Datasets:

kamel-usp
/

jbcs2025_experiments_report

@@ -1,409 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2e1f00d3-5184-41ad-b263-2731bd6907f0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from Utils import calcular_resultados"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d681e1a6-c967-4249-a70d-c6e412175824",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataset = pd.read_csv('dataset//Teste_JBCS25.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d7b27a05-0583-4911-9174-ddfe0583446d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def selecionar_index(dataset):\n",
-    "    index_a = []\n",
-    "    index_b = []\n",
-    "    for index, row in dataset.iterrows():\n",
-    "        if row['reference'] == 'grader_a':\n",
-    "            index_a.append(index)\n",
-    "        elif row['reference'] == 'grader_b':\n",
-    "            index_b.append(index)\n",
-    "        else:\n",
-    "            print(\"Erro\")\n",
-    "    assert len(index_a) == len(index_b)\n",
-    "    return index_a, index_b\n",
-    "index_a, index_b = selecionar_index(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5729f104-bce9-454e-b348-d6590c52fc44",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0, 1, 5, 7, 8, 11, 12, 14, 15, 17, 19, 21, 23, 25, 26, 27, 29, 33, 38, 39, 43, 44, 45, 46, 48, 49, 52, 53, 56, 57, 58, 59, 61, 62, 65, 67, 69, 70, 73, 75, 79, 82, 86, 91, 92, 94, 96, 98, 99, 100, 101, 102, 103, 105, 108, 113, 114, 117, 118, 119, 122, 123, 125, 126, 128, 133, 135, 136, 137]\n",
-      "[2, 3, 4, 6, 9, 10, 13, 16, 18, 20, 22, 24, 28, 30, 31, 32, 34, 35, 36, 37, 40, 41, 42, 47, 50, 51, 54, 55, 60, 63, 64, 66, 68, 71, 72, 74, 76, 77, 78, 80, 81, 83, 84, 85, 87, 88, 89, 90, 93, 95, 97, 104, 106, 107, 109, 110, 111, 112, 115, 116, 120, 121, 124, 127, 129, 130, 131, 132, 134]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(index_a)\n",
-    "print(index_b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4df02146-061a-4594-8470-b582385aedbf",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a3fcc7d5-29ff-4c8f-8542-c58c114a233e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "e100fb4a-2b3e-47a1-a01c-14e05a8e8ef8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def quebrar_respostas(y, y_hat, index_a, index_b):\n",
-    "    y_hat_a = []\n",
-    "    y_hat_b = []\n",
-    "    y_a, y_b = [], []\n",
-    "    #print(y)\n",
-    "    for idx in range(len(y)):\n",
-    "        if idx in index_a:\n",
-    "            y_a.append( y[idx] )\n",
-    "            y_hat_a.append( y_hat[idx] )\n",
-    "        elif idx in index_b:\n",
-    "            y_b.append( y[idx] )\n",
-    "            y_hat_b.append( y_hat[idx] )\n",
-    "        elif (idx in index_a) and (idx in index_b):\n",
-    "            print(\"Erro\")\n",
-    "    return y_a, y_b, y_hat_a, y_hat_b\n",
-    "\n",
-    "def formatar_respostas(dic_a, dic_b, dic_geral):\n",
-    "    F1_m_a = sum(dic_a['F1-Macro'])/len(dic_a['F1-Macro'])\n",
-    "    F1_w_a = sum(dic_a['F1-Weighted'])/len(dic_a['F1-Weighted'])\n",
-    "    QWK_a = sum(dic_a['QWK'])/len(dic_a['QWK'])\n",
-    "    print(f\"A: {F1_m_a:.2f} & {F1_w_a:.2f} & {QWK_a:.2f} &\")\n",
-    "    F1_m_b = sum(dic_b['F1-Macro'])/len(dic_b['F1-Macro'])\n",
-    "    F1_w_b = sum(dic_b['F1-Weighted'])/len(dic_b['F1-Weighted'])\n",
-    "    QWK_b = sum(dic_b['QWK'])/len(dic_b['QWK'])\n",
-    "    print(f\"B: {F1_m_b:.2f} & {F1_w_b:.2f} & {QWK_b:.2f} &\")\n",
-    "    F1_m_geral = sum(dic_geral['F1-Macro'])/len(dic_geral['F1-Macro'])\n",
-    "    F1_w_geral = sum(dic_geral['F1-Weighted'])/len(dic_geral['F1-Weighted'])\n",
-    "    QWK_geral = sum(dic_geral['QWK'])/len(dic_geral['QWK'])\n",
-    "    print(f\"geral: {F1_m_geral:.2f} & {F1_w_geral:.2f} & {QWK_geral:.2f} &\")\n",
-    "    F1_m_media = (F1_m_a + F1_m_b) / 2\n",
-    "    F1_w_media = (F1_w_a + F1_w_b) / 2 \n",
-    "    QWK_media =  (QWK_a + QWK_b) / 2\n",
-    "    print(f\"media: {F1_m_media:.2f} & {F1_w_media:.2f} & {QWK_media:.2f} &\")\n",
-    "\n",
-    "def adicionar_performances(dic_a, dic_b, dic_geral, perf_a, perf_b, perf_geral):\n",
-    "    for key in perf_a:\n",
-    "        perf_a[key].append(dic_a[key])\n",
-    "        perf_b[key].append(dic_b[key])\n",
-    "        perf_geral[key].append(dic_geral[key])\n",
-    "    \n",
-    "def calcular_media(dataset, index_a, index_b):\n",
-    "    perf_a = {'QWK': [], 'F1-Macro': [], 'F1-Weighted': []}\n",
-    "    perf_b = {'QWK': [], 'F1-Macro': [], 'F1-Weighted': []}\n",
-    "    perf_geral = {'QWK': [], 'F1-Macro': [], 'F1-Weighted': []}\n",
-    "    for idx, row in dataset.iterrows():\n",
-    "        y = eval(row['y'])\n",
-    "        y_hat = eval(row['y_hat'])\n",
-    "        y_a, y_b, y_hat_a, y_hat_b = quebrar_respostas(y, y_hat, index_a, index_b)\n",
-    "        dic_a = calcular_resultados(y_a, y_hat_a)\n",
-    "        dic_b = calcular_resultados(y_b, y_hat_b)\n",
-    "        dic_geral = calcular_resultados(y_a + y_b, y_hat_a + y_hat_b)\n",
-    "        adicionar_performances(dic_a, dic_b, dic_geral, perf_a, perf_b, perf_geral)\n",
-    "    formatar_respostas(perf_a, perf_b, perf_geral)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "712a4429-a014-4991-88a1-e69d4cc0441c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nfor comp in range(1, 6):\\n    print(f\"Comp {comp}\")\\n    respostas = pd.read_csv(f\\'RF-{comp}.csv\\')\\n    calcular_media(respostas, index_a, index_b)\\n'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "for comp in range(1, 6):\n",
-    "    print(f\"Comp {comp}\")\n",
-    "    respostas = pd.read_csv(f'RF-{comp}.csv')\n",
-    "    calcular_media(respostas, index_a, index_b)\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "249c4d4b-8e29-43f6-a700-ba20c2b79c07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calcular_media(dataset, index_a, index_b, nome):\n",
-    "    global Y_HAT, NORMALIZACAO\n",
-    "    perf_a = {'QWK': [], 'F1-Macro': [], 'F1-Weighted': []}\n",
-    "    perf_b = {'QWK': [], 'F1-Macro': [], 'F1-Weighted': []}\n",
-    "    perf_geral = {'QWK': [], 'F1-Macro': [], 'F1-Weighted': []}\n",
-    "    y = (dataset['label']*NORMALIZACAO).tolist()\n",
-    "    y_hat = (dataset[Y_HAT]*NORMALIZACAO).tolist()\n",
-    "    y_a, y_b, y_hat_a, y_hat_b = quebrar_respostas(y, y_hat, index_a, index_b)\n",
-    "    dic_a = calcular_resultados(y_a, y_hat_a)\n",
-    "    dic_b = calcular_resultados(y_b, y_hat_b)\n",
-    "    dic_geral = calcular_resultados(y_a + y_b, y_hat_a + y_hat_b)\n",
-    "    adicionar_performances(dic_a, dic_b, dic_geral, perf_a, perf_b, perf_geral)\n",
-    "    formatar_respostas(perf_a, perf_b, perf_geral, nome)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def printar_dic(dic, nome):\n",
-    "    global dic_print_global\n",
-    "    F1_m = sum(dic['F1-Macro'])/len(dic['F1-Macro'])\n",
-    "    F1_w = sum(dic['F1-Weighted'])/len(dic['F1-Weighted'])\n",
-    "    QWK = sum(dic['QWK'])/len(dic['QWK'])\n",
-    "    print_A = f\"{F1_m:.2f} & {F1_w:.2f} & {QWK:.2f} &\"\n",
-    "    print(f\"{nome}: {print_A}\")\n",
-    "    dic_print_global[nome] += print_A\n",
-    "\n",
-    "def fazer_media(dic1, dic2):\n",
-    "    F1_macro = (dic1['F1-Macro'][0]+dic2['F1-Macro'][0])/2\n",
-    "    F1_weighted = (dic1['F1-Weighted'][0]+dic2['F1-Weighted'][0])/2\n",
-    "    #print(dic1['QWK'], dic2['QWK'])\n",
-    "    QWK = (dic1['QWK'][0]+dic2['QWK'][0])/2\n",
-    "    return {'F1-Macro': [F1_macro], 'F1-Weighted': [F1_weighted], 'QWK': [QWK]}\n",
-    "\n",
-    "def formatar_respostas(dic_a, dic_b, dic_geral, nome):\n",
-    "    global dic_print_global\n",
-    "    printar_dic(dic_a, f\"{nome}-A\")\n",
-    "    printar_dic(dic_b, f\"{nome}-B\")\n",
-    "    printar_dic(dic_geral, f\"{nome}-geral\")\n",
-    "    printar_dic(fazer_media(dic_a, dic_b), f\"{nome}-media\")\n",
-    "    \"\"\"\n",
-    "    print(f\"A: {print_A})\n",
-    "    dic_print_global[f\"{nome}-A\"\n",
-    "    F1_m_b = sum(dic_b['F1-Macro'])/len(dic_b['F1-Macro'])\n",
-    "    F1_w_b = sum(dic_b['F1-Weighted'])/len(dic_b['F1-Weighted'])\n",
-    "    QWK_b = sum(dic_b['QWK'])/len(dic_b['QWK'])\n",
-    "    print(f\"B: {F1_m_b:.2f} & {F1_w_b:.2f} & {QWK_b:.2f} &\")\n",
-    "    F1_m_geral = sum(dic_geral['F1-Macro'])/len(dic_geral['F1-Macro'])\n",
-    "    F1_w_geral = sum(dic_geral['F1-Weighted'])/len(dic_geral['F1-Weighted'])\n",
-    "    QWK_geral = sum(dic_geral['QWK'])/len(dic_geral['QWK'])\n",
-    "    print(f\"geral: {F1_m_geral:.2f} & {F1_w_geral:.2f} & {QWK_geral:.2f} &\")\n",
-    "    F1_m_media = (F1_m_a + F1_m_b) / 2\n",
-    "    F1_w_media = (F1_w_a + F1_w_b) / 2 \n",
-    "    QWK_media =  (QWK_a + QWK_b) / 2\n",
-    "    print(f\"media: {F1_m_media:.2f} & {F1_w_media:.2f} & {QWK_media:.2f} &\")\n",
-    "    \"\"\"\n",
-    "\n",
-    "dic_print_global = {}\n",
-    "def gerar_nome(modelo, comp):\n",
-    "    global dic_print_global\n",
-    "    string = \"\"\n",
-    "    if modelo == 'bertimbau-base':\n",
-    "        string = f\"jbcs2025_bertimbau_base-C{comp}-encoder_classification-C{comp}_inference_results.jsonl\"\n",
-    "    elif modelo == 'mbert':\n",
-    "        string = f'jbcs2025_mbert_base-C{comp}-encoder_classification-C{comp}_inference_results.jsonl'\n",
-    "    elif modelo == 'bertimbau-large':\n",
-    "        string = f\"jbcs2025_bertimbau-large-C{comp}-encoder_classification-C{comp}_inference_results.jsonl\"\n",
-    "    elif modelo == \"Llama-essay_only\":\n",
-    "        string = f\"jbcs2025_llama31_8b-balanced-C{comp}-llama31_classification_lora-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == \"Llama-full_context\":\n",
-    "        string = f\"jbcs2025_Llama-3.1-8B-llama31_classification_lora-C{comp}-full_context-llama31_classification_lora-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == \"Phi4-essay_only\":\n",
-    "        string = f\"jbcs2025_phi4-balanced-C{comp}-phi4_classification_lora-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == \"Phi4-full_context\":\n",
-    "        string = f\"jbcs2025_phi-4-phi4_classification_lora-C{comp}-full_context-phi4_classification_lora-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == \"Phi3.5-essay-only\":\n",
-    "        string = f\"jbcs2025_phi35-balanced-C{comp}-phi35_classification_lora-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == \"Phi3.5-full_context\":\n",
-    "        string = f\"jbcs2025_Phi-3.5-mini-instruct-phi35_classification_lora-C{comp}-full_context-phi35_classification_lora-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    #sabias\n",
-    "    elif modelo == \"sabia-essay-only\":\n",
-    "        string = f\"sabia-3-zero-shot-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == \"sabia-full\":\n",
-    "        string = f\"sabia-3-zero-shot-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == \"sabia-grader-full\":\n",
-    "        string = f\"sabia-3-grader-zero-shot-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == \"sabia-grader-essay-only\":\n",
-    "        string = f\"sabia-3-grader-zero-shot-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == \"sabia-extractor-full\":\n",
-    "        string = f\"sabia-3-extractor-zero-shot-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    #gpts\n",
-    "    elif modelo == 'gpt4-essay-only':\n",
-    "        string = f\"gpt-4o-2024-11-20-zero-shot-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == 'gpt4-full':\n",
-    "        string = f\"gpt-4o-2024-11-20-zero-shot-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == 'gpt4-grader-full':\n",
-    "        string = f\"gpt-4o-2024-11-20-zero-shot-grader-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == 'gpt4-grader-essay-only':\n",
-    "        string = f\"gpt-4o-2024-11-20-grader-zero-shot-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    #deepseeks\n",
-    "    elif modelo == 'deepseek-essay-only':\n",
-    "        string = f\"deepseek-reasoner-zero-shot-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == 'deepseek-full':\n",
-    "        string = f\"deepseek-reasoner-zero-shot-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == 'deepseek-grader-essay-only':\n",
-    "        string = f\"deepseek-reasoner-grader-zero-shot-C{comp}-essay_only_inference_results.jsonl\"\n",
-    "    elif modelo == \"deepseek-grader-full-context\":\n",
-    "        string = f\"deepseek-reasoner-grader-zero-shot-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    elif modelo == \"deepseek-extractor-full-context\":\n",
-    "        string = f\"deepseek-reasoner-extractor-zero-shot-C{comp}-full_context_inference_results.jsonl\"\n",
-    "    if comp == 1:\n",
-    "        dic_print_global[f\"{modelo}-A\"] = \"\"\n",
-    "        dic_print_global[f\"{modelo}-B\"] = \"\"\n",
-    "        dic_print_global[f\"{modelo}-geral\"] = \"\"\n",
-    "        dic_print_global[f\"{modelo}-media\"] = \"\"\n",
-    "    return string"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "cebff6ca-2b47-4960-a155-11da9342d707",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sabia-extractor-full-A: 0.09 & 0.24 & 0.29 &\n",
-      "sabia-extractor-full-B: 0.11 & 0.28 & 0.33 &\n",
-      "sabia-extractor-full-geral: 0.09 & 0.26 & 0.31 &\n",
-      "sabia-extractor-full-media: 0.10 & 0.26 & 0.31 &\n",
-      "sabia-extractor-full-A: 0.21 & 0.34 & 0.29 &\n",
-      "sabia-extractor-full-B: 0.12 & 0.14 & 0.27 &\n",
-      "sabia-extractor-full-geral: 0.15 & 0.21 & 0.28 &\n",
-      "sabia-extractor-full-media: 0.16 & 0.24 & 0.28 &\n",
-      "sabia-extractor-full-A: 0.20 & 0.37 & 0.24 &\n",
-      "sabia-extractor-full-B: 0.20 & 0.21 & 0.19 &\n",
-      "sabia-extractor-full-geral: 0.19 & 0.27 & 0.21 &\n",
-      "sabia-extractor-full-media: 0.20 & 0.29 & 0.21 &\n",
-      "sabia-extractor-full-A: 0.19 & 0.42 & 0.35 &\n",
-      "sabia-extractor-full-B: 0.18 & 0.35 & 0.40 &\n",
-      "sabia-extractor-full-geral: 0.17 & 0.38 & 0.38 &\n",
-      "sabia-extractor-full-media: 0.18 & 0.38 & 0.38 &\n",
-      "sabia-extractor-full-A: 0.22 & 0.25 & 0.46 &\n",
-      "sabia-extractor-full-B: 0.21 & 0.23 & 0.56 &\n",
-      "sabia-extractor-full-geral: 0.23 & 0.23 & 0.52 &\n",
-      "sabia-extractor-full-media: 0.22 & 0.24 & 0.51 &\n"
-     ]
-    }
-   ],
-   "source": [
-    "#print(f\"Comp C1\")\n",
-    "modelo = 'sabia-extractor-full'\n",
-    "Y_HAT = \"pontuacao\"\n",
-    "NORMALIZACAO = 1\n",
-    "for comp in range(1, 6):\n",
-    "    #print(comp)\n",
-    "    nome = gerar_nome(modelo, comp)\n",
-    "    respostas = pd.read_json(nome, lines=True)\n",
-    "    #print(respostas.head())\n",
-    "    calcular_media(respostas, index_a, index_b, modelo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "3fa030a0-ea8f-4315-a153-6b4f5dfff3b8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sabia-extractor-full-A & 0.09 & 0.24 & 0.29 &0.21 & 0.34 & 0.29 &0.20 & 0.37 & 0.24 &0.19 & 0.42 & 0.35 &0.22 & 0.25 & 0.46 \\\\\n",
-      "sabia-extractor-full-B & 0.11 & 0.28 & 0.33 &0.12 & 0.14 & 0.27 &0.20 & 0.21 & 0.19 &0.18 & 0.35 & 0.40 &0.21 & 0.23 & 0.56 \\\\\n",
-      "sabia-extractor-full-geral & 0.09 & 0.26 & 0.31 &0.15 & 0.21 & 0.28 &0.19 & 0.27 & 0.21 &0.17 & 0.38 & 0.38 &0.23 & 0.23 & 0.52 \\\\\n",
-      "sabia-extractor-full-media & 0.10 & 0.26 & 0.31 &0.16 & 0.24 & 0.28 &0.20 & 0.29 & 0.21 &0.18 & 0.38 & 0.38 &0.22 & 0.24 & 0.51 \\\\\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key in dic_print_global:\n",
-    "    print(f\"{key} & {dic_print_global[key][:-2] } \\\\\\\\\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a60f717-93de-4b80-907a-d1ae06f7db63",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.9.19"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

