<jupyter_start><jupyter_text>* html -> plain text: html2text.html2text(htmltext)<jupyter_code>for name in p:
    with open(f'./output/{name}', 'r') as in_file:
        data = json.load(in_file)
    for idx in fail_record:
        del data[idx]
    if len(data.keys()) != 145:
        print(f'{name}: error')
    with open(f'./newoutput/{name}', 'w') as out_file:
        json.dump(data, out_file, indent=4, ensure_ascii=False)
with open(f'./ground-truth.json') as in_file:
    data = json.load(in_file)
for idx in fail_record:
    del data[idx]
if len(data.keys()) != 145:
    print(f'{name}: error')
with open(f'./new-ground-truth.json', 'w') as out_file:
    json.dump(data, out_file, indent=4, ensure_ascii=False)
with open('./new-ground-truth.json', 'r') as f:
    data = json.load(f)
for key, value in data.items():
    tmp = get_content(value["url"])
    data[key]['articleBody'] = html2text.html2text(tmp['parsed_content']['content'])
with open(f'./newoutput/glia.json', 'w') as out_file:
    json.dump(data, out_file, indent=4, ensure_ascii=False)
url = "http://www.panarmenian.net/eng/news/275221/"
api = # gliacloud api
json_file = requests.get(api + url).json
print(json_file)<jupyter_output><bound method Response.json of <Response [200]>>
{'fraction_non_alphanumeric': 0.17585931254996004, 'fraction_numerical': 0.017585931254996003, 'mean_word_length': 4.216666666666667, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '60301', 'license': 'MIT', 'n_tokens_mistral': 482, 'n_tokens_neox': 464, 'n_words': 100, 'path': 'Generate.ipynb', 'repo_name': 'livingbio/article-extraction-benchmark', 'size': '1251'}