{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandas in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (2.2.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: numpy<2,>=1.22.4 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (1.26.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (2024.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas) (1.15.0)\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 24.0 is available.\n",
      "You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the filelist\n",
    "\n",
    "For the full results.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2560 results.json files\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "# Specify the path to the folder containing the results.json files\n",
    "folder_path = \"lm-eval-output\"\n",
    "\n",
    "# Use glob to find all the results.json files\n",
    "results_json_files = glob.glob(f\"{folder_path}/**/results.json\", recursive=True)\n",
    "\n",
    "# Show total number of results.json files found\n",
    "print(f\"Found {len(results_json_files)} results.json files\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process all the results.json\n",
    "\n",
    "One file at a time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed example:  {'name': 'mistralai/Mistral-7B-v0.1', 'config': {'dtype=bfloat16,trust_remote_code=True': {'confStr': 'dtype=bfloat16,trust_remote_code=True', 'confObj': {'dtype': 'bfloat16', 'trust_remote_code': 'True'}, 'results': {'cola': {'mcc,none': -0.05103445794224817, 'mcc_stderr,none': 0.03076801823137936, 'alias': 'cola'}, 'anli': {'acc,none': 0.3803125, 'acc_stderr,none': 0.015133650384246593, 'alias': 'anli'}, 'anli_r1': {'acc,none': 0.377, 'acc_stderr,none': 0.01533317012577988, 'alias': ' - anli_r1'}, 'anli_r2': {'acc,none': 0.376, 'acc_stderr,none': 0.015325105508898125, 'alias': ' - anli_r2'}, 'anli_r3': {'acc,none': 0.38666666666666666, 'acc_stderr,none': 0.014063941778353468, 'alias': ' - anli_r3'}, 'wsc': {'acc,none': 0.40384615384615385, 'acc_stderr,none': 0.04834688952654018, 'alias': ' - wsc'}, 'lambada_cloze': {'perplexity,none': 92.9621493847383, 'perplexity_stderr,none': 4.749192287461508, 'acc,none': 0.08354356685425965, 'acc_stderr,none': 0.017359537873426072, 'alias': 'lambada_cloze'}, 'lambada_openai_cloze_yaml': {'perplexity,none': 84.91463890370277, 'perplexity_stderr,none': 2.1929498791439825, 'acc,none': 0.04967979817581991, 'acc_stderr,none': 0.0030271710751734893, 'alias': ' - lambada_openai_cloze_yaml'}, 'lambada_standard_cloze_yaml': {'perplexity,none': 101.0096598657738, 'perplexity_stderr,none': 2.813806288232997, 'acc,none': 0.1174073355326994, 'acc_stderr,none': 0.004484766596365691, 'alias': ' - lambada_standard_cloze_yaml'}, 'glue': {'acc,none': 0.5149773701762745, 'acc_stderr,none': 0.0011586493887209115, 'f1,none': 0.3490504972495486, 'f1_stderr,none': 0.0013384442216884647, 'mcc,none': -0.04847021005996873, 'mcc_stderr,none': 0.030783455837743674, 'alias': 'glue'}, 'mnli': {'acc,none': 0.4542027508914926, 'acc_stderr,none': 0.005025942602094432, 'alias': 'mnli'}, 'mnli_mismatch': {'acc,none': 0.4631814483319772, 'acc_stderr,none': 0.005029102510704409, 'alias': 'mnli_mismatch'}, 'mrpc': {'acc,none': 0.6568627450980392, 'acc_stderr,none': 0.023532824020694156, 'f1,none': 0.7426470588235294, 'f1_stderr,none': 0.02115915180153455, 'alias': 'mrpc'}, 'qnli': {'acc,none': 0.49789492952590153, 'acc_stderr,none': 0.00676535059208955, 'alias': 'qnli'}, 'qqp': {'acc,none': 0.5387336136532278, 'acc_stderr,none': 0.0024792278452134536, 'f1,none': 0.3456720816813445, 'f1_stderr,none': 0.0036333557486569264, 'alias': 'qqp'}, 'rte': {'acc,none': 0.6750902527075813, 'acc_stderr,none': 0.028190822551170353, 'alias': 'rte'}, 'sst2': {'acc,none': 0.6708715596330275, 'acc_stderr,none': 0.01592184233279754, 'alias': 'sst2'}, 'wnli': {'acc,none': 0.5774647887323944, 'acc_stderr,none': 0.05903984205682581, 'alias': 'wnli'}, 'blimp': {'acc,none': 0.8289253731343283, 'acc_stderr,none': 0.1651542013152315, 'alias': ' - blimp'}, 'blimp_adjunct_island': {'acc,none': 0.901, 'acc_stderr,none': 0.009449248027662753, 'alias': '  - blimp_adjunct_island'}, 'blimp_anaphor_gender_agreement': {'acc,none': 0.99, 'acc_stderr,none': 0.003148000938676753, 'alias': '  - blimp_anaphor_gender_agreement'}, 'blimp_anaphor_number_agreement': {'acc,none': 0.996, 'acc_stderr,none': 0.0019969947390987295, 'alias': '  - blimp_anaphor_number_agreement'}, 'blimp_animate_subject_passive': {'acc,none': 0.813, 'acc_stderr,none': 0.012336254828074118, 'alias': '  - blimp_animate_subject_passive'}, 'blimp_animate_subject_trans': {'acc,none': 0.898, 'acc_stderr,none': 0.009575368801653886, 'alias': '  - blimp_animate_subject_trans'}, 'blimp_causative': {'acc,none': 0.758, 'acc_stderr,none': 0.013550631705555965, 'alias': '  - blimp_causative'}, 'blimp_complex_NP_island': {'acc,none': 0.573, 'acc_stderr,none': 0.01564978964446222, 'alias': '  - blimp_complex_NP_island'}, 'blimp_coordinate_structure_constraint_complex_left_branch': {'acc,none': 0.794, 'acc_stderr,none': 0.012795613612786548, 'alias': '  - blimp_coordinate_structure_constraint_complex_left_branch'}, 'blimp_coordinate_structure_constraint_object_extraction': {'acc,none': 0.878, 'acc_stderr,none': 0.010354864712936701, 'alias': '  - blimp_coordinate_structure_constraint_object_extraction'}, 'blimp_determiner_noun_agreement_1': {'acc,none': 0.997, 'acc_stderr,none': 0.0017303161543469276, 'alias': '  - blimp_determiner_noun_agreement_1'}, 'blimp_determiner_noun_agreement_2': {'acc,none': 0.99, 'acc_stderr,none': 0.00314800093867677, 'alias': '  - blimp_determiner_noun_agreement_2'}, 'blimp_determiner_noun_agreement_irregular_1': {'acc,none': 0.964, 'acc_stderr,none': 0.005893957816165549, 'alias': '  - blimp_determiner_noun_agreement_irregular_1'}, 'blimp_determiner_noun_agreement_irregular_2': {'acc,none': 0.956, 'acc_stderr,none': 0.00648892179842742, 'alias': '  - blimp_determiner_noun_agreement_irregular_2'}, 'blimp_determiner_noun_agreement_with_adj_2': {'acc,none': 0.962, 'acc_stderr,none': 0.006049181150584937, 'alias': '  - blimp_determiner_noun_agreement_with_adj_2'}, 'blimp_determiner_noun_agreement_with_adj_irregular_1': {'acc,none': 0.935, 'acc_stderr,none': 0.007799733061832013, 'alias': '  - blimp_determiner_noun_agreement_with_adj_irregular_1'}, 'blimp_determiner_noun_agreement_with_adj_irregular_2': {'acc,none': 0.939, 'acc_stderr,none': 0.007572076091557415, 'alias': '  - blimp_determiner_noun_agreement_with_adj_irregular_2'}, 'blimp_determiner_noun_agreement_with_adjective_1': {'acc,none': 0.982, 'acc_stderr,none': 0.004206387249611458, 