<jupyter_start><jupyter_text>```sh bin/scen_bt_tuning_3.shrm results/res_bt_tuning.txt rm -r conf/embeddings_validation.work/python -m embeddings_validation \    --conf conf/embeddings_validation_short.hocon --workers 10 --total_cpu_count 20 \    --conf_extra \      'report_file: "../results/res_bt_tuning.txt",      auto_features: ["../data/emb__bt_tuning_*.pickle", "../data/barlow_twins_embeddings.pickle"]'less -S results/res_bt_tuning.txt```<jupyter_code>import pandas as pd
df = pd.read_fwf('results/res_bt_tuning.txt').iloc[10:95, :2]
df.columns = ['name', 'm']
df
df.iloc[0, 0]
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={0: 'k'}).set_index('k')['m'].reset_index() \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5), logx=True)
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={1: 'k'}).set_index('k')['m'].reset_index() \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5), logx=True)
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={2: 'k'}).set_index('k')['m'].reset_index() \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5))
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={3: 'k'}).set_index('k')['m'].reset_index() \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5), logx=True)
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={4: 'k'}).set_index('k')['m'].reset_index() \
    .assign(k=lambda x: x['k'].str.replace('^', '-'), regex=False) \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5), logx=True)
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={5: 'k'}).set_index('k')['m'].reset_index() \
    .assign(k=lambda x: x['k'].str.replace('^', '-'), regex=False) \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5))
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={6: 'k'}).set_index('k')['m'].reset_index() \
    .assign(k=lambda x: x['k'].str.replace('^', '-'), regex=False) \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5))
pd.concat([
    df.name.str.replace('e-', 'e^').str.split(r'-', expand=True),
    df.m,
], axis=1).rename(columns={7: 'k'}).set_index('k')['m'].reset_index() \
    .assign(k=lambda x: x['k'].str.replace('^', '-'), regex=False) \
    .assign(n=lambda x: pd.to_numeric(x['k'].str.split('_').str[-1])) \
    .set_index('n').sort_index()['m'].astype(float).plot(figsize=(12, 5))
!cat bin/scen_bt_tuning_1.sh<jupyter_output>export SC_SUFFIX="bt_tuning_lambd_0.006062-hidden_size_2016-prj_size_121-batch_size_32-lr_0.000572-weight_decay_0.001115-step_size_20-step_gamma_0.42003"
    python ../../pl_train_module.py \
        logger_name=${SC_SUFFIX} \
        params.train.lambd=0.006062 \
        params.rnn.hidden_size="2016" \
         "params.head_layers=[[Linear, {in_features: 2016, out_features: 121, bias: false}], [BatchNorm1d, {num_features: 121}], [ReLU, { }], [Linear, {in_features: 121, out_features: 121, bias: false}], [BatchNorm1d, {num_features: 121, affine: False}]]"  \
        data_module.train.batch_size=32 \
        params.train.lr=0.000572 \
        params.train.weight_decay=0.001115 \
        params.lr_scheduler.step_size=20 \
        params.lr_scheduler.step_gamma=0.42003 \
        model_path="models/gender_mlm__$SC_SUFFIX.p" \
        --conf "conf/barlow_twins_params.hocon"
    python ../../pl_inference.py         inference_dataloader.loader.batch_size=500 \
        model[...]
{'fraction_non_alphanumeric': 0.25534333788085495, 'fraction_numerical': 0.035925420645748066, 'mean_word_length': 4.121071012805588, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '513738', 'license': 'Apache-2.0', 'n_tokens_mistral': 1901, 'n_tokens_neox': 1792, 'n_words': 193, 'path': 'experiments/scenario_age_pred/notebooks/make_tuning_script.ipynb', 'repo_name': 'zergey/pytorch-lifestream', 'size': '4398'}