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GitHub / arnavc1712 / Cross-Domain-Fake-News-Detection
Built a domain independent Multi Task and End to End Deep Learning model which can be trained on one news domain to classify fake news, and be used to classify fake news on another weakly labelled domain. The aim was to learn a model which learns domain independent representations from articles and classify them as fake news or not.
Stars: 0
Forks: 0
Open Issues: 10
License: None
Language: Jupyter Notebook
Repo Size: 9.33 MB
Dependencies:
58
Created: over 4 years ago
Updated: 9 months ago
Last pushed: over 1 year ago
Last synced: 9 months ago
Files
Dependencies
- Keras ==2.3.1
- Keras-Applications ==1.0.8
- Keras-Preprocessing ==1.1.0
- Markdown ==3.1.1
- PyYAML ==5.1.2
- Pygments ==2.4.2
- Werkzeug ==0.16.0
- absl-py ==0.8.1
- appnope ==0.1.0
- astor ==0.8.0
- backcall ==0.1.0
- cachetools ==3.1.1
- certifi ==2019.9.11
- chardet ==3.0.4
- decorator ==4.4.1
- gast ==0.2.2
- google-auth ==1.6.3
- google-auth-oauthlib ==0.4.1
- google-pasta ==0.1.8
- grpcio ==1.24.3
- h5py ==2.10.0
- idna ==2.8
- ipykernel ==5.1.3
- ipython ==7.9.0
- ipython-genutils ==0.2.0
- jedi ==0.15.1
- jupyter-client ==5.3.4
- jupyter-core ==4.6.1
- numpy ==1.17.3
- oauthlib ==3.1.0
- opt-einsum ==3.1.0
- pandas ==0.25.3
- parso ==0.5.1
- pexpect ==4.7.0
- pickleshare ==0.7.5
- prompt-toolkit ==2.0.10
- protobuf ==3.10.0
- ptyprocess ==0.6.0
- pyasn1 ==0.4.7
- pyasn1-modules ==0.2.7
- python-dateutil ==2.8.1
- pytz ==2019.3
- pyzmq ==18.1.0
- requests ==2.22.0
- requests-oauthlib ==1.2.0
- rsa ==4.0
- scipy ==1.3.1
- six ==1.12.0
- tensorboard ==2.0.1
- tensorflow ==2.0.0
- tensorflow-estimator ==2.0.1
- termcolor ==1.1.0
- tokenizer ==1.4.1
- tornado ==6.0.3
- traitlets ==4.3.3
- urllib3 ==1.25.6
- wcwidth ==0.1.7
- wrapt ==1.11.2