Update README.md

README.md

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 # MACCROBAT-biomedical-ner
+This data is the same data from [here](https://figshare.com/articles/dataset/MACCROBAT2018/9764942), the only difference is that it has been converted into the Huggingface dataset format. So it can be easily loaded and can be used wherever need.
+
+To convert from the orginal format to huggingface dataset format, followed the following steps (**To know in more detail look at the `create_dataset.py` file**):
+* Read corresponding `*.txt` and `*.ann` file.
+* Used `pandas` to convert the `*.ann` file into dataframe.
+* After converting into dataframe, did some processing and converted NER label information into:
+  ```JSON
+    {
+      "text": "ner-text",
+      "label": "ner-label",
+      "start": 10,
+      "end": 20
+    }
+    ```
+* Standard labels are converted into `B-Tag` and `I-tag`, where `B`- stands for begning of the tag and `I` - stands for inside the tag.
+* Finally the JSON is created and uploaded here.
+
+
+## Source Data
+This ZIP-compressed file contains 200 source documents (in plain text, on sentence per line) and 200 annotation documents (in brat standoff format). Documents are named using PubMed document IDs, e.g. "15939911.txt" contains text from the document "A young man with palpitations and Ebstein's anomaly of the tricuspid valve" by Marcu and Donohue. Text is from PubMed Central full-text documents but has been edited to include only clinical case report details. All annotations were created manually.
+
+"MACCROBAT2020" is the second release of this dataset, following "MACCROBAT2018". The consistency and format of annotations has been improved in the newest version.
 
 
 
 ## Uses
+Use below snippet to load the data properly and it can be used to finetune medical based NER model with some additional processing.
+
 ```Python
 import datasets
 from datasets import load_dataset