'alias': '  - blimp_determiner_noun_agreement_with_adjective_1'}, 'blimp_distractor_agreement_relational_noun': {'acc,none': 0.923, 'acc_stderr,none': 0.00843458014024064, 'alias': '  - blimp_distractor_agreement_relational_noun'}, 'blimp_distractor_agreement_relative_clause': {'acc,none': 0.79, 'acc_stderr,none': 0.012886662332274534, 'alias': '  - blimp_distractor_agreement_relative_clause'}, 'blimp_drop_argument': {'acc,none': 0.753, 'acc_stderr,none': 0.013644675781314121, 'alias': '  - blimp_drop_argument'}, 'blimp_ellipsis_n_bar_1': {'acc,none': 0.811, 'acc_stderr,none': 0.01238678458811771, 'alias': '  - blimp_ellipsis_n_bar_1'}, 'blimp_ellipsis_n_bar_2': {'acc,none': 0.945, 'acc_stderr,none': 0.007212976294639237, 'alias': '  - blimp_ellipsis_n_bar_2'}, 'blimp_existential_there_object_raising': {'acc,none': 0.87, 'acc_stderr,none': 0.010640169792499368, 'alias': '  - blimp_existential_there_object_raising'}, 'blimp_existential_there_quantifiers_1': {'acc,none': 0.987, 'acc_stderr,none': 0.0035838308894036337, 'alias': '  - blimp_existential_there_quantifiers_1'}, 'blimp_existential_there_quantifiers_2': {'acc,none': 0.178, 'acc_stderr,none': 0.012102167676183587, 'alias': '  - blimp_existential_there_quantifiers_2'}, 'blimp_existential_there_subject_raising': {'acc,none': 0.906, 'acc_stderr,none': 0.009233052000787733, 'alias': '  - blimp_existential_there_subject_raising'}, 'blimp_expletive_it_object_raising': {'acc,none': 0.815, 'acc_stderr,none': 0.012285191326386675, 'alias': '  - blimp_expletive_it_object_raising'}, 'blimp_inchoative': {'acc,none': 0.649, 'acc_stderr,none': 0.015100563798316405, 'alias': '  - blimp_inchoative'}, 'blimp_intransitive': {'acc,none': 0.794, 'acc_stderr,none': 0.012795613612786555, 'alias': '  - blimp_intransitive'}, 'blimp_irregular_past_participle_adjectives': {'acc,none': 0.99, 'acc_stderr,none': 0.0031480009386767676, 'alias': '  - blimp_irregular_past_participle_adjectives'}, 'blimp_irregular_past_participle_verbs': {'acc,none': 0.928, 'acc_stderr,none': 0.008178195576218681, 'alias': '  - blimp_irregular_past_participle_verbs'}, 'blimp_irregular_plural_subject_verb_agreement_1': {'acc,none': 0.923, 'acc_stderr,none': 0.00843458014024065, 'alias': '  - blimp_irregular_plural_subject_verb_agreement_1'}, 'blimp_irregular_plural_subject_verb_agreement_2': {'acc,none': 0.921, 'acc_stderr,none': 0.008534156773333452, 'alias': '  - blimp_irregular_plural_subject_verb_agreement_2'}, 'blimp_left_branch_island_echo_question': {'acc,none': 0.549, 'acc_stderr,none': 0.01574315237958554, 'alias': '  - blimp_left_branch_island_echo_question'}, 'blimp_left_branch_island_simple_question': {'acc,none': 0.93, 'acc_stderr,none': 0.008072494358323508, 'alias': '  - blimp_left_branch_island_simple_question'}, 'blimp_matrix_question_npi_licensor_present': {'acc,none': 0.546, 'acc_stderr,none': 0.015752210388771847, 'alias': '  - blimp_matrix_question_npi_licensor_present'}, 'blimp_npi_present_1': {'acc,none': 0.638, 'acc_stderr,none': 0.0152048409129195, 'alias': '  - blimp_npi_present_1'}, 'blimp_npi_present_2': {'acc,none': 0.629, 'acc_stderr,none': 0.015283736211823188, 'alias': '  - blimp_npi_present_2'}, 'blimp_only_npi_licensor_present': {'acc,none': 0.991, 'acc_stderr,none': 0.002987963843142655, 'alias': '  - blimp_only_npi_licensor_present'}, 'blimp_only_npi_scope': {'acc,none': 0.754, 'acc_stderr,none': 0.013626065817750641, 'alias': '  - blimp_only_npi_scope'}, 'blimp_passive_1': {'acc,none': 0.901, 'acc_stderr,none': 0.009449248027662742, 'alias': '  - blimp_passive_1'}, 'blimp_passive_2': {'acc,none': 0.904, 'acc_stderr,none': 0.00932045443478322, 'alias': '  - blimp_passive_2'}, 'blimp_principle_A_c_command': {'acc,none': 0.812, 'acc_stderr,none': 0.01236158601510377, 'alias': '  - blimp_principle_A_c_command'}, 'blimp_principle_A_case_1': {'acc,none': 1.0, 'acc_stderr,none': 0.0, 'alias': '  - blimp_principle_A_case_1'}, 'blimp_principle_A_case_2': {'acc,none': 0.94, 'acc_stderr,none': 0.007513751157474927, 'alias': '  - blimp_principle_A_case_2'}, 'blimp_principle_A_domain_1': {'acc,none': 0.999, 'acc_stderr,none': 0.0010000000000000132, 'alias': '  - blimp_principle_A_domain_1'}, 'blimp_principle_A_domain_2': {'acc,none': 0.869, 'acc_stderr,none': 0.01067487484483796, 'alias': '  - blimp_principle_A_domain_2'}, 'blimp_principle_A_domain_3': {'acc,none': 0.634, 'acc_stderr,none': 0.015240612726405756, 'alias': '  - blimp_principle_A_domain_3'}, 'blimp_principle_A_reconstruction': {'acc,none': 0.48, 'acc_stderr,none': 0.01580663942303517, 'alias': '  - blimp_principle_A_reconstruction'}, 'blimp_regular_plural_subject_verb_agreement_1': {'acc,none': 0.964, 'acc_stderr,none': 0.005893957816165545, 'alias': '  - blimp_regular_plural_subject_verb_agreement_1'}, 'blimp_regular_plural_subject_verb_agreement_2': {'acc,none': 0.879, 'acc_stderr,none': 0.010318210380946088, 'alias': '  - blimp_regular_plural_subject_verb_agreement_2'}, 'blimp_sentential_negation_npi_licensor_present': {'acc,none': 0.996, 'acc_stderr,none': 0.0019969947390987295, 'alias': '  - blimp_sentential_negation_npi_licensor_present'}, 'blimp_sentential_negation_npi_scope': {'acc,none': 0.751, 'acc_stderr,none': 0.01368160027870233, 'alias': '  - blimp_sentential_negation_npi_scope'}, 'blimp_sentential_subject_island': {'acc,none': 0.502, 'acc_stderr,none': 0.015819173374302702, 'alias': '  - blimp_sentential_subject_island'}, 'blimp_superlative_quantifiers_1': {'acc,none': 0.955, 'acc_stderr,none': 0.0065588122414061215, 'alias': '  - blimp_superlative_quantifiers_1'}, 'blimp_superlative_quantifiers_2': {'acc,none': 0.963, 'acc_stderr,none': 0.005972157622389623, 'alias': '  - blimp_superlative_quantifiers_2'}, 'blimp_tough_vs_raising_1': {'acc,none': 0.594, 'acc_stderr,none': 0.015537226438634595, 'alias': '  - blimp_tough_vs_raising_1'}, 'blimp_tough_vs_raising_2': {'acc,none': 0.885, 'acc_stderr,none': 0.01009340759490462, 'alias': '  - blimp_tough_vs_raising_2'}, 'blimp_transitive': {'acc,none': 0.886, 'acc_stderr,none': 0.010055103435823335, 'alias': '  - blimp_transitive'}, 'blimp_wh_island': {'acc,none': 0.744, 'acc_stderr,none': 0.013807775152234195, 'alias': '  - blimp_wh_island'}, 'blimp_wh_questions_object_gap': {'acc,none': 0.852, 'acc_stderr,none': 0.011234866364235247, 'alias': '  - blimp_wh_questions_object_gap'}, 'blimp_wh_questions_subject_gap': {'acc,none': 0.922, 'acc_stderr,none': 0.008484573530118581, 'alias': '  - blimp_wh_questions_subject_gap'}, 'blimp_wh_questions_subject_gap_long_distance': {'acc,none': 0.92, 'acc_stderr,none': 0.008583336977753653, 'alias': '  - blimp_wh_questions_subject_gap_long_distance'}, 