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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a429de48-964c-4ad8-aa98-b3b180321f0a",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import numpy as np\n",
"\n",
"from pathlib import Path\n",
"\n",
"from tabulate import tabulate"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e78d66f4-f7fa-4802-b870-c5b5375a56c7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[PosixPath('electra-base/scandeval_benchmark_results.jsonl'), PosixPath('roberta-base/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-token-dropping-finewebs-1m/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-token-dropping-finewebs-801k/scandeval_benchmark_results.jsonl'), PosixPath('teams-base-finewebs-901k/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-token-dropping-finewebs-851k/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-finewebs-851k/scandeval_benchmark_results.jsonl'), PosixPath('teams-base-finewebs-951k/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-finewebs-801k/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-finewebs-901k/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-cased/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-token-dropping-finewebs-901k/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-finewebs-1m/scandeval_benchmark_results.jsonl'), PosixPath('teams-base-finewebs-851k/scandeval_benchmark_results.jsonl'), PosixPath('teams-base-finewebs-1m/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-finewebs-951k/scandeval_benchmark_results.jsonl'), PosixPath('bert-base-token-dropping-finewebs-951k/scandeval_benchmark_results.jsonl'), PosixPath('teams-base-finewebs-801k/scandeval_benchmark_results.jsonl')]\n",
"google/electra-base-discriminator\n",
"FacebookAI/roberta-base\n",
"model-garden-lms/bert-base-token-dropping-finewebs-1m\n",
"model-garden-lms/bert-base-token-dropping-finewebs-801k\n",
"model-garden-lms/teams-base-finewebs-901k\n",
"model-garden-lms/bert-base-token-dropping-finewebs-851k\n",
"model-garden-lms/bert-base-finewebs-851k\n",
"model-garden-lms/teams-base-finewebs-951k\n",
"model-garden-lms/bert-base-finewebs-801k\n",
"model-garden-lms/bert-base-finewebs-901k\n",
"google-bert/bert-base-cased\n",
"model-garden-lms/bert-base-token-dropping-finewebs-901k\n",
"model-garden-lms/bert-base-finewebs-1m\n",
"model-garden-lms/teams-base-finewebs-851k\n",
"model-garden-lms/teams-base-finewebs-1m\n",
"model-garden-lms/bert-base-finewebs-951k\n",
"model-garden-lms/bert-base-token-dropping-finewebs-951k\n",
"model-garden-lms/teams-base-finewebs-801k\n"
]
}
],
"source": [
"benchmark_result_files = list(Path(\"./\").rglob(\"*scandeval_benchmark_results.jsonl\"))\n",
"\n",
"print(benchmark_result_files)\n",
"\n",
"model_id_results_mapping = {}\n",
"\n",
"for benchmark_result_file in benchmark_result_files: \n",
" model_id = None\n",
"\n",
" dataset_metrics_mapping = {}\n",
"\n",
" scores = []\n",
" \n",
