<jupyter_start><jupyter_text>Series of Experiments using differnt "hyperparameters" for BERT pooling model<jupyter_code># 1. Same hypers, different dev datasets
scores = {}

dev_eval, dev_rho = evaluate_pooled_bert(dev, -1, pooling_function)
scores['dev'] = dev_rho

highest, highest_rho = evaluate_pooled_bert(highest, -1, pooling_function)
scores['highest'] = highest_rho

lowest, lowest_rho = evaluate_pooled_bert(lowest, -1, pooling_function)
scores['lowest'] = lowest_rho

mean, mean_rho = evaluate_pooled_bert(average, -1, pooling_function)
scores['mean'] = mean_rho
scores
#2 same dev set (highest) different pooling functions
pooling_scores = {}

min_eval, min_rho = evaluate_pooled_bert(highest, -1, vsm.min_pooling)
pooling_scores['min'] = min_rho

max_eval, max_rho = evaluate_pooled_bert(highest, -1, vsm.max_pooling)
pooling_scores['max'] = max_rho

mean_eval, mean_rho = evaluate_pooled_bert(highest, -1, vsm.mean_pooling)
pooling_scores['mean'] = mean_rho

last_eval, last_rho = evaluate_pooled_bert(average, -1, vsm.last_pooling)
pooling_scores['last'] = last_rho
pooling_scores<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1103202846975089, 'fraction_numerical': 0.008896797153024912, 'mean_word_length': 8.0, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '8666', 'license': 'Apache-2.0', 'n_tokens_mistral': 432, 'n_tokens_neox': 394, 'n_words': 84, 'path': 'wordrelatedness_model_testing.ipynb', 'repo_name': 'americanthinker/cs224u', 'size': '1124'}