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| # Editors | ||
| .vscode/ | ||
| .idea/ | ||
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| # Vagrant | ||
| .vagrant/ | ||
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| # Mac/OSX | ||
| .DS_Store | ||
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| # Windows | ||
| Thumbs.db | ||
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| # Source for the following rules: https://raw.githubusercontent.com/github/gitignore/master/Python.gitignore | ||
| # Byte-compiled / optimized / DLL files | ||
| __pycache__/ | ||
| *.py[cod] | ||
| *$py.class | ||
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| # C extensions | ||
| *.so | ||
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| # Distribution / packaging | ||
| .Python | ||
| build/ | ||
| develop-eggs/ | ||
| dist/ | ||
| downloads/ | ||
| eggs/ | ||
| .eggs/ | ||
| lib/ | ||
| lib64/ | ||
| parts/ | ||
| sdist/ | ||
| var/ | ||
| wheels/ | ||
| *.egg-info/ | ||
| .installed.cfg | ||
| *.egg | ||
| MANIFEST | ||
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| # PyInstaller | ||
| # Usually these files are written by a python script from a template | ||
| # before PyInstaller builds the exe, so as to inject date/other infos into it. | ||
| *.manifest | ||
| *.spec | ||
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| # Installer logs | ||
| pip-log.txt | ||
| pip-delete-this-directory.txt | ||
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| # Unit test / coverage reports | ||
| htmlcov/ | ||
| .tox/ | ||
| .nox/ | ||
| .coverage | ||
| .coverage.* | ||
| .cache | ||
| nosetests.xml | ||
| coverage.xml | ||
| *.cover | ||
| .hypothesis/ | ||
| .pytest_cache/ | ||
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| # Translations | ||
| *.mo | ||
| *.pot | ||
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| # Django stuff: | ||
| *.log | ||
| local_settings.py | ||
| db.sqlite3 | ||
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| # Flask stuff: | ||
| instance/ | ||
| .webassets-cache | ||
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| # Scrapy stuff: | ||
| .scrapy | ||
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| # Sphinx documentation | ||
| docs/_build/ | ||
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| # PyBuilder | ||
| target/ | ||
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| # Jupyter Notebook | ||
| .ipynb_checkpoints | ||
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| # IPython | ||
| profile_default/ | ||
| ipython_config.py | ||
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| # pyenv | ||
| .python-version | ||
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| # celery beat schedule file | ||
| celerybeat-schedule | ||
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| # SageMath parsed files | ||
| *.sage.py | ||
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| # Environments | ||
| .env | ||
| .venv | ||
| env/ | ||
| venv/ | ||
| ENV/ | ||
| env.bak/ | ||
| venv.bak/ | ||
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| # Spyder project settings | ||
| .spyderproject | ||
| .spyproject | ||
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| # Rope project settings | ||
| .ropeproject | ||
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| # mkdocs documentation | ||
| /site | ||
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| # mypy | ||
| .mypy_cache/ | ||
| .dmypy.json | ||
| dmypy.json | ||
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| # .ipynb checkpoints | ||
| .ipynb_checkpoints | ||
| */.ipynb_checkpoints/* | ||
| **/*.ipynb_checkpoints/ | ||
| */.ipynb_checkpoints/*.ipynb |
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| Input Files: | ||
| dev_article : | ||
| - Text_ID : unique article ids | ||
| - Text: Article Text Data | ||
| - Headline: Headline to the article | ||
| - Mobile_Tech_Flag: Flag shows whether article is related to mobile_tech | ||
| dev_tweet : | ||
| - Text_ID : unique tweet ids | ||
| - Text: Tweet Text Data | ||
| - Mobile_Tech_Flag: Flag shows whether tweet is related to mobile_tech | ||
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| Output: | ||
| sample_output_1: | ||
| - Text_ID : unique article/tweet ids | ||
| - Mobile_Tech_Flag_Actual: Have actual mobile_tech values | ||
| - Mobile_Tech_Flag_Predicted: Have predicted mobile_tech values | ||
| - Headline_Actual_Eng: Headline to the article in English language | ||
| - Headline_Generated_Eng_Lang: Generated headline to the article in English language | ||
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| sample_output_2: | ||
| - Text_ID : unique tweet ids | ||
| - Mobile_Tech_Flag_Actual: Have actual mobile_tech values | ||
| - Mobile_Tech_Flag_Predicted: Have predicted mobile_tech values | ||
| - Brands_Entity_Actual: Actual Brands available in data | ||
| - Sentiment_Actual: Actual Sentiment available in data | ||
| - Brands_Entity_Identified: Predicted Brands available in data | ||
| - Sentiment_Identified: Predicted Sentiment available in data |
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| Original file line number | Diff line number | Diff line change |
