https://github.com/macabdul9/reddit-flait-detector
Reddit Flair Detector
https://github.com/macabdul9/reddit-flait-detector
Last synced: over 1 year ago
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Reddit Flair Detector
- Host: GitHub
- URL: https://github.com/macabdul9/reddit-flait-detector
- Owner: macabdul9
- License: mit
- Created: 2020-04-06T08:32:08.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T04:04:40.000Z (over 3 years ago)
- Last Synced: 2025-02-12T00:45:40.436Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 83.1 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
### Client App: https://midas-rfd.web.app/
### API: https://midas-rfd-api.herokuapp.com/
API Endpoints
- /
- /automated_testing/
### Note: GitHub might not be able to render these notebooks due to their size, see them on nbviewer:
- exploratory-data-analysis.ipynb: https://nbviewer.jupyter.org/github/macabdul9/midas-internship-task/blob/master/src/exploratory-data-analysis.ipynb
- preprocessing-and-classification.ipynb: https://nbviewer.jupyter.org/github/macabdul9/midas-internship-task/blob/master/src/preprocessing-and-classification.ipynb
-
## 0. Directory Structure
- **client/** react app code base to call the flask api where our model is deployed
- **server/** flask rest API code base to inference from the trained model(s)
- **src/**
- **requirements.txt** dependency list required to reproduce the results in local/virtual environment
- **readme.md** That's what I am reading.
## 1. Results
A comprehensive report of trained models can be is given in the below table:
| Model | Accuracy(Test) | Dataset(train/test) |
| :--- | :----: | ---: |
| MultinomialNB | 59 | 48535/5393 |
| SV Classifier | 58 | 48535/5393 |
| RandomForest | 55 | 48535/5393 |
| GradientBoosting | 58 | 48535/5393 |
| Flair | 37 | 24110/3000 |
| BiLSTM (trainable embedding layer)| 62 | 90181/10021 |
| BiLSTM(pretrained embeddings) | 60 | 90181/10021 |
| CNN(pretrained embeddings) | 63 | 90181/10021 |
| Attention-based LSTM | 61 | 90181/10021 |
| DistilBERT | 65 | 90334/10038 |
| XLNet | 65 | 90334/10038 |
##### Download the trained models from here:
- **DistilBERT** https://drive.google.com/file/d/148iGfmDPL82MWhPQM15aPoJXlCUrX9N-/view?usp=sharing
- **XLNet** https://drive.google.com/drive/folders/173S1sfBXA-qZRJ1kVP4flW45n1Hk0Zas?usp=sharing
- **Custom Models**: https://drive.google.com/file/d/1Z9s4agLJ90MVAXB0zErVwW_J-QiSpoGZ/view?usp=sharing
## 2. Data Acquisition, Cleaning and Preprocessing:
i. I have used pushshift API and PRAW(=Reddit API wrapper) to scrap the data from India subreddit
ii. PRAW was used to collect the comments of a post/submission
iii. Total 218371 posts were collected from Jan-2019 upto 10 April, 2020.
iii. Following properties of a post was collected
- features = [
'allow_live_comments', 'author', 'author_premium', 'can_mod_post', 'contest_mode', 'created_utc',
'full_link', 'id', 'is_crosspostable', 'is_meta', 'is_original_content', 'is_self', 'is_video',
'link_flair_text', 'locked', 'media_only', 'no_follow', 'num_comments', 'num_crossposts', 'over_18',
'parent_whitelist_status', 'permalink', 'pinned', 'score', 'selftext', 'send_replies', 'spoiler',
'stickied', 'title', 'total_awards_received', 'url'
]
- custom_features = [
'comments', 'mean_comment_score'
]
iv. Collected data (raw) can be downloaded from here:
- https://drive.google.com/file/d/1uCOeV-mBSC516Pv0PNZ2g6h27mJB2uLj/view?usp=sharing
iv. Undersampling, Imputation, Noise Removal, Spelling Correction, Stopword Removal, Tokenization, TF-IDF Vectorization techniques were used to clean and process the data.
iv. Cleaned and Preprocessed dataset can be download from here:
- https://drive.google.com/file/d/1p4x_-JMB5Wr7LbXmUqRVT_p3hL91rVsB/view?usp=sharing
## 3. To reproduce the results in local/virtual environments
i. Clone this repo using:
- **git clone https://github.com/macabdul9/midas-internship-task**
ii. Create a virtual environment(recommended but not necessary)
- Create: **virtualenv venv**
- Activate:
- **venv/Scripts/activate** *(windows)*
- **source venv/bin/activate** *(linux)*
iii. Install the dependencies:
- pip install -r requirements.txt(assuming you're on the root directory of this repo)
iv. Download the raw/cleaned/preprocessed dataset from [see section 2.4 and 2.6]
v. Now all the src code base can be executed
## 4. To run the client app on localhost
i. You should have node.js and npm (package manager) installed in your machine
ii. Change directory
- cd client/
iii. Install dependencies
- npm i
iv. Run app locally
- npm start
- client app will be running on localhost:3000 (default port)
## 5. To run the API on localhost
i. Change directory
- cd server/
ii. Create and activate a virtual environment (see 2.2)
iii. Install dependencies (see 2.3)
iv. Download the checkpoint from (see results) and set the path accordingly
iv. Run the flask app
- python app.py
- API will be running on localhost:5000(default port)
- Use postman(or other API client tools) to make requests
## 6. Screenshots of running app(and API):
- Post Response (URL)
- Post Response (txt file)
## 7. Python scripts to send POST requests to api
#### i. Sending an url to / endpoint
## Source: Postman
import requests
url = "http://localhost:5000/?url=https://www.reddit.com/r/india/comments/g8dx13/venomous_whisper_of_rumor_mongers_manifest/"
payload = {}
files = {}
headers = {
'url': 'csds'
}
response = requests.request("POST", url, headers=headers, data = payload, files = files)
print(response.text.encode('utf8'))
#### ii. Sending a text file to /automated_tesing/ endpoint
import requests
url = "http://localhost:5000/automated_testing/"
payload = {}
files = [
('file', open('/C:/Users/abdul/OneDrive/Desktop/sample.txt','rb'))
]
headers = {
'url': 'csds'
}
response = requests.request("POST", url, headers=headers, data = payload, files = files)
print(response.text.encode('utf8'))
## 8. Reference
[0] Pushshift API : https://github.com/pushshift/api
[1] PRAW: https://praw.readthedocs.io/en/latest/
[2] SimpleTransformers: https://github.com/ThilinaRajapakse/simpletransformers
[3] HugginFace transformers: https://github.com/huggingface/transformers
[4] Flair: https://towardsdatascience.com/text-classification-with-state-of-the-art-nlp-library-flair-b541d7add21f