<jupyter_start><jupyter_text><jupyter_code>try:
    %cd /content
    !git clone https://github.com/mveer1/Fake-News-Detection.git 
except:
    !rm -r /content/Fake-News-Detection
    !git clone https://github.com/mveer1/Fake-News-Detection.git 

# !git clone https://github.com/mveer1/Fake-News-Detection.git
# !rm -r /content/Fake-News-Detection
%cd /content/Fake-News-Detection/
import os
os.chdir("/content/Fake-News-Detection/")
!pwd<jupyter_output>/content/Fake-News-Detection<jupyter_text>future==0.17.1h5pyKerasKeras-ApplicationsKeras-Preprocessingmatplotlibnvidia-ml-pypandaspython-aptpython-dateutil==2.8.0python-debianpython-slugify==3.0.3python-systemdpytorch-pretrained-bertpytorch-transformers==1.0.0scikit-image==0.14.2scikit-learn==0.20.2scipyseaborn==0.9.0sixsympy==1.4tqdm<jupyter_code>!pip install -r requirements.txt
from google.colab import drive
drive.mount('/content/drive')
!cp '/content/drive/MyDrive/NLP/triBERT.pth' '/content/Fake-News-Detection/'
!python3 FakeNewsDetection.py -num_labels 6 -num_epochs 3 -load_model False<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15013901760889714, 'fraction_numerical': 0.030583873957367932, 'mean_word_length': 5.545454545454546, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '631985', 'license': 'MIT', 'n_tokens_mistral': 437, 'n_tokens_neox': 430, 'n_words': 44, 'path': 'NLP.ipynb', 'repo_name': 'mveer1/Fake-News-Detection', 'size': '1079'}