB', "post_processing_size": null, "size_in_bytes": '1.93 MB'}