Create README.md

README.md

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+---
+Annotator:
+- Abhinav Walia (Owner)
+language:
+- en
+tags:
+- translation
+license: 
+  - Database Open Database 
+  - Contents Database Contents
+---
+
+**Date**: 2022-07-10<br/>
+**Files**: ner_dataset.csv<br/>
+**Source**: [Kaggle entity annotated corpus](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)<br/>
+**notes**: The dataset only contains the tokens and ner tag labels. Labels are uppercase.
+  
+# About Dataset
+[**from Kaggle Datasets**](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)
+## Context:
+Annotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set.
+
+Tip: Use Pandas Dataframe to load dataset if using Python for convenience.
+
+## Content:
+This is the extract from GMB corpus which is tagged, annotated and built specifically to train the classifier to predict named entities such as name, location, etc.
+
+Number of tagged entities:
+
+'O': 1146068', geo-nam': 58388, 'org-nam': 48034, 'per-nam': 23790, 'gpe-nam': 20680, 'tim-dat': 12786, 'tim-dow': 11404, 'per-tit': 9800, 'per-fam': 8152, 'tim-yoc': 5290, 'tim-moy': 4262, 'per-giv': 2413, 'tim-clo': 891, 'art-nam': 866, 'eve-nam': 602, 'nat-nam': 300, 'tim-nam': 146, 'eve-ord': 107, 'per-ini': 60, 'org-leg': 60, 'per-ord': 38, 'tim-dom': 10, 'per-mid': 1, 'art-add': 1
+
+## Essential info about entities:
+
+* geo = Geographical Entity
+* org = Organization
+* per = Person
+* gpe = Geopolitical Entity
+* tim = Time indicator
+* art = Artifact
+* eve = Event
+* nat = Natural Phenomenon
+* Total Words Count = 1354149
+* Target Data Column: "tag" (ner_tag in this repo)
+
+Inspiration: This dataset is getting more interested because of more features added to the recent version of this dataset. Also, it helps to create a broad view of Feature Engineering with respect to this dataset.
+ 
+
+