'blimp_wh_vs_that_no_gap': {'acc,none': 0.977, 'acc_stderr,none': 0.004742730594656796, 'alias': '  - blimp_wh_vs_that_no_gap'}, 'blimp_wh_vs_that_no_gap_long_distance': {'acc,none': 0.97, 'acc_stderr,none': 0.005397140829099193, 'alias': '  - blimp_wh_vs_that_no_gap_long_distance'}, 'blimp_wh_vs_that_with_gap': {'acc,none': 0.364, 'acc_stderr,none': 0.01522286884052202, 'alias': '  - blimp_wh_vs_that_with_gap'}, 'blimp_wh_vs_that_with_gap_long_distance': {'acc,none': 0.329, 'acc_stderr,none': 0.014865395385928355, 'alias': '  - blimp_wh_vs_that_with_gap_long_distance'}, 'sciq': {'acc,none': 0.959, 'acc_stderr,none': 0.006273624021118743, 'acc_norm,none': 0.939, 'acc_norm_stderr,none': 0.007572076091557429, 'alias': ' - sciq'}, 'xcopa': {'acc,none': 0.5594545454545454, 'acc_stderr,none': 0.05387910421610255, 'alias': 'xcopa'}, 'xcopa_et': {'acc,none': 0.472, 'acc_stderr,none': 0.022347949832668093, 'alias': ' - xcopa_et'}, 'xcopa_ht': {'acc,none': 0.516, 'acc_stderr,none': 0.022371610982580396, 'alias': ' - xcopa_ht'}, 'xcopa_id': {'acc,none': 0.584, 'acc_stderr,none': 0.022064943313928866, 'alias': ' - xcopa_id'}, 'xcopa_it': {'acc,none': 0.66, 'acc_stderr,none': 0.021206117013673066, 'alias': ' - xcopa_it'}, 'xcopa_qu': {'acc,none': 0.484, 'acc_stderr,none': 0.022371610982580396, 'alias': ' - xcopa_qu'}, 'xcopa_sw': {'acc,none': 0.518, 'acc_stderr,none': 0.02236856511738799, 'alias': ' - xcopa_sw'}, 'xcopa_ta': {'acc,none': 0.546, 'acc_stderr,none': 0.022288147591176945, 'alias': ' - xcopa_ta'}, 'xcopa_th': {'acc,none': 0.566, 'acc_stderr,none': 0.02218721580302901, 'alias': ' - xcopa_th'}, 'xcopa_tr': {'acc,none': 0.57, 'acc_stderr,none': 0.022162634426652835, 'alias': ' - xcopa_tr'}, 'xcopa_vi': {'acc,none': 0.588, 'acc_stderr,none': 0.022033677993740862, 'alias': ' - xcopa_vi'}, 'xcopa_zh': {'acc,none': 0.65, 'acc_stderr,none': 0.021352091786223104, 'alias': ' - xcopa_zh'}, 'crows_pairs': {'likelihood_diff,none': 4.039303237651172, 'likelihood_diff_stderr,none': 0.4554088930609172, 'pct_stereotype,none': 0.5945140131186643, 'pct_stereotype_stderr,none': 0.08619149455497793, 'alias': 'crows_pairs'}, 'crows_pairs_english': {'likelihood_diff,none': 4.172200931419414, 'likelihood_diff_stderr,none': 0.09444066856881643, 'pct_stereotype,none': 0.6583184257602862, 'pct_stereotype_stderr,none': 0.0115848863578411, 'alias': ' - crows_pairs_english'}, 'crows_pairs_english_age': {'likelihood_diff,none': 3.81760980794718, 'likelihood_diff_stderr,none': 0.39572701594612664, 'pct_stereotype,none': 0.7032967032967034, 'pct_stereotype_stderr,none': 0.04815143362682777, 'alias': ' - crows_pairs_english_age'}, 'crows_pairs_english_autre': {'likelihood_diff,none': 8.978192589499734, 'likelihood_diff_stderr,none': 2.383266706466439, 'pct_stereotype,none': 0.8181818181818182, 'pct_stereotype_stderr,none': 0.12196734422726126, 'alias': ' - crows_pairs_english_autre'}, 'crows_pairs_english_disability': {'likelihood_diff,none': 6.123607283372145, 'likelihood_diff_stderr,none': 0.6269717764216254, 'pct_stereotype,none': 0.7538461538461538, 'pct_stereotype_stderr,none': 0.05384615384615383, 'alias': ' - crows_pairs_english_disability'}, 'crows_pairs_english_gender': {'likelihood_diff,none': 3.593177890777588, 'likelihood_diff_stderr,none': 0.1891835945318628, 'pct_stereotype,none': 0.565625, 'pct_stereotype_stderr,none': 0.02775245248136475, 'alias': ' - crows_pairs_english_gender'}, 'crows_pairs_english_nationality': {'likelihood_diff,none': 3.503406215597082, 'likelihood_diff_stderr,none': 0.2223140250179109, 'pct_stereotype,none': 0.5648148148148148, 'pct_stereotype_stderr,none': 0.03381200005643525, 'alias': ' - crows_pairs_english_nationality'}, 'crows_pairs_english_physical_appearance': {'likelihood_diff,none': 4.352519141303168, 'likelihood_diff_stderr,none': 0.429502683647509, 'pct_stereotype,none': 0.7777777777777778, 'pct_stereotype_stderr,none': 0.04933922619854289, 'alias': ' - crows_pairs_english_physical_appearance'}, 'crows_pairs_english_race_color': {'likelihood_diff,none': 4.192574343343419, 'likelihood_diff_stderr,none': 0.18220038540379538, 'pct_stereotype,none': 0.6220472440944882, 'pct_stereotype_stderr,none': 0.02153408701954117, 'alias': ' - crows_pairs_english_race_color'}, 'crows_pairs_english_religion': {'likelihood_diff,none': 4.5383266930107595, 'likelihood_diff_stderr,none': 0.34676935247597424, 'pct_stereotype,none': 0.8198198198198198, 'pct_stereotype_stderr,none': 0.03664513893725976, 'alias': ' - 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blimp'}, 'xcopa': {'acc,none': 0.5594545454545454, 'acc_stderr,none': 0.05387910421610255, 'alias': 'xcopa'}, 'crows_pairs': {'likelihood_diff,none': 4.039303237651172, 'likelihood_diff_stderr,none': 0.4554088930609172, 'pct_stereotype,none': 0.5945140131186643, 'pct_stereotype_stderr,none': 0.08619149455497793, 'alias': 'crows_pairs'}, 'xnli': {'acc,none': 0.43052208835341366, 'acc_stderr,none': 0.05866526647573086, 'alias': 'xnli'}, 'truthfulqa': {'acc,none': 0.35372429452780935, 'acc_stderr,none': 0.0015289998228574559, 'bleu_max,none': 30.506553066296135, 'bleu_max_stderr,none': 0.8545048499620589, 'bleu_acc,none': 0.41370869033047736, 'bleu_acc_stderr,none': 0.0172408618120998, 'bleu_diff,none': -1.4792658300937298, 'bleu_diff_stderr,none': 1.0365996246291096, 'rouge1_max,none': 55.752927992652076, 'rouge1_max_stderr,none': 0.9304517781051922, 'rouge1_acc,none': 0.397796817625459, 'rouge1_acc_stderr,none': 0.017133934248559652, 'rouge1_diff,none': -1.916659079286129, 'rouge1_diff_stderr,none': 1.2644440329641677, 'rouge2_max,none': 40.96291082037781, 'rouge2_max_stderr,none': 1.1007195429525476, 'rouge2_acc,none': 0.3537331701346389, 'rouge2_acc_stderr,none': 0.016737814358846147, 'rouge2_diff,none': -2.594294632926603, 'rouge2_diff_stderr,none': 1.4182491490828897, 'rougeL_max,none': 53.07319351290984, 'rougeL_max_stderr,none': 0.9465416042598798, 'rougeL_acc,none': 0.40269277845777235, 'rougeL_acc_stderr,none': 0.017168830935187215, 'rougeL_diff,none': -2.232349573503825, 'rougeL_diff_stderr,none': 1.2801381361084667, 'alias': 'truthfulqa'}, 'ceval-valid': {'acc,none': 0.40713224368499257, 'acc_stderr,none': 0.14694388399894986, 'acc_norm,none': 0.40713224368499257, 'acc_norm_stderr,none': 0.14694388399894986, 'alias': 'ceval-valid'}, 'freebase': {'exact_match,none': 0.15403543307086615, 'exact_match_stderr,none': 0.008009980186286517, 'alias': 'freebase'}, 'cmmlu': {'acc,none': 0.3979450872042825, 'acc_stderr,none': 0.09095017592357733, 'acc_norm,none': 0.3979450872042825, 'acc_norm_stderr,none': 0.09095017592357733, 'alias': 'cmmlu'}, 'lambada': {'perplexity,none': 3.477999261370286, 'perplexity_stderr,none': 