CriaTabelaLatex-CI.ipynb DELETED Viewed

@@ -1,235 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dQAr2gM1_wFl"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "df = pd.read_parquet('evaluation_results-00000-of-00001.parquet', engine='pyarrow')\n",
-        "df1 = pd.read_parquet('boostrap_confidence_intervals-00000-of-00001.parquet', engine='pyarrow')"
-      ],
-      "metadata": {
-        "id": "Zlng_K58AFsV"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def filtrar_respostas(df, modelo):\n",
-        "  filtro = df[df['id'].str.startswith(modelo)]\n",
-        "  dic = {'C1': \"\", 'C2': \"\", 'C3': \"\", 'C4': \"\", 'C5': \"\"}\n",
-        "  for idx, row in filtro.iterrows():\n",
-        "    for key in dic:\n",
-        "      if key in row['id']:\n",
-        "        print_string = f\"{row['Micro_F1']:.2f} & {row['Weighted_F1']:.2f} & {row['QWK']:.2f} &\"\n",
-        "        dic[key] += print_string\n",
-        "  return dic\n",
-        "\n",
-        "# def printar_final(dic, modelo):\n",
-        "#   string = f\"{modelo} \"\n",
-        "#   for key in dic:\n",
-        "#     string += f\"& {dic[key]} \"\n",
-        "#   print(f\"{string[:-2]} \\\\\\\\\")\n",
-        "\n",
-        "def filtrar_dfs(perf, boot, modelo, final=\"\"):\n",
-        "  global SELECT\n",
-        "  filtro_perf = perf[perf['id'].str.startswith(modelo) & perf['id'].str.endswith(final)]\n",
-        "  filtro_boot = boot[boot['experiment_id'].str.startswith(modelo) & boot['experiment_id'].str.endswith(final)]\n",
-        "  dic = {'C1': {}, 'C2': {}, 'C3': {}, 'C4': {}, 'C5': {}}\n",
-        "  string = \"\"\n",
-        "  for idx, row in filtro_perf.iterrows():\n",
-        "    for key in dic:\n",
-        "      if key in row['id']:\n",
-        "        dic[key]['Micro_F1'] = row['Micro_F1']\n",
-        "        dic[key]['Weighted_F1'] = row['Weighted_F1']\n",
-        "        dic[key]['QWK'] = row['QWK']\n",
-        "  for idx, row in filtro_boot.iterrows():\n",
-        "    for key in dic:\n",
-        "      if key in row['experiment_id']:\n",
-        "          dic[key]['QWK_mean'] = row['QWK_mean']\n",
-        "          dic[key][SELECT[1]] = row[SELECT[1]]\n",
-        "          dic[key][SELECT[0]] = row[SELECT[0]]\n",
-        "  #print(dic)\n",
-        "  string = \"\"\n",
-        "  for key in dic:\n",
-        "    string += f\"{dic[key][SELECT[0]]:.2f} & {dic[key][SELECT[1]]:.2f} &\"\n",
-        "  print(f\"{modelo}-{final} & {string[:-2]} \\\\\\\\\")\n",
-        "\n",
-        "modelos = [\n",
-        "    #Encoders\n",
-        "    (\"jbcs2025_mbert_base\", \"\"), (\"jbcs2025_bertimbau_base-\", \"\"), (\"jbcs2025_bertimbau-large\", \"\"),\n",
-        "    #Decoders\n",
-        "  #(\"jbcs2025_llama31_8b-balanced\", \"essay_only\"), (\"jbcs2025_phi35-balanced\", \"essay_only\"), (\"jbcs2025_phi4-balanced\", \"essay_only\"),\n",
-        "  # (\"jbcs2025_Llama-3.1-8B-llama31\", \"full_context\"), (\"jbcs2025_Phi-3.5-mini-instruct\", \"full_context\"), (\"jbcs2025_phi-4-phi4\", \"full_context\"),\n",
-        "    #Sabias\n",
-        "   (\"sabia-3-zero-shot\", \"essay_only\"), (\"sabia-3-zero-shot\", \"full_context\"),\n",
-        "    #(\"sabia-3-grader-zero-shot\", \"essay_only\"),\n",
-        "     (\"sabia-3-grader-zero-shot\", \"full_context\"),\n",
-        "    #Gpts\n",
-        "    (\"gpt-4o-2024-11-20-zero-shot\", \"essay_only\"), (\"gpt-4o-2024-11-20-zero-shot\", \"full_context\"),\n",
-        "    (\"gpt-4o-2024-11-20-grader-zero-shot\", \"essay_only\"), (\"gpt-4o-2024-11-20-grader-zero-shot\", \"full_context\"),\n",
-        "    #Deepseeks\n",
-        "    (\"deepseek-reasoner-zero-shot-\", \"essay_only\"), (\"deepseek-reasoner-zero-shot-\", \"full_context\"),\n",
-        "    (\"Deepseek-reasoner-grader-zero-shot-\", \"essay_only\")       ]\n",
-        "SELECT = [\"QWK_lower_95ci\", \"QWK_upper_95ci\"]\n",
-        "for (modelo, final) in modelos:\n",
-        "  filtrar_dfs(df, df1, modelo, final=final)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2_4fdVNNARGi",
-        "outputId": "6e6b6032-97e7-495d-993c-9f5c8a8f49d3"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "jbcs2025_mbert_base- & 0.33 & 0.56 &0.00 & 0.29 &0.10 & 0.42 &0.12 & 0.42 &0.46 & 0.67 \\\\\n",
-            "jbcs2025_bertimbau_base-- & 0.58 & 0.76 &0.28 & 0.55 &0.21 & 0.48 &0.51 & 0.73 &0.34 & 0.59 \\\\\n",
-            "jbcs2025_bertimbau-large- & 0.61 & 0.79 &0.27 & 0.57 &0.13 & 0.40 &0.46 & 0.67 &0.35 & 0.60 \\\\\n",
-            "sabia-3-zero-shot-essay_only & 0.61 & 0.75 &-0.03 & 0.06 &0.16 & 0.43 &0.39 & 0.62 &0.37 & 0.63 \\\\\n",
-            "sabia-3-zero-shot-full_context & 0.47 & 0.66 &0.31 & 0.60 &0.32 & 0.56 &0.23 & 0.52 &0.40 & 0.65 \\\\\n",
-            "sabia-3-grader-zero-shot-full_context & 0.40 & 0.58 &0.35 & 0.62 &0.42 & 0.65 &0.39 & 0.64 &0.34 & 0.63 \\\\\n",
-            "gpt-4o-2024-11-20-zero-shot-essay_only & 0.43 & 0.58 &0.05 & 0.34 &0.24 & 0.51 &0.40 & 0.60 &0.41 & 0.67 \\\\\n",
-            "gpt-4o-2024-11-20-zero-shot-full_context & 0.39 & 0.56 &0.38 & 0.63 &0.42 & 0.63 &0.38 & 0.58 &0.20 & 0.49 \\\\\n",
-            "gpt-4o-2024-11-20-grader-zero-shot-essay_only & 0.40 & 0.56 &0.15 & 0.42 &0.15 & 0.41 &0.40 & 0.60 &0.41 & 0.66 \\\\\n",
-            "gpt-4o-2024-11-20-grader-zero-shot-full_context & 0.47 & 0.63 &0.43 & 0.68 &0.32 & 0.56 &0.39 & 0.60 &0.33 & 0.61 \\\\\n",
-            "deepseek-reasoner-zero-shot--essay_only & 0.27 & 0.43 &-0.14 & 0.11 &0.23 & 0.53 &0.41 & 0.61 &0.41 & 0.67 \\\\\n",
-            "deepseek-reasoner-zero-shot--full_context & 0.27 & 0.43 &0.30 & 0.53 &0.65 & 0.80 &0.39 & 0.58 &0.45 & 0.69 \\\\\n",
-            "Deepseek-reasoner-grader-zero-shot--essay_only & 0.36 & 0.51 &-0.01 & 0.08 &0.23 & 0.54 &0.37 & 0.56 &0.40 & 0.68 \\\\\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "SELECT = [\"Macro_F1_lower_95ci\", \"Macro_F1_upper_95ci\"]\n",
-        "for (modelo, final) in modelos:\n",
-        "  filtrar_dfs(df, df1, modelo, final=final)"
-      ],
-      "metadata": {
-        "id": "dhyTkpR2ARwp",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "68fd310f-a620-4e32-ea7a-b1855c592f3b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "jbcs2025_mbert_base- & 0.26 & 0.46 &0.16 & 0.33 &0.11 & 0.23 &0.13 & 0.29 &0.25 & 0.40 \\\\\n",
-            "jbcs2025_bertimbau_base-- & 0.36 & 0.62 &0.22 & 0.40 &0.20 & 0.37 &0.29 & 0.59 &0.15 & 0.27 \\\\\n",
-            "jbcs2025_bertimbau-large- & 0.39 & 0.66 &0.21 & 0.37 &0.14 & 0.29 &0.23 & 0.43 &0.23 & 0.41 \\\\\n",
-            "jbcs2025_llama31_8b-balanced-essay_only & 0.37 & 0.64 &0.16 & 0.29 &0.21 & 0.38 &0.27 & 0.52 &0.16 & 0.29 \\\\\n",
-            "jbcs2025_phi35-balanced-essay_only & 0.42 & 0.70 &0.16 & 0.37 &0.20 & 0.38 &0.24 & 0.48 &0.22 & 0.34 \\\\\n",
-            "jbcs2025_phi4-balanced-essay_only & 0.39 & 0.65 &0.23 & 0.39 &0.15 & 0.31 &0.23 & 0.40 &0.14 & 0.25 \\\\\n",
-            "jbcs2025_Llama-3.1-8B-llama31-full_context & 0.35 & 0.56 &0.17 & 0.32 &0.19 & 0.34 &0.23 & 0.46 &0.14 & 0.35 \\\\\n",
-            "jbcs2025_Phi-3.5-mini-instruct-full_context & 0.09 & 0.18 &0.09 & 0.18 &0.33 & 0.56 &0.21 & 0.43 &0.19 & 0.33 \\\\\n",
-            "jbcs2025_phi-4-phi4-full_context & 0.30 & 0.53 &0.18 & 0.31 &0.22 & 0.35 &0.24 & 0.40 &0.25 & 0.45 \\\\\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "zyaRKx5oChUn"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from torch import nn\n",
-        "import torch"
-      ],
-      "metadata": {
-        "id": "cB-6u1yyChfR"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "m = nn.Sigmoid()\n",
-        "input = torch.randn(2)\n",
-        "print(input)\n",
-        "output = m(torch.tensor(-100))\n",
-        "output"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "IJ4ySLhpClfe",
-        "outputId": "b48b901d-fdec-4515-830c-8a1069d2b807"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "tensor([ 1.5602, -1.1152])\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "tensor(0.)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "kvuSSIcCCuOa"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}

README.md CHANGED Viewed

@@ -1,109 +1,109 @@
----
-pretty_name: "JBCS2025: AES Experimental Logs and Predictions"
-license: "cc-by-nc-4.0"
-configs:
-- config_name: evaluation_results
-  data_files:
-  - split: evaluation_results
-    path: evaluation_results-*.parquet
-- config_name: bootstrap_confidence_intervals
-  data_files:
-  - split: bootstrap_confidence_intervals
-    path: bootstrap_confidence_intervals-*.parquet
-tags:
-  - automatic-essay-scoring
-  - portuguese
-  - text-classification
----
-# JBCS 2025: Experimental Artefacts for AES in Brazilian Portuguese
-This repository contains all experimental artefacts (logs, configurations, predictions, and evaluation results) described in the paper:
-> **Exploring the Usage of LLMs for Automatic Essay Scoring in Brazilian Portuguese Essays**
-> André Barbosa, Igor Cataneo Silveira, Denis Deratani Mauá
-> TODO
----
-## 📦 What's in this dataset repo?
-This dataset is **not a training dataset**. Instead, it provides comprehensive logs and outputs from experiments evaluating different language models for Automatic Essay Scoring (AES) tasks in Brazilian Portuguese.
-Specifically, it contains:
-- 🔁 **JSONL files**: raw predictions from each evaluated model.
-- 📊 **CSV files**: detailed performance metrics (Quadratic Weighted Kappa, F1-score, etc.).
-- ⚙️ **YAML files**: complete Hydra configurations for reproducibility.
-- 📋 **Log files**: logs detailing each evaluation run.
----
-## 📚 Related Collection
-All models and datasets related to this work are available in the Hugging Face collection:
-🔗 [**AES JBCS2025 Collection**](https://huggingface.co/collections/kamel-usp/jbcs2025-67d5e73a4b89c1f0c878159c)
----
-## 📊 Evaluated Models
-The table below lists all models trained and evaluated for each essay competence (C1 to C5), along with direct links to their Hugging Face repository pages:
-| Model | Architecture | Training Type | Link |
-|-------|--------------|---------------|------|
-| mbert_base-C1 | Encoder-only | Fine-tuned | [mbert_base-C1](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C1) |
-| mbert_base-C2 | Encoder-only | Fine-tuned | [mbert_base-C2](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C2) |
-| mbert_base-C3 | Encoder-only | Fine-tuned | [mbert_base-C3](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C3) |
-| mbert_base-C4 | Encoder-only | Fine-tuned | [mbert_base-C4](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C4) |
-| mbert_base-C5 | Encoder-only | Fine-tuned | [mbert_base-C5](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C5) |
-| bertimbau_base-C1 | Encoder-only | Fine-tuned | [bertimbau_base-C1](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C1) |
-| bertimbau_base-C2 | Encoder-only | Fine-tuned | [bertimbau_base-C2](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C2) |
-| bertimbau_base-C3 | Encoder-only | Fine-tuned | [bertimbau_base-C3](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C3) |
-| bertimbau_base-C4 | Encoder-only | Fine-tuned | [bertimbau_base-C4](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C4) |
-| bertimbau_base-C5 | Encoder-only | Fine-tuned | [bertimbau_base-C5](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C5) |
-| bertimbau_large-C1 | Encoder-only | Fine-tuned | [bertimbau_large-C1](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C1) |
-| bertimbau_large-C2 | Encoder-only | Fine-tuned | [bertimbau_large-C2](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C2) |
-| bertimbau_large-C3 | Encoder-only | Fine-tuned | [bertimbau_large-C3](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C3) |
-| bertimbau_large-C4 | Encoder-only | Fine-tuned | [bertimbau_large-C4](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C4) |
-| bertimbau_large-C5 | Encoder-only | Fine-tuned | [bertimbau_large-C5](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C5) |
-| llama3-8b-C1 | Decoder-only | LoRA | [llama3-8b-C1](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C1) |
-| llama3-8b-C2 | Decoder-only | LoRA | [llama3-8b-C2](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C2) |
-| llama3-8b-C3 | Decoder-only | LoRA | [llama3-8b-C3](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C3) |
-| llama3-8b-C4 | Decoder-only | LoRA | [llama3-8b-C4](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C4) |
-| llama3-8b-C5 | Decoder-only | LoRA | [llama3-8b-C5](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C5) |
-| phi3.5-C1 | Decoder-only | LoRA | [phi3.5-C1](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C1) |
-| phi3.5-C2 | Decoder-only | LoRA | [phi3.5-C2](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C2) |
-| phi3.5-C3 | Decoder-only | LoRA | [phi3.5-C3](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C3) |
-| phi3.5-C4 | Decoder-only | LoRA | [phi3.5-C4](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C4) |
-| phi3.5-C5 | Decoder-only | LoRA | [phi3.5-C5](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C5) |
-| phi4-C1 | Decoder-only | LoRA | [phi4-C1](https://huggingface.co/kamel-usp/jbcs2025_phi4-C1) |
-| phi4-C2 | Decoder-only | LoRA | [phi4-C2](https://huggingface.co/kamel-usp/jbcs2025_phi4-C2) |
-| phi4-C3 | Decoder-only | LoRA | [phi4-C3](https://huggingface.co/kamel-usp/jbcs2025_phi4-C3) |
-| phi4-C4 | Decoder-only | LoRA | [phi4-C4](https://huggingface.co/kamel-usp/jbcs2025_phi4-C4) |
-| phi4-C5 | Decoder-only | LoRA | [phi4-C5](https://huggingface.co/kamel-usp/jbcs2025_phi4-C5) |
-🧠 Additionally, **API-only models** (e.g., DeepSeek-R1, ChatGPT-4o, Sabiá-3) were evaluated but are not hosted on the Hub. Their predictions and logs are still included in this dataset.
----
-## 🧪 How to Use this Dataset
-You can easily load the data using Hugging Face datasets library:
-```python
-from datasets import load_dataset
-ds = load_dataset("kamel-usp/jbcs2025_experiments", split="runs")
-```
----
-## 📄 License and Citation
-This work is licensed under the [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/).
-If you use these artefacts, please cite our paper:
-```bibtex
-TODO
-```

