<jupyter_start><jupyter_text>Model Notebook<jupyter_code>lerning_rate = .5
dataset = get_default_dataset()
# train
model = Model()
model.fit(dataset.x, dataset.y)
score = model.mean_square_error()<jupyter_output><empty_output><jupyter_text>Optimizer Notebook<jupyter_code>model = Notebook('model.ipynb')

score = []
for lerning_rate in [.001, .01, .1]:
    model.ns['lerning_rate'] = lerning_rate
    model.ns['dataset'] = new_dataset
    model.run_tag('train')
    score.append(model.ns['score'])<jupyter_output><empty_output><jupyter_text>Looper Notebook<jupyter_code>parser = Notebook('parser.ipynb')

data = []
for filename in os.listdir():
    parser.ns['filename'] = filename
    parser.run_all(blacklist=['init_filename'])
    data.append(parser.ns['result'])
    # just in case:
    parser.restart()<jupyter_output><empty_output><jupyter_text>Parser Notebook<jupyter_code># init_filename
filename = 'file0.json'<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup