zpwiki/pages/students/2021/manohar_gowdru_shridharu/README.md

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---
title: Manohar Gowdru Shridhara
published: true
taxonomy:
category: [phd2024]
tag: [lm,nlp,hatespeech]
author: Daniel Hladek
---
# Manohar Gowdru Shridhara
Beginning of the study: 2021
## Disertation Thesis
in 2023/24
Hate Speech Detection
Goals:
- Write a dissertaion thesis
- Publish 2 A-class journal papers
## Minimal Thesis
(preliminary dissertaion and exam in 2022/23)
Goals:
- Provide state-of-the-art overview.
- Formulate dissertation theses (describe scientific contribution of the thesis).
- Prepare to reach the scientific contribution.
- Publish 4 conference papers.
## First year of PhD study
Goals:
- Provide state-of-the-art overview.
- Read and make notes from at least 100 scientific papers or books.
- Publish at least 2 conference papers.
- Prepare for minimal thesis.
Resources:
- [Hate Speech Project Page](/topics/hatespeech)
- https://hatespeechdata.com/
- [Hate speech detection: Challenges and solutions](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701757/)
- [HateBase](https://hatebase.org/)
- [Resources and benchmark corpora for hate speech detection: a systematic review](https://link.springer.com/article/10.1007/s10579-020-09502-8)
## Meeting 10.3.22
- Improvement of the report.
- Installed Transformers and Anaconda
Tasks:
- Try [this model](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) with your own text.
- Learn how Transformers Neural Network Works. Learn how Roberta Model works Write a short memo about your findings and papers read on this topic.
- Grab baseline BERT type model and try to finetune it for sentiment classification.
## Meeting 21.2.22
- Written a report about HS detection (in progress)
Tasks:
- Repair the report (rewrite copied parts, make the paragrapsh be logically ordered, teoreticaly - formaly define the HS detection, analyze te datasets in detail - how do they work. what metric do they use).
- Install Hugging Face Transformers and come through a tutorial
## Meeting 31.1.22
- Read some blogs about transformers
- Installed and tied transformers
- Worked on the review paper
- Picked the Twitter Dataset on keggle
- still selecting a method
Open tasks:
- Continue to work on the paper and share the paper with us.
- Prepare som ideas for the common discussion about the project.
- [ ] Try to prepare an experiment with the selected dataset.
- [ ] You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
- [ ] Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
- [x] Get ready to post a paper on the school PhD conference SCYR, deadline is in the middle of February http://scyr.kpi.fei.tuke.sk/.
### Meeting 10.1.22
- Set up a git account https://github.com/ManoGS with script to prepare "twitter" dataset and "english" dataset for HS detection.
- confgured laptop with (Anaconda) / PyCharm, pytorch, cuda gone throug some basic python tutorials.
- Read some blogs how to use kaggle (dataset database).
- tutorials on huggingface transformers - understanding sentiment analysis.
Open tasks:
- [x] Continue to work on the review - with datasets and methods (specified below).
- [x] Read and make notes about transformers, neural language models and finentuning.
- [ ] Pick feasible dataset and method to start with.
- [ ] You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
- [ ] Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
- [ ] Get ready to post a paper on the school PhD conference SCYR, deadline is in the middle of February http://scyr.kpi.fei.tuke.sk/.
#### Meeting 16.12.21
- A report was provided (through Teams).
- Installed Anaconda and started s Transformers tutorial
- Started Dive into python book
Task:
- Report: Create a detailed list of available datasets for HS.
- Report: Create a detailed description of the state of the art approaches for HS detection.
- Practical: Continue with open tasks below. (pick datasetm, perform classification,evaluate the experiment.)
#### Meeting 10.12.21
No report (just draft) was provided so far.
1. Read papers from below and make notes what you have learned fro the papers. For each note make a bibliographic citation. Write down authors of the paper, name paper of the paper, year, publisher and other important information.
When you find out something, make a reference with a number to that paper.
You can use a bibliografic manager software. Mendeley, Endnote, Jabref.
2. From the papers find out answers to the questions below.
3. Pick a hatespeech dataset.
4. Pick an approach and Python library for HS classification.
5. Create a [GIT](https://git.kemt.fei.tuke.sk) repository and share your experiment files. Do not commit data files, just links how to download the files.
6. Perform and evaluate experiments.
#### Meeting 10.11.21
#### First tasks
Prepare a report where you will explain:
- what is hate speech detection,
- where and why you can use hate-speech detection,
- what are state-of-the-art methods for hate speech detection,
- how can you evaluate a hate-speech detection system,
- what datasets for hate-speech detection are available,
The report should properly cite scientific bibliographical sources.
Use a bibliography manager software, such as Mendeley.
Create a [VPN connection](https://uvt.tuke.sk/wps/portal/uv/sluzby/vzdialeny-pristup-vpn) to the university network to have access to the scientific databses. Use scientific indexes to discover literature:
- [Scopus](https://www.scopus.com/) (available from TUKE VPN)
- [Scholar](httyps://scholar.google.com)
Your review can start with:
- [Hate speech detection: Challenges and solutions](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701757/)
- [HateBase](https://hatebase.org/)
- [Resources and benchmark corpora for hate speech detection: a systematic review](https://link.springer.com/article/10.1007/s10579-020-09502-8)
Get to know the Python programming language
- Read [Dive into Python](https://diveintopython3.net/)
- Install [Anaconda](https://www.anaconda.com/)
- Try [HuggingFace Transformers library]( https://huggingface.co/transformers/quicktour.html)