+---
+pretty_name: "JBCS2025: AES Experimental Logs and Predictions"
+license: "cc-by-nc-4.0"
+configs:
+- config_name: evaluation_results
+  data_files:
+  - split: evaluation_results
+    path: evaluation_results-*.parquet
+- config_name: bootstrap_confidence_intervals
+  data_files:
+  - split: boostrap_confidence_intervals
+    path: boostrap_confidence_intervals-*.parquet
+tags:
+  - automatic-essay-scoring
+  - portuguese
+  - text-classification
+---
+# JBCS 2025: Experimental Artefacts for AES in Brazilian Portuguese
+This repository contains all experimental artefacts (logs, configurations, predictions, and evaluation results) described in the paper:
+> **Exploring the Usage of LLMs for Automatic Essay Scoring in Brazilian Portuguese Essays**
+> André Barbosa, Igor Cataneo Silveira, Denis Deratani Mauá
+> TODO
+---
+## 📦 What's in this dataset repo?
+This dataset is **not a training dataset**. Instead, it provides comprehensive logs and outputs from experiments evaluating different language models for Automatic Essay Scoring (AES) tasks in Brazilian Portuguese.
+Specifically, it contains:
+- 🔁 **JSONL files**: raw predictions from each evaluated model.
+- 📊 **CSV files**: detailed performance metrics (Quadratic Weighted Kappa, F1-score, etc.).
+- ⚙️ **YAML files**: complete Hydra configurations for reproducibility.
+- 📋 **Log files**: logs detailing each evaluation run.
+---
+## 📚 Related Collection
+All models and datasets related to this work are available in the Hugging Face collection:
+🔗 [**AES JBCS2025 Collection**](https://huggingface.co/collections/kamel-usp/jbcs2025-67d5e73a4b89c1f0c878159c)
+---
+## 📊 Evaluated Models
+The table below lists all models trained and evaluated for each essay competence (C1 to C5), along with direct links to their Hugging Face repository pages:
+| Model | Architecture | Training Type | Link |
+|-------|--------------|---------------|------|
+| mbert_base-C1 | Encoder-only | Fine-tuned | [mbert_base-C1](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C1) |
+| mbert_base-C2 | Encoder-only | Fine-tuned | [mbert_base-C2](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C2) |
+| mbert_base-C3 | Encoder-only | Fine-tuned | [mbert_base-C3](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C3) |
+| mbert_base-C4 | Encoder-only | Fine-tuned | [mbert_base-C4](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C4) |
+| mbert_base-C5 | Encoder-only | Fine-tuned | [mbert_base-C5](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C5) |
+| bertimbau_base-C1 | Encoder-only | Fine-tuned | [bertimbau_base-C1](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C1) |
+| bertimbau_base-C2 | Encoder-only | Fine-tuned | [bertimbau_base-C2](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C2) |
+| bertimbau_base-C3 | Encoder-only | Fine-tuned | [bertimbau_base-C3](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C3) |
+| bertimbau_base-C4 | Encoder-only | Fine-tuned | [bertimbau_base-C4](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C4) |
+| bertimbau_base-C5 | Encoder-only | Fine-tuned | [bertimbau_base-C5](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C5) |
+| bertimbau_large-C1 | Encoder-only | Fine-tuned | [bertimbau_large-C1](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C1) |
+| bertimbau_large-C2 | Encoder-only | Fine-tuned | [bertimbau_large-C2](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C2) |
+| bertimbau_large-C3 | Encoder-only | Fine-tuned | [bertimbau_large-C3](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C3) |
+| bertimbau_large-C4 | Encoder-only | Fine-tuned | [bertimbau_large-C4](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C4) |
+| bertimbau_large-C5 | Encoder-only | Fine-tuned | [bertimbau_large-C5](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C5) |
+| llama3-8b-C1 | Decoder-only | LoRA | [llama3-8b-C1](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C1) |
+| llama3-8b-C2 | Decoder-only | LoRA | [llama3-8b-C2](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C2) |
+| llama3-8b-C3 | Decoder-only | LoRA | [llama3-8b-C3](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C3) |
+| llama3-8b-C4 | Decoder-only | LoRA | [llama3-8b-C4](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C4) |
+| llama3-8b-C5 | Decoder-only | LoRA | [llama3-8b-C5](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C5) |
+| phi3.5-C1 | Decoder-only | LoRA | [phi3.5-C1](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C1) |
+| phi3.5-C2 | Decoder-only | LoRA | [phi3.5-C2](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C2) |
+| phi3.5-C3 | Decoder-only | LoRA | [phi3.5-C3](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C3) |
+| phi3.5-C4 | Decoder-only | LoRA | [phi3.5-C4](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C4) |
+| phi3.5-C5 | Decoder-only | LoRA | [phi3.5-C5](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C5) |
+| phi4-C1 | Decoder-only | LoRA | [phi4-C1](https://huggingface.co/kamel-usp/jbcs2025_phi4-C1) |
+| phi4-C2 | Decoder-only | LoRA | [phi4-C2](https://huggingface.co/kamel-usp/jbcs2025_phi4-C2) |
+| phi4-C3 | Decoder-only | LoRA | [phi4-C3](https://huggingface.co/kamel-usp/jbcs2025_phi4-C3) |
+| phi4-C4 | Decoder-only | LoRA | [phi4-C4](https://huggingface.co/kamel-usp/jbcs2025_phi4-C4) |
+| phi4-C5 | Decoder-only | LoRA | [phi4-C5](https://huggingface.co/kamel-usp/jbcs2025_phi4-C5) |
+🧠 Additionally, **API-only models** (e.g., DeepSeek-R1, ChatGPT-4o, Sabiá-3) were evaluated but are not hosted on the Hub. Their predictions and logs are still included in this dataset.
+---
+## 🧪 How to Use this Dataset
+You can easily load the data using Hugging Face datasets library:
+```python
+from datasets import load_dataset
+ds = load_dataset("kamel-usp/jbcs2025_experiments", split="runs")
+```
+---
+## 📄 License and Citation
+This work is licensed under the [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/).
+If you use these artefacts, please cite our paper:
+```bibtex
+TODO
+```

Utils.py DELETED Viewed

@@ -1,57 +0,0 @@
-from sklearn.metrics import accuracy_score, cohen_kappa_score, root_mean_squared_error, f1_score
-import numpy as np
-def arredondar_notas(notas):
-    referencia = [0, 40, 80, 120, 160, 200]
-    novas_notas = []
-    for n in notas:
-        mais_prox = 1000
-        arredondado = -1
-        for r in referencia:
-            if abs(n - r) < mais_prox:
-                arredondado = r
-                mais_prox = abs(n - r)
-        novas_notas.append(arredondado)
-    return novas_notas
-def calcular_div(notas1, notas2):
-    #calcula a divergencia horizontal: duas notas são divergentes se a diferença entre elas é maior que 80
-    div = 0
-    for n1, n2 in zip(notas1,notas2):
-        if abs(n1 - n2) > 80:
-            div += 1
-    return 100*div/len(notas1)
-def calcular_agregado(dic_perf):
-    acc = dic_perf['ACC']*100
-    rmse = (200 - dic_perf['RMSE'])/2
-    qwk = dic_perf['QWK']*100
-    div = 100 - dic_perf['DIV']
-    #print(acc, rmse, qwk, div)
-    return (acc + rmse + qwk + div)/4
-def calcular_resultados(y, y_hat):
-    ALL_LABELS = [0, 40, 80, 120, 160, 200]
-    ACC = accuracy_score(y, y_hat)
-    RMSE = root_mean_squared_error(y, y_hat )
-    QWK = cohen_kappa_score(y, y_hat, weights='quadratic', labels=ALL_LABELS)
-    DIV = calcular_div(y, y_hat)
-    macro_f1 = f1_score(
-        y,
-        y_hat,
-        average="macro",
-        labels=ALL_LABELS,
-        zero_division=np.nan,
-    )
-    weighted_f1 = f1_score(
-        y,
-        y_hat,
-        average="weighted",
-        labels=ALL_LABELS,
-        zero_division=np.nan,
-    )
-    if not isinstance(y, list):
-        y = y.tolist()
-    dic = {'ACC': ACC, 'RMSE': RMSE, 'QWK': QWK, 'DIV': DIV, 'F1-Macro': macro_f1, 'F1-Weighted': weighted_f1, 'y': y, 'y_hat': y_hat}
-    dic['Agregado'] = calcular_agregado(dic)
-    return dic

bootstrap_confidence_intervals-00000-of-00001.parquet → boostrap_confidence_intervals-00000-of-00001.parquet RENAMED Viewed

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bootstrap_confidence_intervals_Macro_F1_table.txt DELETED Viewed

@@ -1,74 +0,0 @@
-\begin{tabular}{c|c c| c c| c c| c c| c c}
-\hline\hline
- & \multicolumn{2}{c|}{\textbf{C1}} & \multicolumn{2}{c|}{\textbf{C2}} & \multicolumn{2}{c|}{\textbf{C3}} & \multicolumn{2}{c|}{\textbf{C4}} & \multicolumn{2}{c|}{\textbf{C5}} \\ \hline
- & F1-Macro$_{\text{-lower-bound}}$ & F1-Macro$_{\text{-upper-bound}}$ & F1-Macro$_{\text{-lower-bound}}$ & F1-Macro$_{\text{-upper-bound}}$ & F1-Macro$_{\text{-lower-bound}}$ & F1-Macro$_{\text{-upper-bound}}$ & F1-Macro$_{\text{-lower-bound}}$ & F1-Macro$_{\text{-upper-bound}}$ & F1-Macro$_{\text{-lower-bound}}$ & F1-Macro$_{\text{-upper-bound}}$ \\ \hline
-albertina-1b5-ptbr-essay-only & 0.41 & 0.69 & 0.08 & 0.20 & 0.16 & 0.31 & 0.23 & 0.50 & 0.10 & 0.21 \\
-\hline
-bertimbau-base-essay-only & 0.30 & 0.54 & 0.18 & 0.36 & 0.16 & 0.31 & 0.25 & 0.52 & 0.24 & 0.38 \\
-\hline
-bertimbau-large-essay-only & 0.37 & 0.65 & 0.25 & 0.44 & 0.12 & 0.26 & 0.30 & 0.64 & 0.23 & 0.38 \\
-\hline
-bertugues-base-essay-only & 0.32 & 0.55 & 0.10 & 0.20 & 0.19 & 0.34 & 0.23 & 0.48 & 0.17 & 0.31 \\
-\hline
-mbert-base-essay-only & 0.29 & 0.50 & 0.14 & 0.26 & 0.17 & 0.32 & 0.23 & 0.50 & 0.05 & 0.11 \\
-\hline\hline
-llama3.1-8b-lora-essay-only-r16 & 0.40 & 0.67 & 0.19 & 0.35 & 0.19 & 0.35 & 0.20 & 0.43 & 0.14 & 0.29 \\
-\hline
-llama3.1-8b-lora-essay-only-r8 & 0.36 & 0.64 & 0.11 & 0.23 & 0.18 & 0.35 & 0.18 & 0.31 & 0.19 & 0.35 \\
-\hline
-llama3.1-8b-lora-full-context-r16 & 0.38 & 0.64 & 0.14 & 0.32 & 0.16 & 0.32 & 0.18 & 0.34 & 0.15 & 0.37 \\
-\hline
-llama3.1-8b-lora-full-context-r8 & 0.30 & 0.53 & 0.15 & 0.31 & 0.17 & 0.34 & 0.26 & 0.55 & 0.11 & 0.33 \\
-\hline
-phi3.5-mini-lora-essay-only-r16 & 0.23 & 0.44 & 0.15 & 0.31 & 0.16 & 0.32 & 0.18 & 0.41 & 0.13 & 0.24 \\
-\hline
-phi3.5-mini-lora-essay-only-r8 & 0.23 & 0.44 & 0.15 & 0.31 & 0.16 & 0.32 & 0.18 & 0.41 & 0.13 & 0.24 \\
-\hline
-phi3.5-mini-lora-full-context-r16 & 0.13 & 0.25 & 0.09 & 0.20 & 0.21 & 0.37 & 0.08 & 0.15 & 0.10 & 0.28 \\
-\hline
-phi3.5-mini-lora-full-context-r8 & 0.13 & 0.25 & 0.09 & 0.20 & 0.21 & 0.37 & 0.08 & 0.15 & 0.10 & 0.28 \\
-\hline
-phi4-lora-essay-only-r16 & 0.40 & 0.67 & 0.14 & 0.29 & 0.18 & 0.33 & 0.25 & 0.52 & 0.30 & 0.45 \\
-\hline
-phi4-lora-essay-only-r8 & 0.37 & 0.64 & 0.21 & 0.39 & 0.18 & 0.32 & 0.19 & 0.42 & 0.36 & 0.51 \\
-\hline
-phi4-lora-full-context-r16 & 0.31 & 0.55 & 0.32 & 0.54 & 0.32 & 0.53 & 0.23 & 0.48 & 0.21 & 0.39 \\
-\hline
-phi4-lora-full-context-r8 & 0.36 & 0.61 & 0.31 & 0.54 & 0.27 & 0.48 & 0.24 & 0.48 & 0.23 & 0.37 \\
-\hline
-tucano2b4-lora-essay-only-r16 & 0.38 & 0.65 & 0.12 & 0.23 & 0.18 & 0.32 & 0.23 & 0.56 & 0.19 & 0.35 \\
-\hline
-tucano2b4-lora-essay-only-r8 & 0.38 & 0.66 & 0.11 & 0.20 & 0.18 & 0.34 & 0.22 & 0.46 & 0.19 & 0.31 \\
-\hline
-tucano2b4-lora-full-context-r16 & 0.31 & 0.51 & 0.13 & 0.28 & 0.15 & 0.30 & 0.27 & 0.52 & 0.19 & 0.31 \\
-\hline
-tucano2b4-lora-full-context-r8 & 0.31 & 0.55 & 0.16 & 0.30 & 0.17 & 0.34 & 0.22 & 0.50 & 0.14 & 0.25 \\
-\hline\hline
-deepseekR1-extractor-essay\_only & 0.03 & 0.14 & 0.00 & 0.03 & 0.19 & 0.37 & 0.23 & 0.46 & 0.28 & 0.42 \\
-deepseekR1-extractor-full\_context & 0.01 & 0.06 & 0.19 & 0.38 & 0.24 & 0.40 & 0.26 & 0.50 & 0.30 & 0.45 \\
-\hline
-deepseekR1-graderPrompt-essay\_only & 0.10 & 0.24 & 0.01 & 0.08 & 0.13 & 0.27 & 0.12 & 0.28 & 0.24 & 0.39 \\
-deepseekR1-graderPrompt-full\_context & 0.01 & 0.12 & 0.25 & 0.47 & 0.21 & 0.41 & 0.20 & 0.41 & 0.28 & 0.43 \\
-\hline
-deepseekR1-studentPrompt-essay\_only & 0.05 & 0.17 & 0.10 & 0.20 & 0.23 & 0.41 & 0.15 & 0.40 & 0.30 & 0.44 \\
-deepseekR1-studentPrompt-full\_context & 0.02 & 0.07 & 0.19 & 0.40 & 0.33 & 0.57 & 0.13 & 0.32 & 0.29 & 0.48 \\
-\hline
-gpt4o-extractor-essay\_only & 0.18 & 0.33 & 0.07 & 0.15 & 0.16 & 0.31 & 0.19 & 0.47 & 0.19 & 0.33 \\
-gpt4o-extractor-full\_context & 0.18 & 0.31 & 0.24 & 0.47 & 0.19 & 0.38 & 0.18 & 0.39 & 0.09 & 0.21 \\
-\hline
-gpt4o-graderPrompt-essay\_only & 0.15 & 0.27 & 0.11 & 0.29 & 0.14 & 0.26 & 0.16 & 0.39 & 0.25 & 0.39 \\
-gpt4o-graderPrompt-full\_context & 0.19 & 0.31 & 0.24 & 0.44 & 0.19 & 0.39 & 0.19 & 0.33 & 0.17 & 0.31 \\
-\hline
-gpt4o-studentPrompt-essay\_only & 0.15 & 0.29 & 0.12 & 0.23 & 0.18 & 0.34 & 0.17 & 0.42 & 0.23 & 0.35 \\
-gpt4o-studentPrompt-full\_context & 0.15 & 0.25 & 0.25 & 0.50 & 0.19 & 0.36 & 0.16 & 0.37 & 0.10 & 0.23 \\
-\hline
-sabia3-extractor-essay\_only & 0.07 & 0.12 & 0.08 & 0.18 & 0.08 & 0.16 & 0.13 & 0.25 & 0.16 & 0.26 \\
-sabia3-extractor-full\_context & 0.08 & 0.12 & 0.11 & 0.22 & 0.14 & 0.27 & 0.13 & 0.25 & 0.16 & 0.28 \\
-\hline
-sabia3-graderPrompt-essay\_only & 0.16 & 0.32 & 0.05 & 0.14 & 0.20 & 0.35 & 0.14 & 0.34 & 0.26 & 0.41 \\
-sabia3-graderPrompt-full\_context & 0.17 & 0.33 & 0.24 & 0.48 & 0.23 & 0.48 & 0.20 & 0.45 & 0.20 & 0.36 \\
-\hline
-sabia3-studentPrompt-essay\_only & 0.26 & 0.45 & 0.05 & 0.12 & 0.16 & 0.31 & 0.19 & 0.43 & 0.23 & 0.36 \\
-sabia3-studentPrompt-full\_context & 0.26 & 0.50 & 0.20 & 0.41 & 0.12 & 0.33 & 0.15 & 0.42 & 0.19 & 0.35 \\
-\hline\hline
-\end{tabular}%

