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

---
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 12.4.

- Created repositories, empty so far.
- Tried to replicate the results from "Emotion and sentiment analysis of tweets using BERT" paper  and "Fine-Tuning BERT Based Approach for Multi-Class Sentiment Analysis on Twitter Emotion Data".
- The experiments are based on BERT (which kind?),  Tweet
Emotion Intensity. 
- Prepared colab notebook with experiments.

Tasks:

- Finish experiments, upload source codes into git, provide a description of the experiments.
- Try to improve the results - try different kind of BERT - roberta, electra, xl-net. Can "generative models" be used? (gpt, bart, t5). Can "sentence transformers be used" - labse, laser.
- Learn about "Sentence Transformers".
- Summarize the results in the table, publish the table on git. 
- Use Markdown for formatting. There is "Typora".
- Continue to improve the SCYR paper.
- If you have some conference in mind, tell me.

## Meeting 25.3.22

- Learned about Transformers, BERT, LSTM and RNN.
- Tried HuggingFace transformers library
- Started Google Colab - executing sentiment analysis, hf  transformers pipeline functions.
- prepared datasets: twitter-roberta Datasets. Experiments a re riunnig, no results yet.
- prepared a short note about nlp and neural networks. 
- still working on the SCYR paper

Tasks:

- [-] finish experiments about sentiment and present results.
- [-] create a repository on git.kemt.fei.tuke.sk and upload your experiments, results and notes. Use you student creadentials.
- [-] continue working on "SCYR" review paper, consider publishing it elswhere (the firs version got rejected).
- [-] prepare an outline for another paper with sentiment classification.

## 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 training  works. Learn how BERT model finetuning works. Write a short memo about your findings and papers read on this topic.
- Pick a dataset: 
    - https://huggingface.co/datasets/sentiment140 (english)
    - https://www.clarin.si/repository/xmlui/handle/11356/1054 (multilingua)
    - https://huggingface.co/datasets/tamilmixsentiment (english tamil code switch)
- Grab baseline BERT type model and try to finetune it for sentiment classification.
- For finetuning and evaluation you can use this scrip https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification
- For finetuning you will need to install CUDA and Pytorch. It can work on CPU or NOT.
- If you need GPU, use the school server idoc.fei.tuke.sk or google Colab.
- Continue working on the paper.
- Remind me about the SCYR conference payment.



## 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)