0.16329169211004324, 'acc,none': 0.7254997089074325, 'acc_stderr,none': 0.01676361499326025, 'alias': 'lambada'}, 'sycophancy': {'acc,none': 0.803766929553093, 'acc_stderr,none': 0.08597393979979331, 'alias': 'sycophancy'}, 'qa4mre': {'acc,none': 0.4875886524822695, 'acc_stderr,none': 0.04415178467483081, 'acc_norm,none': 0.5372340425531915, 'acc_norm_stderr,none': 0.07588716246805031, 'alias': 'qa4mre'}, 'ai2_arc': {'acc,none': 0.7065952649379932, 'acc_stderr,none': 0.09736868212773775, 'acc_norm,none': 0.7130777903043969, 'acc_norm_stderr,none': 0.08088060213614173, 'alias': ' - ai2_arc'}, 'pawsx': {'acc,none': 0.4147857142857143, 'acc_stderr,none': 0.057291271279150895, 'alias': 'pawsx'}, 'multimedqa': {'alias': 'stem', 'acc,none': 0.5325762952448545, 'acc_stderr,none': 0.06628005425577563, 'acc_norm,none': 0.4918553467234212, 'acc_norm_stderr,none': 0.0001327699679693839}, 'kmmlu': {'acc,none': 0.33245740687265374, 'acc_stderr,none': 0.047791173115529544, 'acc_norm,none': 0.33245740687265374, 'acc_norm_stderr,none': 0.047791173115529544, 'alias': 'kmmlu'}, 'kobest': {'acc,none': 0.5507564130673098, 'acc_stderr,none': 0.05115523112210208, 'f1,none': 0.4791948665252917, 'f1_stderr,none': 'N/A', 'acc_norm,none': 0.518, 'acc_norm_stderr,none': 0.0005003527054108269, 'alias': 'kobest'}, 'arithmetic': {'acc,none': 0.90035, 'acc_stderr,none': 0.10511691499262178, 'alias': 'arithmetic'}, 'lambada_multilingual': {'perplexity,none': 27.056756454439657, 'perplexity_stderr,none': 10.55570565115782, 'acc,none': 0.5186493304870949, 'acc_stderr,none': 0.09575650539776685, 'alias': 'lambada_multilingual'}, 'pythia': {'acc,none': 0.7809920153879631, 'acc_stderr,none': 0.15963097565490927, 'acc_norm,none': 0.7148213731718109, 'acc_norm_stderr,none': 0.0082806592848003, 'word_perplexity,none': 17.952971816297133, 'word_perplexity_stderr,none': 'N/A', 'byte_perplexity,none': 1.7160493234446637, 'byte_perplexity_stderr,none': 'N/A', 'bits_per_byte,none': 0.7790910200090022, 'bits_per_byte_stderr,none': 'N/A', 'perplexity,none': 3.180029723439131, 'perplexity_stderr,none': 0.05830830767398106, 'alias': 'pythia'}, 'mmlu': {'acc,none': 0.596425010682239, 'acc_stderr,none': 0.13844007438448744, 'alias': 'mmlu'}, 'mmlu_humanities': {'alias': ' - humanities', 'acc,none': 0.5343251859723698, 'acc_stderr,none': 0.16186401202777495}, 'mmlu_other': {'alias': ' - other', 'acc,none': 0.6816865143224976, 'acc_stderr,none': 0.09811125388055714}, 'mmlu_social_sciences': {'alias': ' - social_sciences', 'acc,none': 0.6958076048098798, 'acc_stderr,none': 0.08956129050566648}, 'mmlu_stem': {'alias': ' - stem', 'acc,none': 0.5080875356803045, 'acc_stderr,none': 0.1215476495130753}, 'xwinograd': {'acc,none': 0.8120926050797932, 'acc_stderr,none': 0.04148122639462828, 'alias': 'xwinograd'}, 'xstorycloze': {'acc,none': 0.5916611515552614, 'acc_stderr,none': 0.07661315123253779, 'alias': 'xstorycloze'}}}}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# Global result map if it's not set\n",
    "if 'global_result_map' not in globals():\n",
    "    global_result_map = {}\n",
    "\n",
    "#\n",
    "# Function to process the results.json file\n",
    "#\n",
    "def process_results_json(file_path):\n",
    "    with open(file_path) as f:\n",
    "        data = json.load(f)\n",
    "\n",
    "    # Model args, presplit by ','\n",
    "    model_args = data['config']['model_args'].split(',')\n",
    "\n",
    "    # Extract the pretrained value from config.model_args\n",
    "    modelname = model_args[0].split('=')[1]\n",
    "\n",
    "    # Opt array\n",
    "    confArgsArr = model_args[1:]\n",
    "\n",
    "    # Sort the opt array\n",
    "    confArgsArr.sort()\n",
    "    # Convert it to a string\n",
    "    confStr = ','.join(confArgsArr)\n",
    "\n",
    "    # Convert the option array of key=value strings to a dictionary\n",
    "    confObj = { }\n",
    "    for o in confArgsArr:\n",
    "        k, v = o.split('=')\n",
    "        confObj[k] = v\n",
    "    \n",
    "    # Create a dictionary to store the results, or use the existing one if it exists\n",
    "    if modelname in global_result_map:\n",
    "        modelObj = global_result_map[modelname]\n",
    "    else:\n",
    "        modelObj = {\n",
    "            'name': modelname,\n",
    "            'config': { }\n",
    "        }\n",
    "    \n",
    "    # Get the opt object for the model\n",
    "    if confStr in modelObj['config']:\n",
    "        confSet = modelObj['config'][confStr]\n",
    "    else:\n",
    "        confSet = {\n",
    "            'confStr': confStr,\n",
    "            'confObj': confObj,\n",
    "            'results': {},\n",
    "            'groups': {}\n",
    "        }\n",
    "\n",
    "    # Iterate over the results and extract the result object for each test/group\n",
    "    if 'results' in data:\n",
    "        for test, result in data['results'].items():\n",
    "            confSet['results'][test] = result\n",
    "    if 'groups' in data:\n",
    "        for test, result in data['groups'].items():\n",
    "            confSet['groups'][test] = result\n",
    "    \n",
    "    # Update the global result map object\n",
    "    modelObj['config'][confStr] = confSet\n",
    "    global_result_map[modelname] = modelObj\n",
    "    return modelObj\n",
    "\n",
    "# Lets test the function with the first results.json file\n",
    "first_result = process_results_json(results_json_files[0])\n",
    "print(f\"Processed example: \", first_result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 44 models\n",
      "Models: \n",
      "['mistralai/Mistral-7B-v0.1', 'mosaicml/mpt-7b-instruct', 'mosaicml/mpt-7b', 'mosaicml/mpt-7b-chat', 'bigscience/bloom-7b1', 'bigscience/bloomz-7b1-mt', 'bigscience/bloomz-7b1', 'EleutherAI/pythia-2.8b', 'EleutherAI/pythia-1.4b', 'EleutherAI/gpt-j-6b', 'EleutherAI/pythia-6.9b', 'microsoft/phi-1_5', 'microsoft/phi-2', 'microsoft/phi-1', 'allenai/OLMo-7B', 'TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T', 'TinyLlama/TinyLlama-1.1B-Chat-v1.0', 'RWKV/rwkv-5-world-1b5', 'RWKV/rwkv-5-world-3b', 'RWKV/rwkv-4-world-3b', 'RWKV/rwkv-4-world-1b5', 'RWKV/v5-Eagle-7B-HF', 'RWKV/rwkv-4-world-7b', './rwkv-x-dev/chunk4-0_85_pth', './rwkv-x-dev/chunk0-0_8_pth', './rwkv-x-dev/RWKV-5-World-1B5-v2-20231025-ctx4096', './rwkv-x-dev/RWKV-5-World-3B-v2-20231118-ctx16k', './rwkv-x-dev/RWKV-5-World-7B-v2-20240128-ctx4096', './rwkv-x-dev/chunk6-0_85_pth', './rwkv-x-dev/chunk7-1-0_85_pth', './rwkv-x-dev/Hermes-RWKV-v5-7B_pth', 'togethercomputer/RedPajama-INCITE-7B-Base', 'togethercomputer/RedPajama-INCITE-7B-Instruct', 'togethercomputer/RedPajama-INCITE-7B-Chat', 'facebook/opt-2.7b', 'facebook/opt-6.7b', 'facebook/opt-1.3b', 'tiiuae/falcon-7b-instruct', 'tiiuae/falcon-rw-1b', 'tiiuae/falcon-rw-7b', 'tiiuae/falcon-7b', 'huggyllama/llama-7b', 'meta-llama/Llama-2-7b-chat-hf', 'meta-llama/Llama-2-7b-hf']\n",