bootstrap_confidence_intervals_QWK_table.txt DELETED Viewed

@@ -1,74 +0,0 @@
-\begin{tabular}{c|c c| c c| c c| c c| c c}
-\hline\hline
- & \multicolumn{2}{c|}{\textbf{C1}} & \multicolumn{2}{c|}{\textbf{C2}} & \multicolumn{2}{c|}{\textbf{C3}} & \multicolumn{2}{c|}{\textbf{C4}} & \multicolumn{2}{c|}{\textbf{C5}} \\ \hline
- & QWK$_{\text{lower-bound}}$ & QWK$_{\text{upper-bound}}$ & QWK$_{\text{lower-bound}}$ & QWK$_{\text{upper-bound}}$ & QWK$_{\text{lower-bound}}$ & QWK$_{\text{upper-bound}}$ & QWK$_{\text{lower-bound}}$ & QWK$_{\text{upper-bound}}$ & QWK$_{\text{lower-bound}}$ & QWK$_{\text{upper-bound}}$ \\ \hline
-albertina-1b5-ptbr-essay-only & 0.58 & 0.78 & 0.09 & 0.39 & 0.09 & 0.40 & 0.42 & 0.65 & -0.15 & 0.21 \\
-\hline
-bertimbau-base-essay-only & 0.51 & 0.68 & 0.19 & 0.49 & 0.20 & 0.49 & 0.42 & 0.66 & 0.51 & 0.73 \\
-\hline
-bertimbau-large-essay-only & 0.59 & 0.77 & 0.14 & 0.47 & 0.09 & 0.39 & 0.47 & 0.71 & 0.33 & 0.58 \\
-\hline
-bertugues-base-essay-only & 0.52 & 0.70 & 0.19 & 0.46 & 0.12 & 0.44 & 0.42 & 0.64 & 0.22 & 0.49 \\
-\hline
-mbert-base-essay-only & 0.39 & 0.62 & 0.07 & 0.37 & 0.21 & 0.50 & 0.33 & 0.64 & -0.04 & 0.04 \\
-\hline\hline
-llama3.1-8b-lora-essay-only-r16 & 0.59 & 0.77 & 0.19 & 0.50 & 0.28 & 0.56 & 0.32 & 0.56 & 0.22 & 0.49 \\
-\hline
-llama3.1-8b-lora-essay-only-r8 & 0.54 & 0.74 & 0.16 & 0.46 & 0.21 & 0.51 & 0.26 & 0.51 & 0.12 & 0.46 \\
-\hline
-llama3.1-8b-lora-full-context-r16 & 0.55 & 0.75 & 0.19 & 0.47 & 0.18 & 0.46 & 0.31 & 0.55 & 0.02 & 0.35 \\
-\hline
-llama3.1-8b-lora-full-context-r8 & 0.49 & 0.69 & 0.08 & 0.40 & 0.17 & 0.46 & 0.41 & 0.64 & 0.01 & 0.33 \\
-\hline
-phi3.5-mini-lora-essay-only-r16 & 0.32 & 0.58 & 0.12 & 0.42 & 0.09 & 0.35 & 0.13 & 0.41 & 0.11 & 0.39 \\
-\hline
-phi3.5-mini-lora-essay-only-r8 & 0.32 & 0.58 & 0.12 & 0.42 & 0.09 & 0.35 & 0.13 & 0.41 & 0.11 & 0.39 \\
-\hline
-phi3.5-mini-lora-full-context-r16 & 0.16 & 0.38 & -0.18 & 0.14 & 0.30 & 0.57 & -0.00 & 0.17 & 0.03 & 0.34 \\
-\hline
-phi3.5-mini-lora-full-context-r8 & 0.16 & 0.38 & -0.18 & 0.14 & 0.30 & 0.57 & -0.00 & 0.17 & 0.03 & 0.34 \\
-\hline
-phi4-lora-essay-only-r16 & 0.60 & 0.78 & 0.06 & 0.36 & 0.27 & 0.55 & 0.47 & 0.68 & 0.54 & 0.75 \\
-\hline
-phi4-lora-essay-only-r8 & 0.57 & 0.75 & 0.18 & 0.47 & 0.17 & 0.48 & 0.35 & 0.60 & 0.46 & 0.70 \\
-\hline
-phi4-lora-full-context-r16 & 0.51 & 0.70 & 0.47 & 0.71 & 0.53 & 0.76 & 0.49 & 0.68 & 0.40 & 0.64 \\
-\hline
-phi4-lora-full-context-r8 & 0.57 & 0.75 & 0.46 & 0.70 & 0.45 & 0.68 & 0.43 & 0.65 & 0.38 & 0.64 \\
-\hline
-tucano2b4-lora-essay-only-r16 & 0.56 & 0.76 & 0.07 & 0.35 & 0.21 & 0.49 & 0.36 & 0.63 & 0.40 & 0.65 \\
-\hline
-tucano2b4-lora-essay-only-r8 & 0.54 & 0.75 & 0.07 & 0.38 & 0.22 & 0.50 & 0.36 & 0.61 & 0.45 & 0.68 \\
-\hline
-tucano2b4-lora-full-context-r16 & 0.43 & 0.65 & 0.07 & 0.39 & 0.07 & 0.39 & 0.39 & 0.64 & 0.28 & 0.56 \\
-\hline
-tucano2b4-lora-full-context-r8 & 0.45 & 0.67 & 0.10 & 0.44 & 0.07 & 0.39 & 0.39 & 0.63 & 0.08 & 0.39 \\
-\hline\hline
-deepseekR1-extractor-essay\_only & 0.15 & 0.27 & -0.02 & 0.00 & 0.27 & 0.53 & 0.42 & 0.62 & 0.49 & 0.73 \\
-deepseekR1-extractor-full\_context & 0.15 & 0.26 & 0.39 & 0.63 & 0.40 & 0.64 & 0.45 & 0.66 & 0.48 & 0.73 \\
-\hline
-deepseekR1-graderPrompt-essay\_only & 0.36 & 0.51 & -0.01 & 0.08 & 0.23 & 0.54 & 0.37 & 0.56 & 0.40 & 0.68 \\
-deepseekR1-graderPrompt-full\_context & 0.14 & 0.26 & 0.39 & 0.63 & 0.49 & 0.69 & 0.45 & 0.64 & 0.46 & 0.72 \\
-\hline
-deepseekR1-studentPrompt-essay\_only & 0.27 & 0.43 & -0.14 & 0.11 & 0.23 & 0.53 & 0.41 & 0.61 & 0.41 & 0.67 \\
-deepseekR1-studentPrompt-full\_context & 0.27 & 0.43 & 0.30 & 0.53 & 0.65 & 0.80 & 0.39 & 0.58 & 0.45 & 0.69 \\
-\hline
-gpt4o-extractor-essay\_only & 0.45 & 0.65 & 0.06 & 0.25 & 0.26 & 0.51 & 0.46 & 0.64 & 0.39 & 0.62 \\
-gpt4o-extractor-full\_context & 0.41 & 0.64 & 0.32 & 0.60 & 0.46 & 0.66 & 0.37 & 0.61 & 0.20 & 0.43 \\
-\hline
-gpt4o-graderPrompt-essay\_only & 0.40 & 0.56 & 0.15 & 0.42 & 0.15 & 0.41 & 0.40 & 0.60 & 0.41 & 0.66 \\
-gpt4o-graderPrompt-full\_context & 0.47 & 0.63 & 0.43 & 0.68 & 0.32 & 0.56 & 0.39 & 0.60 & 0.33 & 0.61 \\
-\hline
-gpt4o-studentPrompt-essay\_only & 0.43 & 0.58 & 0.05 & 0.34 & 0.24 & 0.51 & 0.40 & 0.60 & 0.41 & 0.67 \\
-gpt4o-studentPrompt-full\_context & 0.39 & 0.56 & 0.38 & 0.63 & 0.42 & 0.63 & 0.38 & 0.58 & 0.20 & 0.49 \\
-\hline
-sabia3-extractor-essay\_only & 0.21 & 0.37 & 0.08 & 0.29 & -0.05 & 0.24 & 0.36 & 0.54 & 0.41 & 0.62 \\
-sabia3-extractor-full\_context & 0.22 & 0.39 & 0.18 & 0.38 & 0.09 & 0.33 & 0.28 & 0.47 & 0.39 & 0.62 \\
-\hline
-sabia3-graderPrompt-essay\_only & 0.45 & 0.61 & 0.00 & 0.13 & 0.21 & 0.48 & 0.44 & 0.65 & 0.47 & 0.70 \\
-sabia3-graderPrompt-full\_context & 0.40 & 0.58 & 0.35 & 0.62 & 0.42 & 0.65 & 0.39 & 0.64 & 0.34 & 0.63 \\
-\hline
-sabia3-studentPrompt-essay\_only & 0.61 & 0.75 & -0.03 & 0.06 & 0.16 & 0.43 & 0.39 & 0.62 & 0.37 & 0.63 \\
-sabia3-studentPrompt-full\_context & 0.47 & 0.66 & 0.31 & 0.60 & 0.32 & 0.56 & 0.23 & 0.52 & 0.40 & 0.65 \\
-\hline\hline
-\end{tabular}%

bootstrap_confidence_intervals_Weighted_F1_table.txt DELETED Viewed

@@ -1,74 +0,0 @@
-\begin{tabular}{c|c c| c c| c c| c c| c c}
-\hline\hline
- & \multicolumn{2}{c|}{\textbf{C1}} & \multicolumn{2}{c|}{\textbf{C2}} & \multicolumn{2}{c|}{\textbf{C3}} & \multicolumn{2}{c|}{\textbf{C4}} & \multicolumn{2}{c|}{\textbf{C5}} \\ \hline
- & F1-Weighted$_{\text{-lower-bound}}$ & F1-Weighted$_{\text{-upper-bound}}$ & F1-Weighted$_{\text{-lower-bound}}$ & F1-Weighted$_{\text{-upper-bound}}$ & F1-Weighted$_{\text{-lower-bound}}$ & F1-Weighted$_{\text{-upper-bound}}$ & F1-Weighted$_{\text{-lower-bound}}$ & F1-Weighted$_{\text{-upper-bound}}$ & F1-Weighted$_{\text{-lower-bound}}$ & F1-Weighted$_{\text{-upper-bound}}$ \\ \hline
-albertina-1b5-ptbr-essay-only & 0.64 & 0.79 & 0.09 & 0.22 & 0.23 & 0.39 & 0.51 & 0.68 & 0.11 & 0.24 \\
-\hline
-bertimbau-base-essay-only & 0.43 & 0.60 & 0.27 & 0.43 & 0.19 & 0.35 & 0.47 & 0.64 & 0.27 & 0.45 \\
-\hline
-bertimbau-large-essay-only & 0.61 & 0.77 & 0.33 & 0.49 & 0.18 & 0.33 & 0.52 & 0.68 & 0.27 & 0.45 \\
-\hline
-bertugues-base-essay-only & 0.48 & 0.64 & 0.12 & 0.25 & 0.27 & 0.43 & 0.42 & 0.59 & 0.19 & 0.35 \\
-\hline
-mbert-base-essay-only & 0.50 & 0.68 & 0.18 & 0.34 & 0.24 & 0.40 & 0.47 & 0.63 & 0.05 & 0.14 \\
-\hline\hline
-llama3.1-8b-lora-essay-only-r16 & 0.58 & 0.74 & 0.27 & 0.43 & 0.20 & 0.36 & 0.37 & 0.54 & 0.15 & 0.28 \\
-\hline
-llama3.1-8b-lora-essay-only-r8 & 0.58 & 0.75 & 0.10 & 0.23 & 0.28 & 0.44 & 0.45 & 0.62 & 0.20 & 0.35 \\
-\hline
-llama3.1-8b-lora-full-context-r16 & 0.60 & 0.76 & 0.15 & 0.30 & 0.18 & 0.34 & 0.42 & 0.59 & 0.17 & 0.32 \\
-\hline
-llama3.1-8b-lora-full-context-r8 & 0.49 & 0.65 & 0.18 & 0.34 & 0.20 & 0.36 & 0.47 & 0.63 & 0.12 & 0.26 \\
-\hline
-phi3.5-mini-lora-essay-only-r16 & 0.31 & 0.49 & 0.16 & 0.31 & 0.20 & 0.36 & 0.41 & 0.59 & 0.17 & 0.31 \\
-\hline
-phi3.5-mini-lora-essay-only-r8 & 0.31 & 0.49 & 0.16 & 0.31 & 0.20 & 0.36 & 0.41 & 0.59 & 0.17 & 0.31 \\
-\hline
-phi3.5-mini-lora-full-context-r16 & 0.21 & 0.37 & 0.15 & 0.28 & 0.26 & 0.43 & 0.13 & 0.28 & 0.12 & 0.25 \\
-\hline
-phi3.5-mini-lora-full-context-r8 & 0.21 & 0.37 & 0.15 & 0.28 & 0.26 & 0.43 & 0.13 & 0.28 & 0.12 & 0.25 \\
-\hline
-phi4-lora-essay-only-r16 & 0.58 & 0.73 & 0.16 & 0.31 & 0.26 & 0.42 & 0.55 & 0.72 & 0.34 & 0.51 \\
-\hline
-phi4-lora-essay-only-r8 & 0.53 & 0.69 & 0.28 & 0.45 & 0.23 & 0.39 & 0.49 & 0.67 & 0.40 & 0.57 \\
-\hline
-phi4-lora-full-context-r16 & 0.49 & 0.66 & 0.41 & 0.59 & 0.39 & 0.56 & 0.42 & 0.59 & 0.24 & 0.40 \\
-\hline
-phi4-lora-full-context-r8 & 0.54 & 0.70 & 0.39 & 0.56 & 0.29 & 0.46 & 0.49 & 0.66 & 0.23 & 0.39 \\
-\hline
-tucano2b4-lora-essay-only-r16 & 0.57 & 0.74 & 0.15 & 0.29 & 0.22 & 0.38 & 0.50 & 0.66 & 0.21 & 0.36 \\
-\hline
-tucano2b4-lora-essay-only-r8 & 0.56 & 0.72 & 0.12 & 0.27 & 0.26 & 0.42 & 0.48 & 0.65 & 0.22 & 0.37 \\
-\hline
-tucano2b4-lora-full-context-r16 & 0.50 & 0.67 & 0.18 & 0.33 & 0.19 & 0.34 & 0.51 & 0.67 & 0.21 & 0.37 \\
-\hline
-tucano2b4-lora-full-context-r8 & 0.47 & 0.64 & 0.25 & 0.41 & 0.21 & 0.37 & 0.42 & 0.59 & 0.16 & 0.31 \\
-\hline\hline
-deepseekR1-extractor-essay\_only & 0.06 & 0.19 & 0.00 & 0.04 & 0.24 & 0.40 & 0.44 & 0.61 & 0.30 & 0.47 \\
-deepseekR1-extractor-full\_context & 0.03 & 0.14 & 0.23 & 0.41 & 0.31 & 0.49 & 0.51 & 0.67 & 0.33 & 0.51 \\
-\hline
-deepseekR1-graderPrompt-essay\_only & 0.17 & 0.33 & 0.01 & 0.10 & 0.12 & 0.26 & 0.16 & 0.33 & 0.25 & 0.42 \\
-deepseekR1-graderPrompt-full\_context & 0.01 & 0.11 & 0.31 & 0.50 & 0.22 & 0.37 & 0.37 & 0.54 & 0.28 & 0.46 \\
-\hline
-deepseekR1-studentPrompt-essay\_only & 0.10 & 0.24 & 0.17 & 0.33 & 0.25 & 0.41 & 0.31 & 0.48 & 0.34 & 0.51 \\
-deepseekR1-studentPrompt-full\_context & 0.02 & 0.11 & 0.26 & 0.44 & 0.38 & 0.55 & 0.28 & 0.45 & 0.32 & 0.49 \\
-\hline
-gpt4o-extractor-essay\_only & 0.42 & 0.60 & 0.08 & 0.19 & 0.19 & 0.34 & 0.39 & 0.56 & 0.20 & 0.37 \\
-gpt4o-extractor-full\_context & 0.44 & 0.63 & 0.32 & 0.49 & 0.20 & 0.36 & 0.36 & 0.53 & 0.09 & 0.22 \\
-\hline
-gpt4o-graderPrompt-essay\_only & 0.36 & 0.53 & 0.13 & 0.27 & 0.19 & 0.34 & 0.30 & 0.47 & 0.26 & 0.42 \\
-gpt4o-graderPrompt-full\_context & 0.43 & 0.60 & 0.33 & 0.50 & 0.23 & 0.41 & 0.39 & 0.56 & 0.18 & 0.33 \\
-\hline
-gpt4o-studentPrompt-essay\_only & 0.34 & 0.51 & 0.18 & 0.34 & 0.21 & 0.37 & 0.31 & 0.48 & 0.22 & 0.38 \\
-gpt4o-studentPrompt-full\_context & 0.34 & 0.51 & 0.33 & 0.50 & 0.22 & 0.37 & 0.28 & 0.45 & 0.12 & 0.26 \\
-\hline
-sabia3-extractor-essay\_only & 0.17 & 0.33 & 0.10 & 0.24 & 0.11 & 0.25 & 0.27 & 0.45 & 0.14 & 0.29 \\
-sabia3-extractor-full\_context & 0.18 & 0.34 & 0.14 & 0.29 & 0.19 & 0.36 & 0.29 & 0.47 & 0.15 & 0.31 \\
-\hline
-sabia3-graderPrompt-essay\_only & 0.33 & 0.50 & 0.06 & 0.18 & 0.25 & 0.41 & 0.35 & 0.53 & 0.29 & 0.46 \\
-sabia3-graderPrompt-full\_context & 0.41 & 0.59 & 0.33 & 0.50 & 0.29 & 0.45 & 0.41 & 0.58 & 0.24 & 0.40 \\
-\hline
-sabia3-studentPrompt-essay\_only & 0.58 & 0.74 & 0.06 & 0.17 & 0.22 & 0.38 & 0.43 & 0.62 & 0.26 & 0.43 \\
-sabia3-studentPrompt-full\_context & 0.51 & 0.69 & 0.32 & 0.49 & 0.15 & 0.30 & 0.34 & 0.53 & 0.20 & 0.36 \\
-\hline\hline
-\end{tabular}%