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| Input Files: | ||
| dev_article : | ||
| - Text_ID : unique article ids | ||
| - Text: Article Text Data | ||
| - Headline: Headline to the article | ||
| - Mobile_Tech_Flag: Flag shows whether article is related to mobile_tech | ||
| dev_tweet : | ||
| - Text_ID : unique tweet ids | ||
| - Text: Tweet Text Data | ||
| - Mobile_Tech_Flag: Flag shows whether tweet is related to mobile_tech | ||
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| Output: | ||
| sample_output_1: | ||
| - Text_ID : unique article/tweet ids | ||
| - Mobile_Tech_Flag_Actual: Have actual mobile_tech values | ||
| - Mobile_Tech_Flag_Predicted: Have predicted mobile_tech values | ||
| - Headline_Actual_Eng: Headline to the article in English language | ||
| - Headline_Generated_Eng_Lang: Generated headline to the article in English language | ||
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| sample_output_2: | ||
| - Text_ID : unique tweet ids | ||
| - Mobile_Tech_Flag_Actual: Have actual mobile_tech values | ||
| - Mobile_Tech_Flag_Predicted: Have predicted mobile_tech values | ||
| - Brands_Entity_Actual: Actual Brands available in data | ||
| - Sentiment_Actual: Actual Sentiment available in data | ||
| - Brands_Entity_Identified: Predicted Brands available in data | ||
| - Sentiment_Identified: Predicted Sentiment available in data |
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| Brand | ||
| Condor | ||
| Walton | ||
| Gradiente | ||
| Multilaser | ||
| Positivo | ||
| BlackBerry Limited | ||
| DataWind | ||
| 10.Or | ||
| Amoi | ||
| BBK | ||
| Coolpad | ||
| Cubot | ||
| Gfive | ||
| Haier | ||
| Hisense | ||
| Honor | ||
| Huawei | ||
| Konka | ||
| LeEco | ||
| Meitu | ||
| Meizu | ||
| Ningbo Bird | ||
| OnePlus | ||
| Oppo | ||
| Realme | ||
| iQOO | ||
| Smartisan | ||
| TCL Corporation | ||
| Technology Happy Life | ||
| Tecno Mobile | ||
| Vivo | ||
| Vsun | ||
| Wasam | ||
| Xiaomi | ||
| Zopo Mobile | ||
| ZTE | ||
| Subsidiary of Lenovo | ||
| Jablotron | ||
| Verzo | ||
| SICO Technology | ||
| Jolla | ||
| Nokia Corporation | ||
| HMD Global | ||
| Bittium | ||
| Archos | ||
| Groupe Bull | ||
| MobiWire | ||
| Wiko | ||
| Gigaset | ||
| Medion | ||
| TechniSat | ||
| Tiptel | ||
| MLS | ||
| X-tigi Mobile | ||
| Lenovo | ||
| CREO | ||
| Celkon | ||
| Iball | ||
| Intex Technologies | ||
| Karbonn Mobiles | ||
| Lava International | ||
| HCL Technologies | ||
| Jio | ||
| LYF | ||
| Micromax Informatics | ||
| Onida Electronics | ||
| Spice Digital | ||
| Videocon | ||
| Xolo | ||
| YU Televentures | ||
| mPhone | ||
| Nexian | ||
| MITO | ||
| Polytron | ||
| Advan | ||
| Axioo | ||
| IMO | ||
| Zyrex | ||
| Andromax | ||
| Evercoss | ||
| Luna | ||
| Genpro | ||
| Asiafone | ||
| Himax | ||
| SPC | ||
| Vitell | ||
| Venera | ||
| Osmo | ||
| HiCore | ||
| Maxtron | ||
| Brondi | ||
| New Generation Mobile | ||
| Olivetti | ||
| Onda Mobile Communication | ||
| Akai | ||
| Fujitsu | ||
| Casio | ||
| Hitachi | ||
| JRC | ||
| Kyocera | ||
| Mitsubishi Electric | ||
| NEC | ||
| Panasonic | ||
| Sansui | ||
| Sharp | ||
| Sony | ||
| Toshiba | ||
| Just5 | ||
| M Dot | ||
| Ninetology | ||
| Kyoto Electronics | ||
| Lanix | ||
| Zonda | ||
| Fairphone | ||
| John's Phone | ||
| Philips | ||
| Koryolink | ||
| QMobile | ||
| Voice Mobile | ||
| Cherry Mobile | ||
| Starmobile | ||
| Cloudfone | ||
| MyPhone | ||
| Torque | ||
| Kruger&Matz | ||
| Manta Multimedia | ||
| myPhone | ||
| Allview | ||
| Evolio | ||
| E-Boda | ||
| Myria | ||
| Utok | ||
| Beeline | ||
| Explay | ||
| Gresso | ||
| Highscreen | ||
| Megafon | ||
| MTS | ||
| RoverPC | ||
| teXet | ||
| Sitronics | ||
| Yotaphone | ||
| KT Tech | ||
| LG | ||
| Pantech | ||
| Samsung | ||
| BQ | ||
| Doro | ||
| Acer | ||
| Asus | ||
| BenQ | ||
| DBTel | ||
| Dopod | ||
| Foxconn | ||
| Gigabyte Technology | ||
| HTC | ||
| AIS | ||
| DTAC | ||
| Ericsson | ||
| Wellcom | ||
| I-Mobile | ||
| EvertekTunisie | ||
| ASELSAN | ||
| Vestel | ||
| Thuraya | ||
| Bullitt Group | ||
| Wileyfox | ||
| Apple | ||
| BLU Products | ||
| Caterpillar | ||
| Firefly | ||
| Garmin | ||
| HP | ||
| InFocus | ||
| InfoSonics | ||
| Motorola Mobility | ||
| Obi | ||
| Nextbit | ||
| "Purism," | ||
| VinSmart | ||
| GTel |
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| 1. Please refer to the Data Dictionary folder for detailed understanding of the features in all the datasets shared | ||
| 2. Development Data folder has both the datasets for Articles and Tweets. Please use this data to build your solution | ||
| 3. Sample Output folder has the structure of how the submission file should be like | ||
| 4. Sample Output 1 is for the Mobile_Tech Classification & Text Summarisation task while Output Sample 2 is for the Entity Based Sentiment Analsysis | ||
| 5. Non-Adherence to the structure of the sample outputs shared would lead to disqualification | ||
| 6. You can use the Headline Similarity Scores.ipnb notebook to calculate BERT, ROUGH and BLUE scores. This code will be used to evaluate the performance of the headline genertor. |
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