      "Saved to compiled-lm-eval-results.json\n"
     ]
    }
   ],
   "source": [
    "# Lets reset and reprocess all the results.json files\n",
    "global_result_map = {}\n",
    "\n",
    "# Process all the results.json files\n",
    "for file in results_json_files:\n",
    "    process_results_json(file)\n",
    "\n",
    "# Show high level list of models\n",
    "print(f\"Found {len(global_result_map)} models\")\n",
    "print(f\"Models: \\n{list(global_result_map.keys())}\")\n",
    "\n",
    "# Save the result map to a file\n",
    "with open('summary/compiled-lm-eval-results.json', 'w') as f:\n",
    "    json.dump(global_result_map, f, sort_keys=True, indent='\\t')\n",
    "\n",
    "# Echo that its been saved to json\n",
    "print(f\"Saved to compiled-lm-eval-results.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert the results into CSV table formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>avg_acc</th>\n",
       "      <th>avg_acc_stderr</th>\n",
       "      <th>xcopa (acc)</th>\n",
       "      <th>xcopa (acc_stderr)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mistralai/Mistral-7B-v0.1</td>\n",
       "      <td>0.559455</td>\n",
       "      <td>0.053879</td>\n",
       "      <td>0.559455</td>\n",
       "      <td>0.053879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mosaicml/mpt-7b-instruct</td>\n",
       "      <td>0.537091</td>\n",
       "      <td>0.041919</td>\n",
       "      <td>0.537091</td>\n",
       "      <td>0.041919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mosaicml/mpt-7b</td>\n",
       "      <td>0.536000</td>\n",
       "      <td>0.042339</td>\n",
       "      <td>0.536000</td>\n",
       "      <td>0.042339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mosaicml/mpt-7b-chat</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>0.047059</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>0.047059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bigscience/bloom-7b1</td>\n",
       "      <td>0.570909</td>\n",
       "      <td>0.061359</td>\n",
       "      <td>0.570909</td>\n",
       "      <td>0.061359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bigscience/bloomz-7b1-mt</td>\n",
       "      <td>0.546000</td>\n",
       "      <td>0.038321</td>\n",
       "      <td>0.546000</td>\n",
       "      <td>0.038321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bigscience/bloomz-7b1</td>\n",
       "      <td>0.547818</td>\n",
       "      <td>0.038920</td>\n",
       "      <td>0.547818</td>\n",
       "      <td>0.038920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EleutherAI/pythia-2.8b</td>\n",
       "      <td>0.537455</td>\n",
       "      <td>0.026941</td>\n",
       "      <td>0.537455</td>\n",
       "      <td>0.026941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EleutherAI/pythia-1.4b</td>\n",
       "      <td>0.526545</td>\n",
       "      <td>0.027441</td>\n",
       "      <td>0.526545</td>\n",
       "      <td>0.027441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EleutherAI/gpt-j-6b</td>\n",
       "      <td>0.544182</td>\n",
       "      <td>0.034404</td>\n",
       "      <td>0.544182</td>\n",
       "      <td>0.034404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EleutherAI/pythia-6.9b</td>\n",
       "      <td>0.540545</td>\n",
       "      <td>0.029689</td>\n",
       "      <td>0.540545</td>\n",
       "      <td>0.029689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>microsoft/phi-1_5</td>\n",
       "      <td>0.521636</td>\n",
       "      <td>0.026198</td>\n",
       "      <td>0.521636</td>\n",
       "      <td>0.026198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>microsoft/phi-2</td>\n",
       "      <td>0.512182</td>\n",
       "      <td>0.029742</td>\n",
       "      <td>0.512182</td>\n",
       "      <td>0.029742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>microsoft/phi-1</td>\n",
       "      <td>0.517636</td>\n",
       "      <td>0.029612</td>\n",
       "      <td>0.517636</td>\n",
       "      <td>0.029612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>allenai/OLMo-7B</td>\n",
       "      <td>0.537818</td>\n",
       "      <td>0.034147</td>\n",
       "      <td>0.537818</td>\n",
       "      <td>0.034147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TinyLlama/TinyLlama-1.1B-intermediate-step-143...</td>\n",
       "      <td>0.529273</td>\n",
       "      <td>0.029316</td>\n",
       "      <td>0.529273</td>\n",
       "      <td>0.029316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TinyLlama/TinyLlama-1.1B-Chat-v1.0</td>\n",
       "      <td>0.528909</td>\n",
       "      <td>0.031702</td>\n",
       "      <td>0.528909</td>\n",
       "      <td>0.031702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RWKV/rwkv-5-world-1b5</td>\n",
       "      <td>0.578909</td>\n",
       "      <td>0.045103</td>\n",
       "      <td>0.578909</td>\n",
       "      <td>0.045103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RWKV/rwkv-5-world-3b</td>\n",
       "      <td>0.590182</td>\n",
       "      <td>0.056241</td>\n",
       "      <td>0.590182</td>\n",
       "      <td>0.056241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RWKV/rwkv-4-world-3b</td>\n",
       "      <td>0.575455</td>\n",
       "      <td>0.040977</td>\n",
       "      <td>0.575455</td>\n",
       "      <td>0.040977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RWKV/rwkv-4-world-1b5</td>\n",
       "      <td>0.554000</td>\n",
       "      <td>0.039406</td>\n",
       "      <td>0.554000</td>\n",
       "      <td>0.039406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>RWKV/v5-Eagle-7B-HF</td>\n",
       "      <td>0.621818</td>\n",
       "      <td>0.068986</td>\n",
       "      <td>0.621818</td>\n",
       "      <td>0.068986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RWKV/rwkv-4-world-7b</td>\n",
       "      <td>0.601455</td>\n",
       "      <td>0.053116</td>\n",
       "      <td>0.601455</td>\n",
       "      <td>0.053116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>togethercomputer/RedPajama-INCITE-7B-Base</td>\n",
       "      <td>0.525455</td>\n",
       "      <td>0.036407</td>\n",
       "      <td>0.525455</td>\n",
       "      <td>0.036407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>togethercomputer/RedPajama-INCITE-7B-Instruct</td>\n",