create_latex_tables.py DELETED Viewed

@@ -1,420 +0,0 @@
-# create_latex_table.py  –  parses concept from experiment_id
-# ----------------------------------------------------------
-from __future__ import annotations
-import re
-from pathlib import Path
-from typing import Dict, List, Tuple, Optional
-from dataclasses import dataclass
-import pandas as pd            # needs pyarrow OR fastparquet
-@dataclass
-class TableConfig:
-    """Configuration for LaTeX table generation."""
-    concepts: List[str]
-    metric_map: Dict[str, str]  # friendly_name -> column_name
-    latex_labels: Dict[str, str]  # friendly_name -> latex_label
-    @classmethod
-    def evaluation_results(cls) -> TableConfig:
-        """Create evaluation results configuration."""
-        return cls(
-            concepts=[f"C{i}" for i in range(1, 6)],  # C1 through C5
-            metric_map={
-                "F1_ma": "Macro_F1",
-                "F1_w": "Weighted_F1",
-                "QWK": "QWK",
-            },
-            latex_labels={
-                "F1_ma": r"F1$_{ma}$",
-                "F1_w": r"F1$_{w}$",
-                "QWK": r"QWK",
-            }
-        )
-    @classmethod
-    def bootstrap(cls) -> TableConfig:
-        """Create configuration for bootstrap confidence intervals."""
-        return cls(
-            concepts=[f"C{i}" for i in range(1, 6)],  # C1 through C5
-            metric_map={
-                "QWK_lower": "QWK_lower_95ci",
-                "QWK_upper": "QWK_upper_95ci",
-            },
-            latex_labels={
-                "QWK_lower": r"QWK$_{\text{lower-bound}}$",
-                "QWK_upper": r"QWK$_{\text{upper-bound}}$",
-            }
-        )
-    @classmethod
-    def bootstrap_qwk(cls) -> TableConfig:
-        """Create configuration for QWK bootstrap confidence intervals."""
-        return cls.bootstrap()  # Alias for backward compatibility
-    @classmethod
-    def bootstrap_macro_f1(cls) -> TableConfig:
-        """Create configuration for Macro F1 bootstrap confidence intervals."""
-        return cls(
-            concepts=[f"C{i}" for i in range(1, 6)],  # C1 through C5
-            metric_map={
-                "F1_ma_lower": "Macro_F1_lower_95ci",
-                "F1_ma_upper": "Macro_F1_upper_95ci",
-            },
-            latex_labels={
-                "F1_ma_lower": r"F1-Macro$_{\text{-lower-bound}}$",
-                "F1_ma_upper": r"F1-Macro$_{\text{-upper-bound}}$",
-            }
-        )
-    @classmethod
-    def bootstrap_weighted_f1(cls) -> TableConfig:
-        """Create configuration for Weighted F1 bootstrap confidence intervals."""
-        return cls(
-            concepts=[f"C{i}" for i in range(1, 6)],  # C1 through C5
-            metric_map={
-                "F1_w_lower": "Weighted_F1_lower_95ci",
-                "F1_w_upper": "Weighted_F1_upper_95ci",
-            },
-            latex_labels={
-                "F1_w_lower": r"F1-Weighted$_{\text{-lower-bound}}$",
-                "F1_w_upper": r"F1-Weighted$_{\text{-upper-bound}}$",
-            }
-        )
-class ExperimentIdParser:
-    """Handles parsing and manipulation of experiment IDs."""
-    CONCEPT_PATTERN = re.compile(r"(?:^|-)(C[1-5])(?:-|$)", flags=re.IGNORECASE)
-    VARIANT_SUFFIX_PATTERN = re.compile(
-        r"-(essay[-_]only|full[-_]context|full)$",
-        flags=re.IGNORECASE
-    )
-    AGGREGATION_SUFFIX_PATTERN = re.compile(
-        r"-(onlyA|onlyB|avg\(A,B\)|concat\(A,B\))$",
-        flags=re.IGNORECASE
-    )
-    # Group definitions
-    GROUP_1_PREFIXES = ["bertimbau", "bertugues", "mbert", "albertina"]
-    GROUP_2_PREFIXES = ["phi3.5", "phi4", "llama3.1", "tucano2b4"]
-    GROUP_3_PREFIXES = ["sabia3", "deepseekr1", "gpt4o"]
-    @classmethod
-    def get_experiment_group(cls, experiment_id: str) -> int:
-        """
-        Classify experiment into groups based on model name.
-        Returns:
-            1, 2, or 3 for the respective groups, or 4 for unclassified
-        """
-        exp_lower = experiment_id.lower()
-        for prefix in cls.GROUP_1_PREFIXES:
-            if exp_lower.startswith(prefix):
-                return 1
-        for prefix in cls.GROUP_2_PREFIXES:
-            if exp_lower.startswith(prefix):
-                return 2
-        for prefix in cls.GROUP_3_PREFIXES:
-            if exp_lower.startswith(prefix):
-                return 3
-        return 4  # Unclassified experiments
-    @classmethod
-    def split_experiment_id(cls, experiment_id: str) -> Tuple[str, str]:
-        """
-        Extract base ID and concept from experiment ID.
-        Args:
-            experiment_id: Full experiment ID string
-        Returns:
-            Tuple of (base_id_without_concept, concept)
-        Example:
-            'deepseekR1-extractor-C3-full_context' →
-            ('deepseekR1-extractor-full_context', 'C3')
-        """
-        match = cls.CONCEPT_PATTERN.search(experiment_id)
-        if not match:
-            raise ValueError(
-                f"Could not find C1–C5 token in experiment_id='{experiment_id}'"
-            )
-        concept = match.group(1).upper()
-        # Replace the concept token with a single dash to maintain separation
-        base_id = cls.CONCEPT_PATTERN.sub("-", experiment_id)
-        # Clean up any resulting double dashes or leading/trailing dashes
-        base_id = re.sub(r"-+", "-", base_id).strip("-")
-        return base_id, concept
-    @classmethod
-    def extract_family_name(cls, experiment_id: str) -> str:
-        """
-        Extract family name by removing variant suffixes.
-        Collapses variations like:
-        - 'phi3.5-mini-lora-essay-only-A'
-        - 'phi3.5-mini-lora-full-context-B'
-        into the same family: 'phi3.5-mini-lora'
-        """
-        # Remove aggregation suffixes
-        family = cls.AGGREGATION_SUFFIX_PATTERN.sub("", experiment_id)
-        # Remove variant suffixes
-        family = cls.VARIANT_SUFFIX_PATTERN.sub("", family)
-        return family
-class DataProcessor:
-    """Handles data loading and transformation."""
-    def __init__(self, config: TableConfig):
-        self.config = config
-        self.parser = ExperimentIdParser()
-    def load_and_prepare_data(self, file_path: Path, metric_group_filter: Optional[str] = None) -> pd.DataFrame:
-        """Load parquet file and add experiment metadata.
-        Args:
-            file_path: Path to the parquet file
-            metric_group_filter: If provided, only keep rows with this metric_group value
-        """
-        df = pd.read_parquet(file_path)
-        # Check if metric_group column exists
-        if "metric_group" in df.columns:
-            # Filter by metric_group if specified
-            if metric_group_filter:
-                df = df[df["metric_group"] == metric_group_filter].copy()
-            # Combine experiment_id with metric_group
-            df["experiment_id"] = df.apply(
-                lambda x: f"{x['experiment_id']}-{x['metric_group']}",
-                axis=1
-            )
-        # If no metric_group column, experiment_id remains unchanged (bootstrap case)
-        # Parse experiment IDs
-        base_ids, concepts = zip(*df["experiment_id"].map(self.parser.split_experiment_id))
-        df = df.assign(__base_id=base_ids, __concept=concepts)
-        return df
-    def create_pivot_table(self, df: pd.DataFrame) -> pd.DataFrame:
-        """Create pivoted table with one row per base ID and columns for each metric/concept."""
-        value_columns = list(self.config.metric_map.values())
-        # Melt data for pivoting
-        melted = (
-            df[["__base_id", "__concept"] + value_columns]
-            .melt(
-                id_vars=["__base_id", "__concept"],
-                var_name="metric_real",
-                value_name="value"
-            )
-        )
-        # Map back to friendly metric names
-        reverse_metric_map = {v: k for k, v in self.config.metric_map.items()}
-        melted["metric"] = melted["metric_real"].map(reverse_metric_map)
-        # Create pivot table
-        pivot = melted.pivot_table(
-            index="__base_id",
-            columns=["__concept", "metric"],
-            values="value",
-            aggfunc="mean"  # handle rare duplicates
-        )
-        # Ensure consistent column order
-        ordered_columns = pd.MultiIndex.from_product(
-            [self.config.concepts, list(self.config.metric_map.keys())]
-        )
-        pivot = pivot.reindex(columns=ordered_columns, fill_value=pd.NA)
-        # Add group information for sorting
-        pivot["__group"] = pivot.index.map(self.parser.get_experiment_group)
-        # Sort by group first, then by experiment name
-        pivot = pivot.sort_values(["__group", "__base_id"])
-        # Remove the temporary group column
-        pivot = pivot.drop(columns=["__group"])
-        return pivot
-class LaTeXTableGenerator:
-    """Generates LaTeX table from processed data."""
-    def __init__(self, config: TableConfig):
-        self.config = config
-        self.parser = ExperimentIdParser()
-    @staticmethod
-    def escape_latex(text: str) -> str:
-        """Escape special LaTeX characters."""
-        replacements = {
-            "_": r"\_",
-            "%": r"\%",
-            "&": r"\&",
-        }
-        for char, escaped in replacements.items():
-            text = text.replace(char, escaped)
-        return text
-    def _generate_header(self) -> List[str]:
-        """Generate table header lines."""
-        lines = []
-        # Table opening with column specifications
-        num_metrics = len(self.config.metric_map)
-        column_spec = "c|" + " ".join([" ".join(["c"] * num_metrics) + "|" for _ in self.config.concepts[:-1]]) + " " + " ".join(["c"] * num_metrics)
-        lines.append(rf"\begin{{tabular}}{{{column_spec}}}")
-        lines.append(r"\hline\hline")
-        # First header row - concept names
-        concept_headers = [""]
-        for concept in self.config.concepts:
-            concept_headers.append(rf"\multicolumn{{{num_metrics}}}{{c|}}{{\textbf{{{concept}}}}}")
-        concept_headers[-1] = concept_headers[-1].rstrip("|")
-        lines.append(" & ".join(concept_headers) + r" \\ \hline")
-        # Second header row - metric labels
-        metric_headers = [""]
-        for _ in self.config.concepts:
-            metric_headers.extend([
-                self.config.latex_labels[metric]
-                for metric in self.config.metric_map
-            ])
-        lines.append(" & ".join(metric_headers) + r" \\ \hline")
-        return lines
-    def _generate_body(self, data: pd.DataFrame) -> List[str]:
-        """Generate table body lines."""
-        lines = []
-        previous_family = None
-        previous_group = None
-        for base_id, row in data.iterrows():
-            family = self.parser.extract_family_name(base_id)
-            current_group = self.parser.get_experiment_group(base_id)
-            # Add double line separator between different groups
-            if previous_group is not None and current_group != previous_group:
-                lines.append(r"\hline\hline")
-            # Add single line separator between different families within same group
-            elif previous_family is not None and family != previous_family:
-                lines.append(r"\hline")
-            previous_family = family
-            previous_group = current_group
-            # Build row values
-            row_values = [self.escape_latex(base_id)]
-            for concept in self.config.concepts:
-                for metric in self.config.metric_map:
-                    value = row[(concept, metric)]
-                    row_values.append("--" if pd.isna(value) else f"{value:.2f}")
-            lines.append(" & ".join(row_values) + r" \\")
-        return lines
-    def generate_table(self, data: pd.DataFrame) -> str:
-        """Generate complete LaTeX table."""
-        lines = []
-        lines.extend(self._generate_header())
-        lines.extend(self._generate_body(data))
-        lines.append(r"\hline\hline")
-        lines.append(r"\end{tabular}%")
-        return "\n".join(lines)
-def main():
-    """Main entry point for the script."""
-    # Configuration for evaluation results
-    config = TableConfig.evaluation_results()
-    input_file = Path("evaluation_results-00000-of-00001.parquet")
-    output_file_full = Path("evaluation_table_full.txt")
-    output_file_avg = Path("evaluation_table_avg_only.txt")
-    # Process data for full table
-    processor = DataProcessor(config)
-    df_full = processor.load_and_prepare_data(input_file)
-    pivot_data_full = processor.create_pivot_table(df_full)
-    # Process data for avg(A,B) only
-    df_avg = processor.load_and_prepare_data(input_file, metric_group_filter="avg(A,B)")
-    pivot_data_avg = processor.create_pivot_table(df_avg)
-    # Generate LaTeX tables
-    generator = LaTeXTableGenerator(config)
-    latex_table_full = generator.generate_table(pivot_data_full)
-    latex_table_avg = generator.generate_table(pivot_data_avg)
-    # Save outputs
-    output_file_full.write_text(latex_table_full, encoding="utf-8")
-    output_file_avg.write_text(latex_table_avg, encoding="utf-8")
-    print(f"✅  Wrote {output_file_full} – full table with all metric groups")
-    print(f"✅  Wrote {output_file_avg} – filtered table with only avg(A,B) metric group")
-    # Process bootstrap confidence intervals
-    input_file_bootstrap = Path("bootstrap_confidence_intervals-00000-of-00001.parquet")
-    # 1. QWK bootstrap confidence intervals
-    config_bootstrap_qwk = TableConfig.bootstrap_qwk()
-    output_file_bootstrap_qwk = Path("bootstrap_confidence_intervals_QWK_table.txt")
-    processor_bootstrap_qwk = DataProcessor(config_bootstrap_qwk)
-    df_bootstrap = processor_bootstrap_qwk.load_and_prepare_data(input_file_bootstrap)
-    pivot_data_bootstrap_qwk = processor_bootstrap_qwk.create_pivot_table(df_bootstrap)
-    generator_bootstrap_qwk = LaTeXTableGenerator(config_bootstrap_qwk)
-    latex_table_bootstrap_qwk = generator_bootstrap_qwk.generate_table(pivot_data_bootstrap_qwk)
-    output_file_bootstrap_qwk.write_text(latex_table_bootstrap_qwk, encoding="utf-8")
-    print(f"✅  Wrote {output_file_bootstrap_qwk} – QWK bootstrap confidence intervals table")
-    # 2. Macro F1 bootstrap confidence intervals
-    config_bootstrap_macro_f1 = TableConfig.bootstrap_macro_f1()
-    output_file_bootstrap_macro_f1 = Path("bootstrap_confidence_intervals_Macro_F1_table.txt")
-    processor_bootstrap_macro_f1 = DataProcessor(config_bootstrap_macro_f1)
-    # Reuse the already loaded dataframe
-    pivot_data_bootstrap_macro_f1 = processor_bootstrap_macro_f1.create_pivot_table(df_bootstrap)
-    generator_bootstrap_macro_f1 = LaTeXTableGenerator(config_bootstrap_macro_f1)
-    latex_table_bootstrap_macro_f1 = generator_bootstrap_macro_f1.generate_table(pivot_data_bootstrap_macro_f1)
-    output_file_bootstrap_macro_f1.write_text(latex_table_bootstrap_macro_f1, encoding="utf-8")
-    print(f"✅  Wrote {output_file_bootstrap_macro_f1} – Macro F1 bootstrap confidence intervals table")
-    # 3. Weighted F1 bootstrap confidence intervals
-    config_bootstrap_weighted_f1 = TableConfig.bootstrap_weighted_f1()
-    output_file_bootstrap_weighted_f1 = Path("bootstrap_confidence_intervals_Weighted_F1_table.txt")
-    processor_bootstrap_weighted_f1 = DataProcessor(config_bootstrap_weighted_f1)
-    # Reuse the already loaded dataframe
-    pivot_data_bootstrap_weighted_f1 = processor_bootstrap_weighted_f1.create_pivot_table(df_bootstrap)
-    generator_bootstrap_weighted_f1 = LaTeXTableGenerator(config_bootstrap_weighted_f1)
-    latex_table_bootstrap_weighted_f1 = generator_bootstrap_weighted_f1.generate_table(pivot_data_bootstrap_weighted_f1)
-    output_file_bootstrap_weighted_f1.write_text(latex_table_bootstrap_weighted_f1, encoding="utf-8")
-    print(f"✅  Wrote {output_file_bootstrap_weighted_f1} – Weighted F1 bootstrap confidence intervals table")
-if __name__ == "__main__":
-    main()