" with open(benchmark_result_file) as f_p:\n",
" for line in f_p:\n",
" line = line.strip()\n",
" if not line:\n",
" continue\n",
" data = json.loads(line)\n",
"\n",
" model_id = data[\"model\"]\n",
" dataset = data[\"dataset\"]\n",
" total = data[\"results\"][\"total\"]\n",
" if dataset == \"conll-en\":\n",
" test_micro_f1_no_misc = round(total[\"test_micro_f1_no_misc\"], 2)\n",
" test_micro_f1_no_misc_se = round(total[\"test_micro_f1_no_misc_se\"], 2)\n",
" test_micro_f1 = round(total[\"test_micro_f1\"], 2)\n",
" test_micro_f1_se = round(total[\"test_micro_f1_se\"], 2)\n",
"\n",
" scores.append(test_micro_f1_no_misc)\n",
" scores.append(test_micro_f1)\n",
" \n",
" metric_string = f\"{test_micro_f1_no_misc} ± {test_micro_f1_no_misc_se} / {test_micro_f1} ± {test_micro_f1_se}\"\n",
" dataset_metrics_mapping[dataset] = metric_string\n",
" elif dataset in [\"sst5\", \"scala-en\"]:\n",
" test_mcc = round(total[\"test_mcc\"], 2)\n",
" test_mcc_se = round(total[\"test_mcc_se\"], 2)\n",
" test_macro_f1 = round(total[\"test_macro_f1\"], 2)\n",
" test_macro_f1_se = round(total[\"test_macro_f1_se\"], 2)\n",
"\n",
" scores.append(test_mcc)\n",
" scores.append(test_macro_f1)\n",
" \n",
" metric_string = f\"{test_mcc} ± {test_mcc_se} / {test_macro_f1} ± {test_macro_f1_se}\"\n",
" dataset_metrics_mapping[dataset] = metric_string\n",
" elif dataset == \"squad\":\n",
" test_em = round(total[\"test_em\"], 2)\n",
" test_em_se = round(total[\"test_em_se\"], 2)\n",
" test_f1 = round(total[\"test_f1\"], 2)\n",
" test_f1_se = round(total[\"test_f1_se\"], 2)\n",
"\n",
" scores.append(test_em)\n",
" scores.append(test_f1)\n",
" \n",
" metric_string = f\"{test_em} ± {test_em_se} / {test_f1} ± {test_f1_se}\"\n",
" dataset_metrics_mapping[dataset] = metric_string\n",
"\n",
" score = round(np.mean(scores), 2)\n",
" score_string = f\"{score}\"\n",
" \n",
" dataset_metrics_mapping[\"score\"] = score_string\n",
" \n",
" print(model_id)\n",
" \n",
" model_id_results_mapping[model_id] = dataset_metrics_mapping"
]
},
{
"cell_type": "markdown",
"id": "30cdec2c-d0da-49a5-9965-b923f8212340",
"metadata": {},
"source": [
"# Overall"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "730ef788-95d6-4149-960b-6f2ca9311ea5",
"metadata": {},
"outputs": [],
"source": [
"model_id_order = [\n",
" \"model-garden-lms/bert-base-finewebs-1m\",\n",
" \"model-garden-lms/bert-base-finewebs-951k\",\n",
" \"model-garden-lms/bert-base-finewebs-901k\",\n",
" \"model-garden-lms/bert-base-finewebs-851k\",\n",
" \"model-garden-lms/bert-base-finewebs-801k\",\n",
" \"model-garden-lms/bert-base-token-dropping-finewebs-1m\",\n",
" \"model-garden-lms/bert-base-token-dropping-finewebs-951k\",\n",
" \"model-garden-lms/bert-base-token-dropping-finewebs-901k\",\n",
" \"model-garden-lms/bert-base-token-dropping-finewebs-851k\",\n",
" \"model-garden-lms/bert-base-token-dropping-finewebs-801k\",\n",
" \"model-garden-lms/teams-base-finewebs-1m\",\n",
" \"model-garden-lms/teams-base-finewebs-951k\",\n",