       "      <td>0.528545</td>\n",
       "      <td>0.036470</td>\n",
       "      <td>0.528545</td>\n",
       "      <td>0.036470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>togethercomputer/RedPajama-INCITE-7B-Chat</td>\n",
       "      <td>0.535455</td>\n",
       "      <td>0.038723</td>\n",
       "      <td>0.535455</td>\n",
       "      <td>0.038723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>facebook/opt-2.7b</td>\n",
       "      <td>0.521818</td>\n",
       "      <td>0.029821</td>\n",
       "      <td>0.521818</td>\n",
       "      <td>0.029821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>facebook/opt-6.7b</td>\n",
       "      <td>0.522909</td>\n",
       "      <td>0.027216</td>\n",
       "      <td>0.522909</td>\n",
       "      <td>0.027216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>facebook/opt-1.3b</td>\n",
       "      <td>0.521818</td>\n",
       "      <td>0.029112</td>\n",
       "      <td>0.521818</td>\n",
       "      <td>0.029112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>tiiuae/falcon-7b-instruct</td>\n",
       "      <td>0.536727</td>\n",
       "      <td>0.053430</td>\n",
       "      <td>0.536727</td>\n",
       "      <td>0.053430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>tiiuae/falcon-rw-1b</td>\n",
       "      <td>0.522545</td>\n",
       "      <td>0.029446</td>\n",
       "      <td>0.522545</td>\n",
       "      <td>0.029446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>tiiuae/falcon-rw-7b</td>\n",
       "      <td>0.535818</td>\n",
       "      <td>0.033185</td>\n",
       "      <td>0.535818</td>\n",
       "      <td>0.033185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>tiiuae/falcon-7b</td>\n",
       "      <td>0.559636</td>\n",
       "      <td>0.071650</td>\n",
       "      <td>0.559636</td>\n",
       "      <td>0.071650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>huggyllama/llama-7b</td>\n",
       "      <td>0.541818</td>\n",
       "      <td>0.040718</td>\n",
       "      <td>0.541818</td>\n",
       "      <td>0.040718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>meta-llama/Llama-2-7b-chat-hf</td>\n",
       "      <td>0.559818</td>\n",
       "      <td>0.054954</td>\n",
       "      <td>0.559818</td>\n",
       "      <td>0.054954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>meta-llama/Llama-2-7b-hf</td>\n",
       "      <td>0.566727</td>\n",
       "      <td>0.052515</td>\n",
       "      <td>0.566727</td>\n",
       "      <td>0.052515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model   avg_acc  \\\n",
       "0                           mistralai/Mistral-7B-v0.1  0.559455   \n",
       "1                            mosaicml/mpt-7b-instruct  0.537091   \n",
       "2                                     mosaicml/mpt-7b  0.536000   \n",
       "3                                mosaicml/mpt-7b-chat  0.538000   \n",
       "4                                bigscience/bloom-7b1  0.570909   \n",
       "5                            bigscience/bloomz-7b1-mt  0.546000   \n",
       "6                               bigscience/bloomz-7b1  0.547818   \n",
       "7                              EleutherAI/pythia-2.8b  0.537455   \n",
       "8                              EleutherAI/pythia-1.4b  0.526545   \n",
       "9                                 EleutherAI/gpt-j-6b  0.544182   \n",
       "10                             EleutherAI/pythia-6.9b  0.540545   \n",
       "11                                  microsoft/phi-1_5  0.521636   \n",
       "12                                    microsoft/phi-2  0.512182   \n",
       "13                                    microsoft/phi-1  0.517636   \n",
       "14                                    allenai/OLMo-7B  0.537818   \n",
       "15  TinyLlama/TinyLlama-1.1B-intermediate-step-143...  0.529273   \n",
       "16                 TinyLlama/TinyLlama-1.1B-Chat-v1.0  0.528909   \n",
       "17                              RWKV/rwkv-5-world-1b5  0.578909   \n",
       "18                               RWKV/rwkv-5-world-3b  0.590182   \n",
       "19                               RWKV/rwkv-4-world-3b  0.575455   \n",
       "20                              RWKV/rwkv-4-world-1b5  0.554000   \n",
       "21                                RWKV/v5-Eagle-7B-HF  0.621818   \n",
       "22                               RWKV/rwkv-4-world-7b  0.601455   \n",
       "23          togethercomputer/RedPajama-INCITE-7B-Base  0.525455   \n",
       "24      togethercomputer/RedPajama-INCITE-7B-Instruct  0.528545   \n",
       "25          togethercomputer/RedPajama-INCITE-7B-Chat  0.535455   \n",
       "26                                  facebook/opt-2.7b  0.521818   \n",
       "27                                  facebook/opt-6.7b  0.522909   \n",
       "28                                  facebook/opt-1.3b  0.521818   \n",
       "29                          tiiuae/falcon-7b-instruct  0.536727   \n",
       "30                                tiiuae/falcon-rw-1b  0.522545   \n",
       "31                                tiiuae/falcon-rw-7b  0.535818   \n",
       "32                                   tiiuae/falcon-7b  0.559636   \n",
       "33                                huggyllama/llama-7b  0.541818   \n",
       "34                      meta-llama/Llama-2-7b-chat-hf  0.559818   \n",
       "35                           meta-llama/Llama-2-7b-hf  0.566727   \n",
       "\n",
       "    avg_acc_stderr  xcopa (acc)  xcopa (acc_stderr)  \n",
       "0         0.053879     0.559455            0.053879  \n",
       "1         0.041919     0.537091            0.041919  \n",
       "2         0.042339     0.536000            0.042339  \n",
       "3         0.047059     0.538000            0.047059  \n",
       "4         0.061359     0.570909            0.061359  \n",
       "5         0.038321     0.546000            0.038321  \n",
       "6         0.038920     0.547818            0.038920  \n",
       "7         0.026941     0.537455            0.026941  \n",
       "8         0.027441     0.526545            0.027441  \n",
       "9         0.034404     0.544182            0.034404  \n",
       "10        0.029689     0.540545            0.029689  \n",
       "11        0.026198     0.521636            0.026198  \n",
       "12        0.029742     0.512182            0.029742  \n",
       "13        0.029612     0.517636            0.029612  \n",
       "14        0.034147     0.537818            0.034147  \n",
       "15        0.029316     0.529273            0.029316  \n",
       "16        0.031702     0.528909            0.031702  \n",