create_parquet_files.py CHANGED Viewed

@@ -1,401 +1,40 @@
 import pandas as pd
 from pathlib import Path
 import pyarrow  # ensures pyarrow is installed for Parquet support
-import numpy as np
-import sys
-from tqdm.auto import tqdm
-import logging
-from datetime import datetime
-# Add the api_models directory to the Python path to import existing modules
-sys.path.append(str(Path(__file__).parent / "runs" / "api_models"))
-from compute_bootstrap_ci import (
-    load_inference_results_by_grader,
-    extract_config_from_log,
-)
-from metrics import compute_metrics
-from omegaconf import OmegaConf
-# Set up logging
-log_dir = Path("logs")
-log_dir.mkdir(exist_ok=True)
-log_file = log_dir / f"create_parquet_files_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
-# Configure logging to write to both file and console
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler(log_file),
-        logging.StreamHandler(sys.stdout)
-    ]
-)
-logger = logging.getLogger(__name__)
-# Also create a separate error-only log file
-error_log_file = log_dir / f"create_parquet_files_errors_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
-error_handler = logging.FileHandler(error_log_file)
-error_handler.setLevel(logging.ERROR)
-error_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
-logger.addHandler(error_handler)
-def simplify_experiment_name(name):
-    """Simplify experiment names according to the mapping rules."""
-    if pd.isna(name):
-        return name
-    # Convert to string to handle any non-string inputs
-    name = str(name)
-    # Define the mapping rules
-    mappings = {
-        # Sabia-3 mappings
-        'sabia-3-zero-shot': 'sabia3-studentPrompt',
-        'sabia-3-extractor-zero-shot': 'sabia3-extractor',
-        'sabia-3-grader-zero-shot': 'sabia3-graderPrompt',
-        # Deepseek mappings
-        'deepseek-reasoner-zero-shot': 'deepseekR1-studentPrompt',
-        'deepseek-reasoner-extractor-zero-shot': 'deepseekR1-extractor',
-        'deepseek-reasoner-grader-zero-shot': 'deepseekR1-graderPrompt',
-        # GPT-4o mappings
-        'gpt-4o-2024-11-20-zero-shot': 'gpt4o-studentPrompt',
-        'gpt-4o-2024-11-20-extractor-zero-shot': 'gpt4o-extractor',
-        'gpt-4o-2024-11-20-grader-zero-shot': 'gpt4o-graderPrompt',
-    }
-    # Apply direct mappings first
-    for pattern, replacement in mappings.items():
-        if pattern in name:
-            name = name.replace(pattern, replacement)
-    # Handle jbcs2025 prefixed names
-    if name.startswith('jbcs2025_'):
-        # Remove the prefix
-        name = name[9:]
-        # First, remove any duplicated model-specific patterns that appear multiple times
-        # These patterns indicate the experiment setup was duplicated in the name
-        duplication_patterns = [
-            'llama31_classification_lora',
-            'phi35_classification_lora',
-            'phi4_classification_lora',
-            'encoder_classification'
-            'tucano_classification_lora'
-        ]
-        for pattern in duplication_patterns:
-            # Count occurrences
-            count = name.count(f'-{pattern}-')
-            if count > 1:
-                # Replace all but keep track of components
-                parts = name.split(f'-{pattern}-')
-                # Keep the first part and the last part (which has the config)
-                if len(parts) > 2:
-                    name = parts[0] + '-' + parts[-1]
-        # Handle BERT variants
-        if 'bert-base-portuguese-cased' in name:
-            name = name.replace('bert-base-portuguese-cased', 'bertimbau-base')
-        elif 'BERTugues-base-portuguese-cased' in name:
-            name = name.replace('BERTugues-base-portuguese-cased', 'bertugues-base')
-        elif 'bert-base-multilingual-cased' in name:
-            name = name.replace('bert-base-multilingual-cased', 'mbert-base')
-        elif 'bert-large-portuguese-cased' in name:
-            name = name.replace('bert-large-portuguese-cased', 'bertimbau-large')
-        elif 'albertina-1b5-portuguese-ptbr-encoder' in name:
-            name = name.replace('albertina-1b5-portuguese-ptbr-encoder', 'albertina-1b5-ptbr')
-        # Handle Llama variants
-        elif 'Llama-3.1-8B-llama31_classification_lora' in name:
-            name = name.replace('Llama-3.1-8B-llama31_classification_lora', 'llama3.1-8b-lora')
-        elif 'Llama-3.1-8B' in name:
-            name = name.replace('Llama-3.1-8B', 'llama3.1-8b-lora')
-        # Handle Tucano variants
-        elif 'Tucano-2b4-Instruct-tucano_classification_lora' in name:
-            name = name.replace('Tucano-2b4-Instruct-tucano_classification_lora', 'tucano2b4-lora')
-        elif 'Tucano-2b4-Instruct' in name:
-            name = name.replace('Tucano-2b4-Instruct', 'tucano2b4-lora')
-        # Handle Phi variants
-        elif 'Phi-3.5-mini-instruct-phi35_classification_lora' in name:
-            name = name.replace('Phi-3.5-mini-instruct-phi35_classification_lora', 'phi3.5-mini-lora')
-        elif 'Phi-3.5-mini-instruct' in name:
-            name = name.replace('Phi-3.5-mini-instruct', 'phi3.5-mini-lora')
-        elif 'phi-4-phi4_classification_lora' in name:
-            name = name.replace('phi-4-phi4_classification_lora', 'phi4-lora')
-        elif 'phi-4' in name:
-            name = name.replace('phi-4', 'phi4-lora')
-        # Clean up any remaining classification patterns
-        name = name.replace('-encoder_classification', '')
-        name = name.replace('_classification_lora', '')
-        name = name.replace('-llama31', '')
-        name = name.replace('-phi35', '')
-        name = name.replace('-phi4', '')
-        name = name.replace('-tucano', '')
-        # Extract components and reorder
-        parts = name.split('-')
-        # Look for competency (C1-C5), context type, and LoRA rank
-        competency = None
-        context = None
-        lora_rank = None
-        model_parts = []
-        i = 0
-        while i < len(parts):
-            part = parts[i]
-            if part in ['C1', 'C2', 'C3', 'C4', 'C5']:
-                competency = part
-            elif part == 'essay_only':
-                context = 'essay-only'
-            elif part == 'full_context':
-                context = 'full-context'
-            elif part in ['essay', 'full'] and i + 1 < len(parts):
-                # Handle split context names
-                if parts[i+1] == 'only':
-                    context = 'essay-only'
-                    i += 1  # Skip next part
-                elif parts[i+1] == 'context':
-                    context = 'full-context'
-                    i += 1  # Skip next part
-            elif part in ['r8', 'r16']:
-                lora_rank = part
-            elif part and part not in ['only', 'context']:  # Skip empty parts and orphaned context words
-                model_parts.append(part)
-            i += 1
-        # Reconstruct the name in the desired order: model-competency-context-rank
-        new_parts = model_parts
-        if competency:
-            new_parts.append(competency)
-        if context:
-            new_parts.append(context)
-        if lora_rank:
-            new_parts.append(lora_rank)
-        name = '-'.join(new_parts)
-    # Final cleanup: remove any double dashes
-    while '--' in name:
-        name = name.replace('--', '-')
-    return name
 def find_and_group_csvs():
     base = Path(".")
     groups = {
         "evaluation_results": sorted(base.rglob("evaluation_results.csv")),
-        "bootstrap_confidence_intervals": sorted(
-            base.rglob("bootstrap_confidence_intervals.csv")
-        ),
     }
     for name, paths in groups.items():
-        logger.info(f"Found {len(paths)} files for '{name}'")
         if not paths:
-            logger.warning(f"No files found for '{name}'")
     return groups
-def enhance_evaluation_results(eval_df, csv_paths):
-    """Enhance evaluation results with additional metrics from JSONL files."""
-    enhanced_rows = []
-    failed_count = 0
-    # Create a mapping from row index to CSV path
-    # Since we're processing multiple CSVs that get concatenated,
-    # we need to track which rows came from which CSV file
-    row_to_path = {}
-    current_idx = 0
-    for path in csv_paths:
-        df = pd.read_csv(path)
-        for i in range(len(df)):
-            row_to_path[current_idx + i] = path
-        current_idx += len(df)
-    for idx, row in tqdm(
-        eval_df.iterrows(), desc="Processing evaluation rows", total=len(eval_df)
-    ):
-        # Get the CSV path for this row
-        csv_path = row_to_path.get(idx)
-        if csv_path is None:
-            error_msg = f"CSV file not found for row {idx}"
-            logger.error(error_msg)
-            failed_count += 1
-            continue
-        try:
-            # Extract experiment ID from the path
-            # The experiment ID is typically the parent directory name
-            experiment_id = csv_path.parent.name
-            # Simplify the experiment ID
-            experiment_id = simplify_experiment_name(experiment_id)
-            # Find corresponding JSONL file in the same directory
-            jsonl_path = csv_path.parent / "inference_results.jsonl"
-            if not jsonl_path.exists():
-                # Try with experiment name prefix
-                jsonl_files = list(csv_path.parent.glob("*_inference_results.jsonl"))
-                if jsonl_files:
-                    jsonl_path = jsonl_files[0]
-                else:
-                    raise FileNotFoundError(f"JSONL file not found in {csv_path.parent}")
-            # Find log file to extract configuration
-            log_files = list(csv_path.parent.glob("*run_inference_experiment.log"))
-            if not log_files:
-                raise FileNotFoundError(f"Log file not found in {csv_path.parent}")
-            log_path = log_files[0]
-            # Load inference results and compute metrics
-            # Extract configuration from log file
-            config_dict = extract_config_from_log(log_path)
-            # Convert to OmegaConf DictConfig for compatibility with compute_metrics
-            cfg = OmegaConf.create(config_dict)
-            # Load data using the existing function
-            grader_a_data, grader_b_data = load_inference_results_by_grader(jsonl_path)
-            # Extract predictions and labels for each grader
-            all_predictions_a = np.array(
-                [data["prediction"] for data in grader_a_data.values()]
-            )
-            all_labels_a = np.array([data["label"] for data in grader_a_data.values()])
-            all_predictions_b = np.array(
-                [data["prediction"] for data in grader_b_data.values()]
-            )
-            all_labels_b = np.array([data["label"] for data in grader_b_data.values()])
-            # Compute concat(A,B) metrics for verification
-            # Concatenate predictions and labels from both graders
-            concat_predictions = np.concatenate([all_predictions_a, all_predictions_b])
-            concat_labels = np.concatenate([all_labels_a, all_labels_b])
-            metrics_concat = compute_metrics((concat_predictions, concat_labels), cfg)
-            # Verify that computed concat metrics match original CSV values
-            # Check a few key metrics with some tolerance for floating point comparison
-            tolerance = 1e-6
-            for metric in ["accuracy", "QWK", "Macro_F1", "Weighted_F1"]:
-                if metric in row and metric in metrics_concat:
-                    original_value = row[metric]
-                    computed_value = metrics_concat[metric]
-                    # You can make this a hard assertion if needed:
-                    assert abs(original_value - computed_value) <= tolerance, (
-                        f"Metric {metric} mismatch: CSV={original_value}, Computed={computed_value}"
-                    )
-            # 1. Add original row with concat(A,B) metrics
-            concat_row = row.copy()
-            concat_row["experiment_id"] = experiment_id
-            concat_row["metric_group"] = "concat(A,B)"
-            enhanced_rows.append(concat_row)
-            # 2. Compute metrics for A and B separately first
-            metrics_a = compute_metrics((all_predictions_a, all_labels_a), cfg)
-            metrics_b = compute_metrics((all_predictions_b, all_labels_b), cfg)
-            # 3. Compute avg(A,B) as the average of metrics, not metrics of averaged predictions
-            avg_row = row.copy()
-            avg_row["experiment_id"] = experiment_id
-            avg_row["metric_group"] = "avg(A,B)"
-            # Average the metrics from A and B
-            for metric in metrics_a:
-                if metric in metrics_b and metric in avg_row:
-                    avg_value = (metrics_a[metric] + metrics_b[metric]) / 2
-                    avg_row[metric] = avg_value
-            enhanced_rows.append(avg_row)
-            # 4. Add onlyA metrics
-            only_a_row = row.copy()
-            only_a_row["experiment_id"] = experiment_id
-            only_a_row["metric_group"] = "onlyA"
-            # Update metric columns with onlyA values
-            for metric, value in metrics_a.items():
-                if metric in only_a_row:
-                    only_a_row[metric] = value
-            enhanced_rows.append(only_a_row)
-            # 5. Add onlyB metrics
-            only_b_row = row.copy()
-            only_b_row["experiment_id"] = experiment_id
-            only_b_row["metric_group"] = "onlyB"
-            # Update metric columns with onlyB values
-            for metric, value in metrics_b.items():
-                if metric in only_b_row:
-                    only_b_row[metric] = value
-            enhanced_rows.append(only_b_row)
-        except Exception as e:
-            failed_count += 1
-            error_msg = f"Failed to process {csv_path.parent if csv_path else 'unknown path'}: {str(e)}"
-            logger.error(error_msg)
-            # Log full traceback for debugging
-            import traceback
-            logger.error(f"Traceback:\n{traceback.format_exc()}")
-            # Skip this row and continue with the next one
-            continue
-    logger.info(f"Successfully processed {len(enhanced_rows)//4} out of {len(eval_df)} rows")
-    if failed_count > 0:
-        logger.warning(f"Failed to process {failed_count} rows. Check error log: {error_log_file}")
-    return pd.DataFrame(enhanced_rows)
 def combine(paths, out_path):
     if not paths:
-        logger.info(f"No files to combine for {out_path}")
         return
-    logger.info(f"Combining {len(paths)} files into {out_path}")
-    dfs = []
-    for p in paths:
-        df = pd.read_csv(p)
-        # Add experiment_id column based on the parent directory name
-        experiment_id = p.parent.name
-        experiment_id = simplify_experiment_name(experiment_id)
-        df["experiment_id"] = experiment_id
-        dfs.append(df)
     # Basic schema validation
     cols = {tuple(df.columns) for df in dfs}
     if len(cols) > 1:
-        error_msg = f"{out_path}: header mismatch across shards"
-        logger.error(error_msg)
-        raise ValueError(error_msg)
     combined = pd.concat(dfs, ignore_index=True)
-    # Enhance evaluation results with additional metrics
-    if "evaluation_results" in out_path:
-        logger.info("Enhancing evaluation results with additional metrics...")
-        combined = enhance_evaluation_results(combined, paths)
     combined.to_parquet(out_path, engine="pyarrow", index=False)
-    logger.info(f"Successfully written {out_path} with {len(combined)} rows")
 if __name__ == "__main__":
-    logger.info(f"Starting parquet file creation. Logs will be saved to: {log_file}")
-    logger.info(f"Error-only log will be saved to: {error_log_file}")
     groups = find_and_group_csvs()
     combine(groups["evaluation_results"], "evaluation_results-00000-of-00001.parquet")
-    combine(
-        groups["bootstrap_confidence_intervals"],
-        "bootstrap_confidence_intervals-00000-of-00001.parquet",
-    )
-    logger.info("Parquet file creation completed")

 import pandas as pd
 from pathlib import Path
 import pyarrow  # ensures pyarrow is installed for Parquet support
 def find_and_group_csvs():
     base = Path(".")
     groups = {
         "evaluation_results": sorted(base.rglob("evaluation_results.csv")),
+        "bootstrap_confidence_intervals": sorted(base.rglob("bootstrap_confidence_intervals.csv")),
     }
     for name, paths in groups.items():
+        print(f"[INFO] Found {len(paths)} files for '{name}'")
         if not paths:
+            print(f"[WARNING] No files found for '{name}'")
     return groups
 def combine(paths, out_path):
     if not paths:
+        print(f"[SKIP] No files to combine for {out_path}")
         return
+    print(f"[INFO] Combining {len(paths)} files into {out_path}")
+    dfs = [pd.read_csv(p) for p in paths]
     # Basic schema validation
     cols = {tuple(df.columns) for df in dfs}
     if len(cols) > 1:
+        raise ValueError(f"[ERROR] {out_path}: header mismatch across shards")
     combined = pd.concat(dfs, ignore_index=True)
     combined.to_parquet(out_path, engine="pyarrow", index=False)
+    print(f"[SUCCESS] Written {out_path} with {len(combined)} rows")
 if __name__ == "__main__":
     groups = find_and_group_csvs()
     combine(groups["evaluation_results"], "evaluation_results-00000-of-00001.parquet")
+    combine(groups["bootstrap_confidence_intervals"], "boostrap_confidence_intervals-00000-of-00001.parquet")