" \"model-garden-lms/teams-base-finewebs-901k\",\n",
" \"model-garden-lms/teams-base-finewebs-851k\",\n",
" \"model-garden-lms/teams-base-finewebs-801k\",\n",
" \"google-bert/bert-base-cased\",\n",
" \"google/electra-base-discriminator\",\n",
" \"FacebookAI/roberta-base\",\n",
"]\n",
"\n",
"dataset_order = [\"score\", \"conll-en\", \"sst5\", \"scala-en\", \"squad\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "af8ee3a3-7798-4bed-8b45-c5e7ba89d9ac",
"metadata": {},
"outputs": [],
"source": [
"headers = [\"Model ID\", \"Avg. Score\", \"CoNLL-En\", \"SST5\", \"ScaLA-En\", \"SQuAD\"]\n",
"\n",
"table = []\n",
"\n",
"for model_id in model_id_order:\n",
" current_row = []\n",
" \n",
" model_id_markdown = f\"[{model_id}](https://huggingface.co/{model_id})\"\n",
" current_row.append(model_id_markdown)\n",
"\n",
" for dataset in dataset_order:\n",
" current_row.append(model_id_results_mapping[model_id][dataset])\n",
"\n",
" table.append(current_row)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "24cafd52-5e5f-44b9-8a9d-1ce04991473f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Model ID | Avg. Score | CoNLL-En | SST5 | ScaLA-En | SQuAD |\n",
"|-------------------------------------------------------------------------------------------------------------------------------------------|--------------|-----------------------------|-----------------------------|-----------------------------|-----------------------------|\n",
"| [model-garden-lms/bert-base-finewebs-1m](https://huggingface.co/model-garden-lms/bert-base-finewebs-1m) | 69.03 | 88.98 ± 0.43 / 88.67 ± 0.36 | 58.11 ± 1.2 / 59.77 ± 1.49 | 57.29 ± 3.57 / 77.15 ± 2.17 | 55.82 ± 1.35 / 66.46 ± 1.51 |\n",
"| [model-garden-lms/bert-base-finewebs-951k](https://huggingface.co/model-garden-lms/bert-base-finewebs-951k) | 69.41 | 89.25 ± 0.4 / 88.9 ± 0.37 | 58.17 ± 1.26 / 59.86 ± 1.65 | 58.83 ± 3.46 / 78.22 ± 2.11 | 55.66 ± 1.19 / 66.36 ± 1.42 |\n",
"| [model-garden-lms/bert-base-finewebs-901k](https://huggingface.co/model-garden-lms/bert-base-finewebs-901k) | 69.12 | 89.22 ± 0.69 / 88.97 ± 0.45 | 57.93 ± 1.1 / 59.49 ± 1.44 | 58.66 ± 2.99 / 77.94 ± 1.88 | 55.0 ± 1.05 / 65.75 ± 1.29 |\n",
"| [model-garden-lms/bert-base-finewebs-851k](https://huggingface.co/model-garden-lms/bert-base-finewebs-851k) | 68.76 | 89.29 ± 0.52 / 89.0 ± 0.51 | 57.68 ± 0.97 / 59.01 ± 1.23 | 57.11 ± 3.77 / 77.36 ± 1.97 | 54.79 ± 1.21 / 65.87 ± 1.32 |\n",
"| [model-garden-lms/bert-base-finewebs-801k](https://huggingface.co/model-garden-lms/bert-base-finewebs-801k) | 68.12 | 88.92 ± 0.45 / 88.6 ± 0.44 | 57.64 ± 1.09 / 60.8 ± 1.88 | 54.28 ± 4.83 / 75.48 ± 2.97 | 54.13 ± 1.61 / 65.09 ± 1.65 |\n",
"| [model-garden-lms/bert-base-token-dropping-finewebs-1m](https://huggingface.co/model-garden-lms/bert-base-token-dropping-finewebs-1m) | 67.66 | 88.68 ± 0.76 / 88.47 ± 0.62 | 57.4 ± 1.7 / 59.61 ± 1.6 | 52.72 ± 5.13 / 73.6 ± 4.42 | 55.04 ± 1.54 / 65.72 ± 1.75 |\n",