       "17        0.045103     0.578909            0.045103  \n",
       "18        0.056241     0.590182            0.056241  \n",
       "19        0.040977     0.575455            0.040977  \n",
       "20        0.039406     0.554000            0.039406  \n",
       "21        0.068986     0.621818            0.068986  \n",
       "22        0.053116     0.601455            0.053116  \n",
       "23        0.036407     0.525455            0.036407  \n",
       "24        0.036470     0.528545            0.036470  \n",
       "25        0.038723     0.535455            0.038723  \n",
       "26        0.029821     0.521818            0.029821  \n",
       "27        0.027216     0.522909            0.027216  \n",
       "28        0.029112     0.521818            0.029112  \n",
       "29        0.053430     0.536727            0.053430  \n",
       "30        0.029446     0.522545            0.029446  \n",
       "31        0.033185     0.535818            0.033185  \n",
       "32        0.071650     0.559636            0.071650  \n",
       "33        0.040718     0.541818            0.040718  \n",
       "34        0.054954     0.559818            0.054954  \n",
       "35        0.052515     0.566727            0.052515  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets convert this into a table, which we will display in this notebook, and save as a CSV\n",
    "import pandas as pd\n",
    "\n",
    "##################################################\n",
    "#\n",
    "#  Utility functions\n",
    "#\n",
    "##################################################\n",
    "\n",
    "# Check if the given name string, is within the list, including \"*\" wildcard\n",
    "def is_in_list(name, list):\n",
    "    for n in list:\n",
    "        if n[-1] == '*':\n",
    "            if name.startswith(n[:-1]):\n",
    "                return True\n",
    "        elif n == name:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "# Is in inclusion exclusion list pair\n",
    "def is_in_list_pair(name, inList, exList):\n",
    "    if not is_in_list(name, inList):\n",
    "        return False\n",
    "    if is_in_list(name, exList):\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "# Prepare a single test/group result object\n",
    "# By applying common filtering and formatting changes\n",
    "def prepare_test_result(result):\n",
    "    # The reutrn object\n",
    "    ret = {}\n",
    "    # Iterate the result key/value\n",
    "    for k, v in result.items():\n",
    "        # Skip if its alias\n",
    "        if k == 'alias':\n",
    "            continue\n",
    "\n",
    "        # If the key ends with \",none\", drop the \",none\"\n",
    "        if k.endswith(',none'):\n",
    "            k = k[:-5]\n",
    "        \n",
    "        # Save the result\n",
    "        ret[k] = v\n",
    "    \n",
    "    # Return the result\n",
    "    return ret\n",
    "\n",
    "##################################################\n",
    "#\n",
    "#  Generate the result\n",
    "#\n",
    "##################################################\n",
    "\n",
    "# Create a list of rows for the table\n",
    "def generate_result_table(\n",
    "    inConfig = { \"dtype\": \"bfloat16\" },\n",
    "\n",
    "    # Models to include/exclude\n",
    "    inModels = [\"*\"],\n",
    "    exModels = [\"./rwkv-x-dev/*\", \"rwkv-x-dev\"],\n",
    "\n",
    "    # Results and groups to include\n",
    "    inResults = [],\n",
    "    inGroups = [\"*\"],\n",
    "\n",
    "    # Exclude results and groups, applied after inResults and inGroups\n",
    "    exResults = [],\n",
    "    exGroups = [],\n",
    "\n",
    "    # Sorted\n",
    "    sort = False\n",
    "):\n",
    "    table_rows = []\n",
    "\n",
    "    # Iterate over the models\n",
    "    for model, modelObj in global_result_map.items():\n",
    "        # Skip if not in the inModels or exModels\n",
    "        if not is_in_list_pair(model, inModels, exModels):\n",
    "            continue\n",
    "\n",
    "        # Iterate over the configurations\n",
    "        for confStr, confSet in modelObj['config'].items():\n",
    "            # Get the confObj\n",
    "            confObj = confSet['confObj']\n",
    "\n",
    "            # Check if the inConfig, matches the confObj\n",
    "            if inConfig:\n",
    "                skip = False\n",
    "                for k, v in inConfig.items():\n",
    "                    if k not in confObj or confObj[k] != v:\n",
    "                        skip = True\n",
    "                        break\n",
    "                if skip:\n",
    "                    continue\n",
    "\n",
    "            # Create a row object\n",
    "            row = {\n",
    "                'model': model,\n",
    "                # 'config': confStr\n",
    "\n",
    "                \"avg_acc\": 0.0,\n",
    "                \"avg_acc_stderr\": 0.0,\n",
    "            }\n",
    "\n",
    "            # Total acc / acc_stderr\n",
    "            acc_total = 0.0\n",
    "            acc_count = 0\n",
    "            acc_stderr_total = 0.0\n",
    "            acc_stderr_count = 0\n",
    "\n",
    "            # Add the groups\n",
    "            for test, result in confSet['groups'].items():\n",
    "\n",
    "                # Skip if not in the inGroups or exGroups\n",
    "                if not is_in_list_pair(test, inGroups, exGroups):\n",
    "                    continue\n",
    "\n",
    "                # Filter the result obj\n",
    "                cleanResult = prepare_test_result(result)\n",
    "\n",
    "                # Add the result to the row, as seperate columns for each key\n",
    "                for k, v in cleanResult.items():\n",
    "                    row[f\"{test} ({k})\"] = v\n",
    "\n",
    "                    if k == 'acc':\n",
    "                        acc_total += v\n",
    "                        acc_count += 1\n",
    "                    elif k == 'acc_stderr':\n",
    "                        acc_stderr_total += v\n",
    "                        acc_stderr_count += 1\n",
    "\n",
    "            # Add the results\n",
    "            for test, result in confSet['results'].items():\n",
    "\n",
    "                # Skip if not in the inResults or exResults\n",
    "                if not is_in_list_pair(test, inResults, exResults):\n",
    "                    continue\n",
    "\n",
    "                # Filter the result obj\n",
    "                cleanResult = prepare_test_result(result)\n",
    "\n",
    "                # Add the result to the row, as seperate columns for each key\n",
