How to prepare data for Named Entity Recognition with BIO annotation? Assume the task here is extracting important facts for resume like a candidate skills and his education etc.., Here is resume is parsed text from pdf or docx resume.

First, I'll obtain skills and education data from various online websites, job portals etc.., the obtaining data will be two text files and in each file every row represent a skills or a university name like below,

skills.txt 
___________
c 
python 
java
node js


education.txt 
___________
massachusetts institute of Technology 
harvard university 


I want to know if these be enough to be able to train a named entity recognition model to recognize skills and education for raw resume text. The data I have is not sentences but just entities. I've read somewhere that we require some context along with the entity for NER model to learn better. Like this example below,

skills.txt 
___________
c is used at facebook
python is my favorite programming language


If I use my collected data education.txt to train the modeel and BIO annotate them then it will be like below, it won't have O-Other token.

massachusetts B-EDU
institute I-EDU
of I-EDU
Technology I-EDU

harvard B-EDU
university I-EDU

indian B-EDU
institute I-EDU
of I-EDU
technology I-EDU


But I don't know how to access such data for my resume-extraction problem. How do I proceed further?  How to build an effective NER model for my resume facts identification domain-specific task? Any inputs/suggestions would really help.
If your data always looks like this, there is little reason to use sequence labeling: every token belongs to an entity, so it's just a matter of correctly separating the entities and classifying them. But since the entities are already separated by line breaks, there's no need to train a model to separate them. So in the end you just have to classify the entities by category, and this doesn't require sequence labeling. But even for that, from your example it looks like the skills vs. education entities are already separated, so in the end I'm not sure what you want the model to learn?