"| [model-garden-lms/bert-base-token-dropping-finewebs-951k](https://huggingface.co/model-garden-lms/bert-base-token-dropping-finewebs-951k) | 66.87 | 88.81 ± 0.68 / 88.64 ± 0.54 | 57.44 ± 1.39 / 56.85 ± 2.09 | 50.91 ± 5.08 / 72.22 ± 4.2 | 54.63 ± 1.3 / 65.43 ± 1.43 |\n",
"| [model-garden-lms/bert-base-token-dropping-finewebs-901k](https://huggingface.co/model-garden-lms/bert-base-token-dropping-finewebs-901k) | 68.01 | 88.98 ± 0.64 / 88.67 ± 0.55 | 57.79 ± 1.31 / 58.91 ± 1.85 | 54.25 ± 6.3 / 75.73 ± 3.54 | 54.4 ± 0.72 / 65.31 ± 1.01 |\n",
"| [model-garden-lms/bert-base-token-dropping-finewebs-851k](https://huggingface.co/model-garden-lms/bert-base-token-dropping-finewebs-851k) | 67.97 | 88.9 ± 0.7 / 88.81 ± 0.54 | 58.0 ± 1.02 / 58.73 ± 1.8 | 54.04 ± 2.61 / 74.89 ± 2.07 | 54.75 ± 1.08 / 65.66 ± 1.26 |\n",
"| [model-garden-lms/bert-base-token-dropping-finewebs-801k](https://huggingface.co/model-garden-lms/bert-base-token-dropping-finewebs-801k) | 67.8 | 88.95 ± 0.7 / 88.73 ± 0.58 | 57.71 ± 1.43 / 60.5 ± 1.69 | 50.95 ± 6.3 / 74.16 ± 3.2 | 55.24 ± 1.37 / 66.13 ± 1.24 |\n",
"| [model-garden-lms/teams-base-finewebs-1m](https://huggingface.co/model-garden-lms/teams-base-finewebs-1m) | 72.64 | 89.27 ± 0.41 / 88.82 ± 0.41 | 59.58 ± 0.64 / 62.63 ± 3.0 | 66.72 ± 0.94 / 83.01 ± 0.45 | 59.95 ± 0.71 / 71.13 ± 0.58 |\n",
"| [model-garden-lms/teams-base-finewebs-951k](https://huggingface.co/model-garden-lms/teams-base-finewebs-951k) | 72.06 | 89.64 ± 0.52 / 89.18 ± 0.42 | 60.31 ± 1.03 / 58.82 ± 2.79 | 65.85 ± 2.01 / 82.47 ± 1.23 | 59.36 ± 0.77 / 70.82 ± 0.62 |\n",
"| [model-garden-lms/teams-base-finewebs-901k](https://huggingface.co/model-garden-lms/teams-base-finewebs-901k) | 72.19 | 89.31 ± 0.52 / 88.71 ± 0.53 | 59.86 ± 1.05 / 62.17 ± 2.61 | 64.89 ± 2.86 / 81.84 ± 1.65 | 59.74 ± 0.55 / 71.0 ± 0.5 |\n",
"| [model-garden-lms/teams-base-finewebs-851k](https://huggingface.co/model-garden-lms/teams-base-finewebs-851k) | 71.41 | 89.48 ± 0.47 / 88.99 ± 0.52 | 59.17 ± 1.2 / 60.25 ± 3.25 | 63.01 ± 2.31 / 80.77 ± 1.38 | 59.13 ± 0.53 / 70.5 ± 0.49 |\n",
"| [model-garden-lms/teams-base-finewebs-801k](https://huggingface.co/model-garden-lms/teams-base-finewebs-801k) | 70.73 | 89.2 ± 0.43 / 88.8 ± 0.46 | 59.21 ± 1.5 / 61.41 ± 2.36 | 58.47 ± 4.1 / 78.24 ± 2.4 | 59.59 ± 0.66 / 70.9 ± 0.59 |\n",
"| [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) | 62.26 | 87.39 ± 0.79 / 87.11 ± 0.66 | 54.49 ± 1.36 / 53.22 ± 1.15 | 52.08 ± 2.13 / 74.52 ± 1.31 | 38.63 ± 2.1 / 50.68 ± 1.87 |\n",
"| [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) | 69.26 | 87.82 ± 0.69 / 86.83 ± 0.62 | 62.3 ± 1.12 / 55.93 ± 0.67 | 62.61 ± 1.21 / 80.85 ± 0.59 | 52.51 ± 0.86 / 65.2 ± 0.85 |\n",
"| [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) | 68.96 | 90.35 ± 0.23 / 90.14 ± 0.2 | 60.95 ± 1.4 / 57.52 ± 1.97 | 50.64 ± 1.69 / 74.55 ± 0.9 | 57.82 ± 1.35 / 69.68 ± 1.02 |\n"
]
}
],
"source": [
"print(tabulate(table, headers=headers, tablefmt=\"github\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|