    "                for k, v in cleanResult.items():\n",
    "                    row[f\"{test} ({k})\"] = v\n",
    "\n",
    "                    if k == 'acc':\n",
    "                        acc_total += v\n",
    "                        acc_count += 1\n",
    "                    elif k == 'acc_stderr':\n",
    "                        acc_stderr_total += v\n",
    "                        acc_stderr_count += 1\n",
    "                \n",
    "            # Add the avg acc and acc_stderr\n",
    "            if acc_count > 0:\n",
    "               row[\"avg_acc\"] = acc_total / acc_count\n",
    "            if acc_stderr_count > 0:\n",
    "                row[\"avg_acc_stderr\"] = acc_stderr_total / acc_stderr_count\n",
    "\n",
    "            # Append the row to the table\n",
    "            table_rows.append(row)\n",
    "\n",
    "    # Create a dataframe from the table rows\n",
    "    df = pd.DataFrame(table_rows)\n",
    "\n",
    "    # Sort by avg_acc\n",
    "    if sort:\n",
    "        df = df.sort_values(by='avg_acc', ascending=False)\n",
    "\n",
    "    # Show the dataframe\n",
    "    return df\n",
    "\n",
    "# Generate the dataframe\n",
    "df = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"xcopa\"], inResults=[] )\n",
    "\n",
    "# # Save the dataframe to a CSV file\n",
    "# df.to_csv('summary/compiled-lm-eval-results.csv', index=False)\n",
    "\n",
    "# Show results\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 14936\n",
      "-rw-r--r--@ 1 picocreator  staff   930K Feb 26 01:25 bf16-all-results-and-groups.csv\n",
      "-rw-r--r--@ 1 picocreator  staff    60K Feb 26 01:25 bf16-eng-focus.csv\n",
      "-rw-r--r--@ 1 picocreator  staff   847K Feb 26 01:25 bf16-eng-results.csv\n",
      "-rw-r--r--@ 1 picocreator  staff    72K Feb 26 01:25 bf16-eng-summary.csv\n",
      "-rw-r--r--@ 1 picocreator  staff    86K Feb 26 01:25 bf16-multilang-results.csv\n",
      "-rw-r--r--@ 1 picocreator  staff    12K Feb 26 01:25 bf16-multilang-summary.csv\n",
      "-rw-r--r--@ 1 picocreator  staff    60K Feb 26 01:25 bf16-sorted-eng-focus.csv\n",
      "-rw-r--r--@ 1 picocreator  staff   847K Feb 26 01:25 bf16-sorted-eng-results.csv\n",
      "-rw-r--r--@ 1 picocreator  staff    72K Feb 26 01:25 bf16-sorted-eng-summary.csv\n",
      "-rw-r--r--@ 1 picocreator  staff    12K Feb 26 01:25 bf16-sorted-multilang-summary.csv\n",
      "-rw-r--r--  1 picocreator  staff   3.7M Feb 26 01:25 compiled-lm-eval-results.json\n",
      "-rw-r--r--  1 picocreator  staff    13K Feb 26 01:25 rwkv-x-dev-bf16-sorted-eng-focus.csv\n",
      "-rw-r--r--@ 1 picocreator  staff   3.8K Feb 26 01:25 rwkv-x-dev-bf16-sorted-multilang-summary.csv\n"
     ]
    }
   ],
   "source": [
    "##################################################\n",
    "#\n",
    "#  Build the various subsets\n",
    "#\n",
    "##################################################\n",
    "\n",
    "# Overall results\n",
    "all_results = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[\"*\"] )\n",
    "all_results.to_csv('summary/bf16-all-results-and-groups.csv', index=False)\n",
    "\n",
    "# Multilang results\n",
    "multiLang_tTest = [\"xcopa_*\", \"xnli_*\", \"xstorycloze_*\", \"xwinograd_*\", \"lambada_openai_*\",     \"pawsx_*\"]\n",
    "multiLang_tGrps = [\"xcopa\",   \"xnli\",   \"xstorycloze\",   \"xwinograd\",   \"lambada_multilingual\", \"pawsx\"]\n",
    "# Both test and groups, merged into a single list\n",
    "multiLang_joint = multiLang_tTest + multiLang_tGrps\n",
    "\n",
    "multilang_grp = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=[] )\n",
    "multilang_test = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=multiLang_tTest )\n",
    "multilang_grp.to_csv('summary/bf16-multilang-summary.csv', index=False)\n",
    "multilang_test.to_csv('summary/bf16-multilang-results.csv', index=False)\n",
    "\n",
    "multilang_grp_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=[], sort=True )\n",
    "multilang_grp_sorted.to_csv('summary/bf16-sorted-multilang-summary.csv', index=False)\n",
    "\n",
    "# RWKV perf tracking\n",
    "rwkv_multilang_grp_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=[], exModels=[], inModels=[\"./rwkv-x-dev/*\", \"rwkv-x-dev/*\", \"RWKV/*\"], sort=True )\n",
    "rwkv_multilang_grp_sorted.to_csv('summary/rwkv-x-dev-bf16-sorted-multilang-summary.csv', index=False)\n",
    "\n",
    "# All other results\n",
    "eng_grp = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[], exGroups=multiLang_joint, exResults=multiLang_joint )\n",
    "eng_grp_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[], exGroups=multiLang_joint, exResults=multiLang_joint, sort=True )\n",
    "eng_test = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[\"*\"], exGroups=multiLang_joint, exResults=multiLang_joint )\n",
    "eng_test_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[\"*\"], exGroups=multiLang_joint, exResults=multiLang_joint, sort=True )\n",
    "\n",
    "eng_grp.to_csv('summary/bf16-eng-summary.csv', index=False)\n",
    "eng_test.to_csv('summary/bf16-eng-results.csv', index=False)\n",
    "eng_test_sorted.to_csv('summary/bf16-sorted-eng-results.csv', index=False)\n",
    "eng_grp_sorted.to_csv('summary/bf16-sorted-eng-summary.csv', index=False)\n",
    "\n",
    "# English focused subset\n",
    "eng_focus_tGrps=[\"anli\", \"glue\", \"truthfulqa\", \"lambada\", \"cmmlu\", \"pythia\", \"mmlu\",    \"blimp\", \"trivaqa\", \"record\", \"np_open\", \"piqa\", \"copa\", \"sciq\"]\n",
    "eng_focus_tTest=[\"blimp\", \"arc_*\", \"logiqa\", \"winogrande\", \"openbookqa\", \"hellaswag\",   \"blimp\", \"trivaqa\", \"record\", \"np_open\", \"piqa\", \"copa\", \"sciq\"]\n",
    "eng_focus = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=eng_focus_tGrps, inResults=eng_focus_tTest )\n",
    "eng_focus_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=eng_focus_tGrps, inResults=eng_focus_tTest, sort=True )\n",
    "eng_focus.to_csv('summary/bf16-eng-focus.csv', index=False)\n",
    "eng_focus_sorted.to_csv('summary/bf16-sorted-eng-focus.csv', index=False)\n",
    "\n",
    "# RWKV perf tracking\n",
    "rwkv_eng_focus_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=eng_focus_tGrps, inResults=eng_focus_tTest, exModels=[], inModels=[\"./rwkv-x-dev/*\", \"rwkv-x-dev/*\", \"RWKV/*\"], sort=True )\n",
    "rwkv_eng_focus_sorted.to_csv('summary/rwkv-x-dev-bf16-sorted-eng-focus.csv', index=False)\n",
    "\n",
    "# List the files\n",
    "!ls -lh summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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