TUNIZI is the first 100% Tunisian Arabizi sentiment analysis dataset, developed as part of AI4D’s ongoing NLP project for African languages. Tunisian Arabizi is the representation of the Tunisian dialect written in Latin characters and numbers rather than Arabic letters.
The objective of this challenge is to build a sentiment analysis classifier for the Tunisian Arabizi Dialect.
For more information about this challenge, have a look on Zindi.
|---- nlp (package)
| |--- . . .
| |--- {module}.py
| |--- . . .
|
|---- data (placeholder for raw and preprocessed data)
| |--- Train.csv
| |--- Test.csv
| |--- SampleSubmission.csv
| |--- . . .
|
|---- notebooks
| |--- AI4D_Processing.ipynb
| |--- AI4D_rzA27Luehf.ipynb
| |--- AI4D_AH7LwUXCvT.ipynb
| |--- AI4D_10WwJdQcXs.ipynb
|
|---- submissions (auto-generated)
| |--- *.csv
| |--- *.csv
|
|---- setup.py
|
|---- Readme.md
PS: This isn't the definitive structure. During the code execution, new directories will be created.
# 1. Make sure to follow the repo structure
# 2. Run 'pip install ./'
# 3. Run 'notebooks/AI4D_Processing.ipynb'
# 4. Run 'notebooks/AI4D_rzA27Luehf.ipynb', 'notebooks/AI4D_AH7LwUXCvT.ipynb', 'notebooks/AI4D_10WwJdQcXs.ipynb'
# 5. Run 'python blend.py'
To make sure that everything is working smoothly, here is what to expect from above (steps):
# 1.
# 2. This step installs the nlp package
# 3. After this step, verify that 'data/{TrainNormalized.csv, TestNormalized}.csv' exist
# 4. Directory 'submissions/' will be added to the repo structure and contain '{multi-dialect-bert-base-arabic*, bert-multilingual-cased*, roberta-base*}.csv'.
# 5. Performs a simple weight-blend, then creates 'submissions/final_submission.csv' which is the final submission file.
Look for : Muhamed_Tuo
Rank : 9th/312
Accuracy Score: 0.8362(Private) - 0.8394(Public)
| Name | Zindi ID | Github ID |
|---|---|---|
| Muhamed TUO | @Muhamed_Tuo | @NazarioR9 |