dataset/Teste_JBCS25.csv DELETED Viewed

@@ -1,139 +0,0 @@
-adjective_ratio,adverbs,content_words,flesch,function_words,sentences_per_paragraph,syllables_per_content_word,words_per_sentence,noun_ratio,paragraphs,sentences,words,pronoun_ratio,verbs,logic_operators,and_ratio,if_ratio,or_ratio,negation_ratio,cw_freq,cw_freq_brwac,cw_freq_bra,min_cw_freq,min_cw_freq_brwac,min_freq_brwac,min_cw_freq_bra,min_freq_bra,freq_brwac,freq_bra,hypernyms_verbs,brunet,honore,personal_pronouns,ttr,conn_ratio,add_neg_conn_ratio,add_pos_conn_ratio,cau_neg_conn_ratio,cau_pos_conn_ratio,log_neg_conn_ratio,log_pos_conn_ratio,tmp_neg_conn_ratio,tmp_pos_conn_ratio,adjectives_ambiguity,adverbs_ambiguity,nouns_ambiguity,verbs_ambiguity,yngve,frazier,dep_distance,cross_entropy,content_density,adjacent_refs,anaphoric_refs,adj_arg_ovl,arg_ovl,adj_stem_ovl,stem_ovl,adj_cw_ovl,lsa_adj_mean,lsa_adj_std,lsa_all_mean,lsa_all_std,lsa_paragraph_mean,lsa_paragraph_std,lsa_givenness_mean,lsa_givenness_std,lsa_span_mean,lsa_span_std,negative_words,positive_words,ratio_function_to_content_words,grade,index_text,id,id_prompt,reference
-0.12676,0.03521,0.56338,-8.32523,0.43662,1.5,3.2875,47.33333,0.28169,2,3,142,0.10563,0.11972,0.02817,0.02113,0.0,0.0,0.0,410626.275,4.49631,4.71685,34.66667,1.15767,1.15767,2.046,2.046,5.37575,5.54761,0.5625,9.89749,1354.6756,0.01,0.80256,0.07042,0.01408,0.04225,0.0,0.03521,0.00704,0.02113,0.0,0.00704,4.33333,3.5,3.86486,8.25,3.68051,9.0,163.0,0.45075,1.29032,1.5,1.5,3.0,2.0,6.0,5.0,4.0,0.93878,0.01231,0.93677,0.0,0.96,0.0,0.94554,0.00556,0.94625,0.00485,0.2,0.75,0.775,"[120, 160, 120, 120, 80, 600]",0,7.html,o-brasil-e-os-imigrantes-no-mundo-contemporaneo,grader_a
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-\begin{tabular}{c|c c c| c c c| c c c| c c c| c c c}
-\hline\hline
- & \multicolumn{3}{c|}{\textbf{C1}} & \multicolumn{3}{c|}{\textbf{C2}} & \multicolumn{3}{c|}{\textbf{C3}} & \multicolumn{3}{c|}{\textbf{C4}} & \multicolumn{3}{c|}{\textbf{C5}} \\ \hline
- & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK \\ \hline
-albertina-1b5-ptbr-essay-only-avg(A,B) & 0.55 & 0.71 & 0.68 & 0.12 & 0.18 & 0.24 & 0.23 & 0.32 & 0.26 & 0.36 & 0.59 & 0.54 & 0.15 & 0.17 & 0.04 \\
-\hline
-bertimbau-base-essay-only-avg(A,B) & 0.42 & 0.52 & 0.60 & 0.26 & 0.37 & 0.36 & 0.23 & 0.28 & 0.35 & 0.38 & 0.55 & 0.55 & 0.29 & 0.37 & 0.63 \\
-\hline
-bertimbau-large-essay-only-avg(A,B) & 0.52 & 0.69 & 0.68 & 0.33 & 0.43 & 0.32 & 0.19 & 0.26 & 0.24 & 0.48 & 0.61 & 0.60 & 0.29 & 0.37 & 0.46 \\
-\hline
-bertugues-base-essay-only-avg(A,B) & 0.44 & 0.56 & 0.62 & 0.14 & 0.19 & 0.33 & 0.25 & 0.36 & 0.29 & 0.35 & 0.51 & 0.54 & 0.23 & 0.28 & 0.36 \\
-\hline
-mbert-base-essay-only-avg(A,B) & 0.40 & 0.59 & 0.52 & 0.18 & 0.27 & 0.22 & 0.25 & 0.32 & 0.35 & 0.35 & 0.55 & 0.50 & 0.08 & 0.09 & 0.00 \\
-\hline\hline
-llama3.1-8b-lora-essay-only-r16-avg(A,B) & 0.53 & 0.67 & 0.69 & 0.26 & 0.36 & 0.36 & 0.26 & 0.30 & 0.43 & 0.31 & 0.45 & 0.45 & 0.21 & 0.22 & 0.36 \\
-\hline
-llama3.1-8b-lora-essay-only-r8-avg(A,B) & 0.49 & 0.67 & 0.65 & 0.16 & 0.19 & 0.32 & 0.25 & 0.37 & 0.37 & 0.24 & 0.53 & 0.39 & 0.26 & 0.28 & 0.29 \\
-\hline
-llama3.1-8b-lora-full-context-r16-avg(A,B) & 0.51 & 0.68 & 0.66 & 0.20 & 0.25 & 0.33 & 0.24 & 0.26 & 0.32 & 0.26 & 0.51 & 0.44 & 0.25 & 0.24 & 0.19 \\
-\hline
-llama3.1-8b-lora-full-context-r8-avg(A,B) & 0.41 & 0.57 & 0.60 & 0.21 & 0.29 & 0.25 & 0.25 & 0.28 & 0.32 & 0.39 & 0.55 & 0.54 & 0.21 & 0.20 & 0.18 \\
-\hline
-phi3.5-mini-lora-essay-only-r16-avg(A,B) & 0.33 & 0.40 & 0.45 & 0.21 & 0.26 & 0.28 & 0.24 & 0.28 & 0.22 & 0.29 & 0.50 & 0.27 & 0.19 & 0.24 & 0.25 \\
-\hline
-phi3.5-mini-lora-essay-only-r8-avg(A,B) & 0.33 & 0.40 & 0.45 & 0.21 & 0.26 & 0.28 & 0.24 & 0.28 & 0.22 & 0.29 & 0.50 & 0.27 & 0.19 & 0.24 & 0.25 \\
-\hline
-phi3.5-mini-lora-full-context-r16-avg(A,B) & 0.19 & 0.29 & 0.28 & 0.14 & 0.22 & -0.03 & 0.28 & 0.35 & 0.44 & 0.11 & 0.20 & 0.08 & 0.18 & 0.19 & 0.19 \\
-\hline
-phi3.5-mini-lora-full-context-r8-avg(A,B) & 0.19 & 0.29 & 0.28 & 0.14 & 0.22 & -0.03 & 0.28 & 0.35 & 0.44 & 0.11 & 0.20 & 0.08 & 0.18 & 0.19 & 0.19 \\
-\hline
-phi4-lora-essay-only-r16-avg(A,B) & 0.53 & 0.66 & 0.70 & 0.19 & 0.25 & 0.21 & 0.25 & 0.34 & 0.41 & 0.38 & 0.63 & 0.58 & 0.36 & 0.42 & 0.65 \\
-\hline
-phi4-lora-essay-only-r8-avg(A,B) & 0.50 & 0.61 & 0.67 & 0.28 & 0.39 & 0.34 & 0.24 & 0.31 & 0.33 & 0.30 & 0.58 & 0.48 & 0.44 & 0.49 & 0.59 \\
-\hline
-phi4-lora-full-context-r16-avg(A,B) & 0.41 & 0.58 & 0.61 & 0.42 & 0.52 & 0.60 & 0.42 & 0.48 & 0.66 & 0.35 & 0.50 & 0.60 & 0.29 & 0.33 & 0.53 \\
-\hline
-phi4-lora-full-context-r8-avg(A,B) & 0.48 & 0.63 & 0.67 & 0.40 & 0.50 & 0.59 & 0.37 & 0.38 & 0.57 & 0.37 & 0.58 & 0.55 & 0.29 & 0.32 & 0.52 \\
-\hline
-tucano2b4-lora-essay-only-r16-avg(A,B) & 0.52 & 0.66 & 0.67 & 0.15 & 0.24 & 0.22 & 0.24 & 0.31 & 0.36 & 0.40 & 0.58 & 0.50 & 0.26 & 0.29 & 0.54 \\
-\hline
-tucano2b4-lora-essay-only-r8-avg(A,B) & 0.52 & 0.64 & 0.66 & 0.14 & 0.22 & 0.23 & 0.25 & 0.35 & 0.36 & 0.34 & 0.57 & 0.49 & 0.25 & 0.30 & 0.58 \\
-\hline
-tucano2b4-lora-full-context-r16-avg(A,B) & 0.40 & 0.59 & 0.54 & 0.19 & 0.27 & 0.24 & 0.22 & 0.27 & 0.24 & 0.39 & 0.59 & 0.52 & 0.25 & 0.29 & 0.43 \\
-\hline
-tucano2b4-lora-full-context-r8-avg(A,B) & 0.43 & 0.56 & 0.57 & 0.21 & 0.35 & 0.28 & 0.24 & 0.31 & 0.24 & 0.37 & 0.51 & 0.52 & 0.19 & 0.24 & 0.24 \\
-\hline\hline
-deepseekR1-extractor-essay\_only-avg(A,B) & 0.07 & 0.12 & 0.21 & 0.01 & 0.01 & -0.01 & 0.27 & 0.34 & 0.41 & 0.33 & 0.53 & 0.53 & 0.35 & 0.39 & 0.62 \\
-deepseekR1-extractor-full\_context-avg(A,B) & 0.04 & 0.08 & 0.20 & 0.27 & 0.34 & 0.51 & 0.30 & 0.41 & 0.52 & 0.37 & 0.60 & 0.56 & 0.37 & 0.42 & 0.61 \\
-\hline
-deepseekR1-graderPrompt-essay\_only-avg(A,B) & 0.17 & 0.25 & 0.44 & 0.05 & 0.05 & 0.03 & 0.19 & 0.19 & 0.39 & 0.21 & 0.25 & 0.48 & 0.31 & 0.34 & 0.55 \\
-deepseekR1-graderPrompt-full\_context-avg(A,B) & 0.05 & 0.06 & 0.20 & 0.32 & 0.43 & 0.53 & 0.30 & 0.30 & 0.60 & 0.29 & 0.46 & 0.55 & 0.35 & 0.38 & 0.60 \\
-\hline
-deepseekR1-studentPrompt-essay\_only-avg(A,B) & 0.11 & 0.17 & 0.36 & 0.13 & 0.26 & -0.02 & 0.32 & 0.34 & 0.38 & 0.27 & 0.39 & 0.52 & 0.37 & 0.43 & 0.55 \\
-deepseekR1-studentPrompt-full\_context-avg(A,B) & 0.04 & 0.06 & 0.35 & 0.26 & 0.37 & 0.42 & 0.44 & 0.47 & 0.73 & 0.22 & 0.37 & 0.49 & 0.37 & 0.40 & 0.58 \\
-\hline
-gpt4o-extractor-essay\_only-avg(A,B) & 0.25 & 0.51 & 0.55 & 0.12 & 0.14 & 0.15 & 0.23 & 0.28 & 0.39 & 0.34 & 0.48 & 0.56 & 0.26 & 0.28 & 0.51 \\
-gpt4o-extractor-full\_context-avg(A,B) & 0.25 & 0.54 & 0.53 & 0.32 & 0.43 & 0.47 & 0.27 & 0.30 & 0.57 & 0.27 & 0.45 & 0.50 & 0.14 & 0.15 & 0.32 \\
-\hline
-gpt4o-graderPrompt-essay\_only-avg(A,B) & 0.21 & 0.45 & 0.49 & 0.16 & 0.22 & 0.28 & 0.20 & 0.27 & 0.28 & 0.29 & 0.39 & 0.51 & 0.32 & 0.35 & 0.54 \\
-gpt4o-graderPrompt-full\_context-avg(A,B) & 0.25 & 0.52 & 0.55 & 0.31 & 0.44 & 0.56 & 0.27 & 0.33 & 0.45 & 0.26 & 0.47 & 0.50 & 0.24 & 0.26 & 0.48 \\
-\hline
-gpt4o-studentPrompt-essay\_only-avg(A,B) & 0.22 & 0.43 & 0.51 & 0.16 & 0.27 & 0.20 & 0.26 & 0.30 & 0.38 & 0.30 & 0.40 & 0.51 & 0.28 & 0.31 & 0.55 \\
-gpt4o-studentPrompt-full\_context-avg(A,B) & 0.21 & 0.42 & 0.48 & 0.34 & 0.43 & 0.52 & 0.27 & 0.30 & 0.53 & 0.26 & 0.37 & 0.49 & 0.17 & 0.19 & 0.35 \\
-\hline
-sabia3-extractor-essay\_only-avg(A,B) & 0.09 & 0.25 & 0.29 & 0.13 & 0.19 & 0.18 & 0.11 & 0.19 & 0.11 & 0.19 & 0.36 & 0.45 & 0.21 & 0.22 & 0.52 \\
-sabia3-extractor-full\_context-avg(A,B) & 0.10 & 0.26 & 0.31 & 0.16 & 0.24 & 0.28 & 0.20 & 0.29 & 0.21 & 0.18 & 0.38 & 0.38 & 0.22 & 0.24 & 0.51 \\
-\hline
-sabia3-graderPrompt-essay\_only-avg(A,B) & 0.23 & 0.42 & 0.53 & 0.11 & 0.12 & 0.06 & 0.28 & 0.33 & 0.34 & 0.24 & 0.43 & 0.55 & 0.33 & 0.38 & 0.59 \\
-sabia3-graderPrompt-full\_context-avg(A,B) & 0.24 & 0.50 & 0.50 & 0.33 & 0.43 & 0.50 & 0.33 & 0.37 & 0.54 & 0.31 & 0.49 & 0.53 & 0.28 & 0.32 & 0.50 \\
-\hline
-sabia3-studentPrompt-essay\_only-avg(A,B) & 0.35 & 0.66 & 0.69 & 0.11 & 0.11 & 0.01 & 0.23 & 0.32 & 0.30 & 0.31 & 0.53 & 0.52 & 0.29 & 0.35 & 0.51 \\
-sabia3-studentPrompt-full\_context-avg(A,B) & 0.35 & 0.60 & 0.58 & 0.28 & 0.42 & 0.47 & 0.21 & 0.24 & 0.45 & 0.28 & 0.43 & 0.39 & 0.27 & 0.28 & 0.54 \\
-\hline\hline
-\end{tabular}%

