<jupyter_start><jupyter_text>English Web Treebanks<jupyter_code>xtest, ytest = data_to_mat('./data/WebTreeBank/weblog_penntrees.test.conll', vocab,
                           reader.word_to_id, is_not_twitter=True, start_tag=0, end_tag=15, pad_tag=16)
print('Webblog OOD Detection\n')
show_ood_detection_results(err, X_test, xtest, mask_for_data(ytest))<jupyter_output>Webblog OOD Detection

OOD Example Prediction Probability (mean, std):
0.933841 0.15627

Normality Detection
Normality base rate (%): 86.01
KL[p||u]: Normality Detection
AUPR (%): 87.88
AUROC (%): 59.84
Prediction Prob: Normality Detection
AUPR (%): 87.84
AUROC (%): 59.7
Normality base rate (%): 85.55
KL[p||u]: Normality Detection (relative to correct examples)
AUPR (%): 87.94
AUROC (%): 61.6
Prediction Prob: Normality Detection (relative to correct examples)
AUPR (%): 87.91
AUROC (%): 61.48


Abnormality Detection
Abnormality base rate (%): 13.99
KL[p||u]: Abnormality Detection
AUPR (%): 24.98
AUROC (%): 59.84
Prediction Prob: Normality Detection
AUPR (%): 23.91
AUROC (%): 59.7
Abnormality base rate (%): 14.45
KL[p||u]: Normality Detection (relative to correct examples)
AUPR (%): 30.57
AUROC (%): 61.6
Prediction Prob: Normality Detection (relative to correct examples)
AUPR (%): 29.52
AUROC (%): 61.48
{'fraction_non_alphanumeric': 0.1410756040530008, 'fraction_numerical': 0.07326578332034295, 'mean_word_length': 5.233009708737864, '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': '663449', 'license': 'MIT', 'n_tokens_mistral': 557, 'n_tokens_neox': 459, 'n_words': 124, 'path': 'NLP/POS/WSJ.ipynb', 'repo_name': 'hendrycks/error-detection', 'size': '1283'}