evaluation_table_full.txt DELETED Viewed

@@ -1,191 +0,0 @@
-\begin{tabular}{c|c c c| c c c| c c c| c c c| c c c}
-\hline\hline
- & \multicolumn{3}{c|}{\textbf{C1}} & \multicolumn{3}{c|}{\textbf{C2}} & \multicolumn{3}{c|}{\textbf{C3}} & \multicolumn{3}{c|}{\textbf{C4}} & \multicolumn{3}{c|}{\textbf{C5}} \\ \hline
- & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK & F1$_{ma}$ & F1$_{w}$ & QWK \\ \hline
-albertina-1b5-ptbr-essay-only-avg(A,B) & 0.55 & 0.71 & 0.68 & 0.12 & 0.18 & 0.24 & 0.23 & 0.32 & 0.26 & 0.36 & 0.59 & 0.54 & 0.15 & 0.17 & 0.04 \\
-albertina-1b5-ptbr-essay-only-concat(A,B) & 0.50 & 0.71 & 0.68 & 0.13 & 0.15 & 0.24 & 0.22 & 0.31 & 0.25 & 0.30 & 0.59 & 0.54 & 0.15 & 0.17 & 0.03 \\
-albertina-1b5-ptbr-essay-only-onlyA & 0.55 & 0.76 & 0.71 & 0.10 & 0.18 & 0.26 & 0.19 & 0.32 & 0.28 & 0.38 & 0.62 & 0.49 & 0.16 & 0.19 & 0.15 \\
-albertina-1b5-ptbr-essay-only-onlyB & 0.56 & 0.67 & 0.65 & 0.14 & 0.17 & 0.22 & 0.28 & 0.31 & 0.23 & 0.33 & 0.57 & 0.59 & 0.15 & 0.16 & -0.07 \\
-\hline
-bertimbau-base-essay-only-avg(A,B) & 0.42 & 0.52 & 0.60 & 0.26 & 0.37 & 0.36 & 0.23 & 0.28 & 0.35 & 0.38 & 0.55 & 0.55 & 0.29 & 0.37 & 0.63 \\
-bertimbau-base-essay-only-concat(A,B) & 0.37 & 0.52 & 0.60 & 0.25 & 0.35 & 0.35 & 0.22 & 0.27 & 0.35 & 0.32 & 0.55 & 0.55 & 0.30 & 0.36 & 0.63 \\
-bertimbau-base-essay-only-onlyA & 0.35 & 0.52 & 0.59 & 0.18 & 0.34 & 0.28 & 0.17 & 0.29 & 0.34 & 0.40 & 0.58 & 0.49 & 0.29 & 0.34 & 0.60 \\
-bertimbau-base-essay-only-onlyB & 0.49 & 0.51 & 0.61 & 0.34 & 0.39 & 0.44 & 0.29 & 0.27 & 0.36 & 0.36 & 0.52 & 0.61 & 0.30 & 0.40 & 0.65 \\
-\hline
-bertimbau-large-essay-only-avg(A,B) & 0.52 & 0.69 & 0.68 & 0.33 & 0.43 & 0.32 & 0.19 & 0.26 & 0.24 & 0.48 & 0.61 & 0.60 & 0.29 & 0.37 & 0.46 \\
-bertimbau-large-essay-only-concat(A,B) & 0.46 & 0.69 & 0.68 & 0.32 & 0.41 & 0.32 & 0.17 & 0.26 & 0.25 & 0.46 & 0.60 & 0.60 & 0.30 & 0.36 & 0.46 \\
-bertimbau-large-essay-only-onlyA & 0.49 & 0.71 & 0.69 & 0.26 & 0.43 & 0.24 & 0.14 & 0.25 & 0.18 & 0.31 & 0.54 & 0.53 & 0.34 & 0.43 & 0.49 \\
-bertimbau-large-essay-only-onlyB & 0.54 & 0.67 & 0.67 & 0.40 & 0.44 & 0.41 & 0.24 & 0.26 & 0.30 & 0.65 & 0.67 & 0.67 & 0.25 & 0.31 & 0.43 \\
-\hline
-bertugues-base-essay-only-avg(A,B) & 0.44 & 0.56 & 0.62 & 0.14 & 0.19 & 0.33 & 0.25 & 0.36 & 0.29 & 0.35 & 0.51 & 0.54 & 0.23 & 0.28 & 0.36 \\
-bertugues-base-essay-only-concat(A,B) & 0.39 & 0.56 & 0.61 & 0.14 & 0.18 & 0.33 & 0.26 & 0.35 & 0.29 & 0.29 & 0.51 & 0.54 & 0.24 & 0.27 & 0.36 \\
-bertugues-base-essay-only-onlyA & 0.35 & 0.53 & 0.54 & 0.14 & 0.29 & 0.34 & 0.22 & 0.36 & 0.29 & 0.37 & 0.50 & 0.50 & 0.24 & 0.31 & 0.37 \\
-bertugues-base-essay-only-onlyB & 0.53 & 0.60 & 0.70 & 0.14 & 0.10 & 0.33 & 0.28 & 0.35 & 0.28 & 0.32 & 0.51 & 0.58 & 0.22 & 0.24 & 0.35 \\
-\hline
-mbert-base-essay-only-avg(A,B) & 0.40 & 0.59 & 0.52 & 0.18 & 0.27 & 0.22 & 0.25 & 0.32 & 0.35 & 0.35 & 0.55 & 0.50 & 0.08 & 0.09 & 0.00 \\
-mbert-base-essay-only-concat(A,B) & 0.35 & 0.59 & 0.51 & 0.19 & 0.26 & 0.22 & 0.22 & 0.32 & 0.36 & 0.29 & 0.55 & 0.50 & 0.08 & 0.09 & 0.00 \\
-mbert-base-essay-only-onlyA & 0.34 & 0.56 & 0.42 & 0.17 & 0.29 & 0.24 & 0.19 & 0.30 & 0.31 & 0.37 & 0.57 & 0.40 & 0.06 & 0.04 & 0.01 \\
-mbert-base-essay-only-onlyB & 0.46 & 0.62 & 0.61 & 0.19 & 0.26 & 0.21 & 0.30 & 0.35 & 0.40 & 0.34 & 0.53 & 0.59 & 0.10 & 0.14 & -0.01 \\
-\hline\hline
-llama3.1-8b-lora-essay-only-r16-avg(A,B) & 0.53 & 0.67 & 0.69 & 0.26 & 0.36 & 0.36 & 0.26 & 0.30 & 0.43 & 0.31 & 0.45 & 0.45 & 0.21 & 0.22 & 0.36 \\
-llama3.1-8b-lora-essay-only-r16-concat(A,B) & 0.48 & 0.67 & 0.69 & 0.25 & 0.35 & 0.36 & 0.25 & 0.28 & 0.43 & 0.26 & 0.45 & 0.45 & 0.22 & 0.21 & 0.36 \\
-llama3.1-8b-lora-essay-only-r16-onlyA & 0.53 & 0.69 & 0.71 & 0.21 & 0.36 & 0.33 & 0.22 & 0.33 & 0.39 & 0.37 & 0.50 & 0.47 & 0.14 & 0.15 & 0.28 \\
-llama3.1-8b-lora-essay-only-r16-onlyB & 0.53 & 0.65 & 0.67 & 0.32 & 0.36 & 0.39 & 0.31 & 0.26 & 0.47 & 0.24 & 0.41 & 0.43 & 0.28 & 0.29 & 0.44 \\
-\hline
-llama3.1-8b-lora-essay-only-r8-avg(A,B) & 0.49 & 0.67 & 0.65 & 0.16 & 0.19 & 0.32 & 0.25 & 0.37 & 0.37 & 0.24 & 0.53 & 0.39 & 0.26 & 0.28 & 0.29 \\
-llama3.1-8b-lora-essay-only-r8-concat(A,B) & 0.45 & 0.66 & 0.65 & 0.16 & 0.16 & 0.32 & 0.24 & 0.36 & 0.37 & 0.22 & 0.53 & 0.39 & 0.27 & 0.27 & 0.30 \\
-llama3.1-8b-lora-essay-only-r8-onlyA & 0.47 & 0.68 & 0.64 & 0.08 & 0.09 & 0.24 & 0.23 & 0.46 & 0.37 & 0.22 & 0.55 & 0.41 & 0.26 & 0.27 & 0.23 \\
-llama3.1-8b-lora-essay-only-r8-onlyB & 0.51 & 0.65 & 0.65 & 0.24 & 0.30 & 0.40 & 0.26 & 0.27 & 0.37 & 0.26 & 0.52 & 0.37 & 0.27 & 0.30 & 0.36 \\
-\hline
-llama3.1-8b-lora-full-context-r16-avg(A,B) & 0.51 & 0.68 & 0.66 & 0.20 & 0.25 & 0.33 & 0.24 & 0.26 & 0.32 & 0.26 & 0.51 & 0.44 & 0.25 & 0.24 & 0.19 \\
-llama3.1-8b-lora-full-context-r16-concat(A,B) & 0.46 & 0.68 & 0.66 & 0.21 & 0.22 & 0.33 & 0.22 & 0.26 & 0.32 & 0.23 & 0.50 & 0.44 & 0.26 & 0.24 & 0.19 \\
-llama3.1-8b-lora-full-context-r16-onlyA & 0.48 & 0.68 & 0.64 & 0.14 & 0.18 & 0.29 & 0.22 & 0.28 & 0.26 & 0.19 & 0.44 & 0.35 & 0.20 & 0.24 & 0.21 \\
-llama3.1-8b-lora-full-context-r16-onlyB & 0.54 & 0.69 & 0.68 & 0.26 & 0.33 & 0.38 & 0.25 & 0.25 & 0.38 & 0.33 & 0.57 & 0.54 & 0.30 & 0.25 & 0.17 \\
-\hline
-llama3.1-8b-lora-full-context-r8-avg(A,B) & 0.41 & 0.57 & 0.60 & 0.21 & 0.29 & 0.25 & 0.25 & 0.28 & 0.32 & 0.39 & 0.55 & 0.54 & 0.21 & 0.20 & 0.18 \\
-llama3.1-8b-lora-full-context-r8-concat(A,B) & 0.37 & 0.57 & 0.59 & 0.21 & 0.26 & 0.24 & 0.23 & 0.28 & 0.32 & 0.33 & 0.55 & 0.54 & 0.23 & 0.19 & 0.18 \\
-llama3.1-8b-lora-full-context-r8-onlyA & 0.38 & 0.59 & 0.56 & 0.17 & 0.26 & 0.23 & 0.25 & 0.33 & 0.29 & 0.34 & 0.51 & 0.44 & 0.18 & 0.22 & 0.19 \\
-llama3.1-8b-lora-full-context-r8-onlyB & 0.43 & 0.56 & 0.63 & 0.25 & 0.32 & 0.26 & 0.24 & 0.24 & 0.34 & 0.44 & 0.59 & 0.64 & 0.25 & 0.17 & 0.17 \\
-\hline
-phi3.5-mini-lora-essay-only-r16-avg(A,B) & 0.33 & 0.40 & 0.45 & 0.21 & 0.26 & 0.28 & 0.24 & 0.28 & 0.22 & 0.29 & 0.50 & 0.27 & 0.19 & 0.24 & 0.25 \\
-phi3.5-mini-lora-essay-only-r16-concat(A,B) & 0.30 & 0.40 & 0.45 & 0.21 & 0.23 & 0.28 & 0.22 & 0.28 & 0.22 & 0.24 & 0.50 & 0.27 & 0.19 & 0.24 & 0.25 \\
-phi3.5-mini-lora-essay-only-r16-onlyA & 0.31 & 0.42 & 0.42 & 0.15 & 0.22 & 0.23 & 0.19 & 0.26 & 0.16 & 0.28 & 0.47 & 0.31 & 0.14 & 0.16 & 0.25 \\
-phi3.5-mini-lora-essay-only-r16-onlyB & 0.36 & 0.38 & 0.49 & 0.28 & 0.29 & 0.33 & 0.29 & 0.30 & 0.27 & 0.30 & 0.53 & 0.24 & 0.23 & 0.31 & 0.26 \\
-\hline
-phi3.5-mini-lora-essay-only-r8-avg(A,B) & 0.33 & 0.40 & 0.45 & 0.21 & 0.26 & 0.28 & 0.24 & 0.28 & 0.22 & 0.29 & 0.50 & 0.27 & 0.19 & 0.24 & 0.25 \\
-phi3.5-mini-lora-essay-only-r8-concat(A,B) & 0.30 & 0.40 & 0.45 & 0.21 & 0.23 & 0.28 & 0.22 & 0.28 & 0.22 & 0.24 & 0.50 & 0.27 & 0.19 & 0.24 & 0.25 \\
-phi3.5-mini-lora-essay-only-r8-onlyA & 0.31 & 0.42 & 0.42 & 0.15 & 0.22 & 0.23 & 0.19 & 0.26 & 0.16 & 0.28 & 0.47 & 0.31 & 0.14 & 0.16 & 0.25 \\
-phi3.5-mini-lora-essay-only-r8-onlyB & 0.36 & 0.38 & 0.49 & 0.28 & 0.29 & 0.33 & 0.29 & 0.30 & 0.27 & 0.30 & 0.53 & 0.24 & 0.23 & 0.31 & 0.26 \\
-\hline
-phi3.5-mini-lora-full-context-r16-avg(A,B) & 0.19 & 0.29 & 0.28 & 0.14 & 0.22 & -0.03 & 0.28 & 0.35 & 0.44 & 0.11 & 0.20 & 0.08 & 0.18 & 0.19 & 0.19 \\
-phi3.5-mini-lora-full-context-r16-concat(A,B) & 0.17 & 0.29 & 0.28 & 0.14 & 0.21 & -0.02 & 0.26 & 0.34 & 0.45 & 0.09 & 0.20 & 0.08 & 0.19 & 0.18 & 0.19 \\
-phi3.5-mini-lora-full-context-r16-onlyA & 0.15 & 0.27 & 0.25 & 0.14 & 0.23 & 0.00 & 0.22 & 0.34 & 0.36 & 0.10 & 0.16 & 0.12 & 0.14 & 0.18 & 0.20 \\
-phi3.5-mini-lora-full-context-r16-onlyB & 0.23 & 0.31 & 0.31 & 0.14 & 0.20 & -0.06 & 0.34 & 0.37 & 0.52 & 0.12 & 0.23 & 0.04 & 0.21 & 0.20 & 0.18 \\
-\hline
-phi3.5-mini-lora-full-context-r8-avg(A,B) & 0.19 & 0.29 & 0.28 & 0.14 & 0.22 & -0.03 & 0.28 & 0.35 & 0.44 & 0.11 & 0.20 & 0.08 & 0.18 & 0.19 & 0.19 \\
-phi3.5-mini-lora-full-context-r8-concat(A,B) & 0.17 & 0.29 & 0.28 & 0.14 & 0.21 & -0.02 & 0.26 & 0.34 & 0.45 & 0.09 & 0.20 & 0.08 & 0.19 & 0.18 & 0.19 \\
-phi3.5-mini-lora-full-context-r8-onlyA & 0.15 & 0.27 & 0.25 & 0.14 & 0.23 & 0.00 & 0.22 & 0.34 & 0.36 & 0.10 & 0.16 & 0.12 & 0.14 & 0.18 & 0.20 \\
-phi3.5-mini-lora-full-context-r8-onlyB & 0.23 & 0.31 & 0.31 & 0.14 & 0.20 & -0.06 & 0.34 & 0.37 & 0.52 & 0.12 & 0.23 & 0.04 & 0.21 & 0.20 & 0.18 \\
-\hline
-phi4-lora-essay-only-r16-avg(A,B) & 0.53 & 0.66 & 0.70 & 0.19 & 0.25 & 0.21 & 0.25 & 0.34 & 0.41 & 0.38 & 0.63 & 0.58 & 0.36 & 0.42 & 0.65 \\
-phi4-lora-essay-only-r16-concat(A,B) & 0.48 & 0.65 & 0.70 & 0.20 & 0.23 & 0.21 & 0.23 & 0.34 & 0.42 & 0.31 & 0.63 & 0.58 & 0.37 & 0.42 & 0.65 \\
-phi4-lora-essay-only-r16-onlyA & 0.49 & 0.66 & 0.69 & 0.13 & 0.22 & 0.22 & 0.21 & 0.37 & 0.37 & 0.43 & 0.64 & 0.52 & 0.37 & 0.42 & 0.64 \\
-phi4-lora-essay-only-r16-onlyB & 0.58 & 0.65 & 0.70 & 0.26 & 0.29 & 0.20 & 0.29 & 0.32 & 0.46 & 0.33 & 0.63 & 0.65 & 0.35 & 0.43 & 0.66 \\
-\hline
-phi4-lora-essay-only-r8-avg(A,B) & 0.50 & 0.61 & 0.67 & 0.28 & 0.39 & 0.34 & 0.24 & 0.31 & 0.33 & 0.30 & 0.58 & 0.48 & 0.44 & 0.49 & 0.59 \\
-phi4-lora-essay-only-r8-concat(A,B) & 0.45 & 0.61 & 0.67 & 0.27 & 0.36 & 0.33 & 0.24 & 0.31 & 0.33 & 0.25 & 0.58 & 0.48 & 0.44 & 0.49 & 0.59 \\
-phi4-lora-essay-only-r8-onlyA & 0.49 & 0.63 & 0.68 & 0.19 & 0.32 & 0.29 & 0.24 & 0.36 & 0.30 & 0.31 & 0.60 & 0.47 & 0.44 & 0.49 & 0.60 \\
-phi4-lora-essay-only-r8-onlyB & 0.51 & 0.60 & 0.66 & 0.37 & 0.46 & 0.39 & 0.24 & 0.26 & 0.36 & 0.29 & 0.55 & 0.49 & 0.43 & 0.49 & 0.58 \\
-\hline
-phi4-lora-full-context-r16-avg(A,B) & 0.41 & 0.58 & 0.61 & 0.42 & 0.52 & 0.60 & 0.42 & 0.48 & 0.66 & 0.35 & 0.50 & 0.60 & 0.29 & 0.33 & 0.53 \\
-phi4-lora-full-context-r16-concat(A,B) & 0.38 & 0.57 & 0.61 & 0.40 & 0.50 & 0.60 & 0.39 & 0.47 & 0.66 & 0.29 & 0.50 & 0.60 & 0.30 & 0.32 & 0.53 \\
-phi4-lora-full-context-r16-onlyA & 0.44 & 0.60 & 0.62 & 0.36 & 0.59 & 0.56 & 0.32 & 0.46 & 0.66 & 0.34 & 0.47 & 0.55 & 0.24 & 0.29 & 0.50 \\
-phi4-lora-full-context-r16-onlyB & 0.38 & 0.55 & 0.61 & 0.47 & 0.46 & 0.65 & 0.51 & 0.51 & 0.66 & 0.36 & 0.54 & 0.64 & 0.33 & 0.37 & 0.56 \\
-\hline
-phi4-lora-full-context-r8-avg(A,B) & 0.48 & 0.63 & 0.67 & 0.40 & 0.50 & 0.59 & 0.37 & 0.38 & 0.57 & 0.37 & 0.58 & 0.55 & 0.29 & 0.32 & 0.52 \\
-phi4-lora-full-context-r8-concat(A,B) & 0.43 & 0.62 & 0.67 & 0.39 & 0.47 & 0.59 & 0.34 & 0.37 & 0.57 & 0.33 & 0.58 & 0.55 & 0.30 & 0.31 & 0.52 \\
-phi4-lora-full-context-r8-onlyA & 0.45 & 0.61 & 0.66 & 0.28 & 0.46 & 0.56 & 0.25 & 0.33 & 0.53 & 0.30 & 0.58 & 0.50 & 0.25 & 0.28 & 0.49 \\
-phi4-lora-full-context-r8-onlyB & 0.52 & 0.64 & 0.68 & 0.53 & 0.54 & 0.62 & 0.49 & 0.44 & 0.61 & 0.44 & 0.57 & 0.60 & 0.34 & 0.35 & 0.55 \\
-\hline
-tucano2b4-lora-essay-only-r16-avg(A,B) & 0.52 & 0.66 & 0.67 & 0.15 & 0.24 & 0.22 & 0.24 & 0.31 & 0.36 & 0.40 & 0.58 & 0.50 & 0.26 & 0.29 & 0.54 \\
-tucano2b4-lora-essay-only-r16-concat(A,B) & 0.46 & 0.66 & 0.67 & 0.17 & 0.22 & 0.22 & 0.24 & 0.30 & 0.36 & 0.39 & 0.58 & 0.50 & 0.27 & 0.28 & 0.54 \\
-tucano2b4-lora-essay-only-r16-onlyA & 0.42 & 0.62 & 0.62 & 0.15 & 0.23 & 0.20 & 0.20 & 0.28 & 0.33 & 0.21 & 0.52 & 0.38 & 0.23 & 0.29 & 0.52 \\
-tucano2b4-lora-essay-only-r16-onlyB & 0.63 & 0.70 & 0.72 & 0.16 & 0.25 & 0.24 & 0.28 & 0.34 & 0.39 & 0.59 & 0.64 & 0.62 & 0.30 & 0.29 & 0.55 \\
-\hline
-tucano2b4-lora-essay-only-r8-avg(A,B) & 0.52 & 0.64 & 0.66 & 0.14 & 0.22 & 0.23 & 0.25 & 0.35 & 0.36 & 0.34 & 0.57 & 0.49 & 0.25 & 0.30 & 0.58 \\
-tucano2b4-lora-essay-only-r8-concat(A,B) & 0.47 & 0.64 & 0.65 & 0.15 & 0.20 & 0.23 & 0.26 & 0.34 & 0.36 & 0.32 & 0.56 & 0.50 & 0.25 & 0.29 & 0.58 \\
-tucano2b4-lora-essay-only-r8-onlyA & 0.47 & 0.62 & 0.62 & 0.08 & 0.12 & 0.17 & 0.25 & 0.39 & 0.36 & 0.23 & 0.52 & 0.41 & 0.26 & 0.33 & 0.56 \\
-tucano2b4-lora-essay-only-r8-onlyB & 0.58 & 0.66 & 0.69 & 0.20 & 0.32 & 0.30 & 0.25 & 0.30 & 0.36 & 0.45 & 0.61 & 0.58 & 0.23 & 0.27 & 0.59 \\
-\hline
-tucano2b4-lora-full-context-r16-avg(A,B) & 0.40 & 0.59 & 0.54 & 0.19 & 0.27 & 0.24 & 0.22 & 0.27 & 0.24 & 0.39 & 0.59 & 0.52 & 0.25 & 0.29 & 0.43 \\
-tucano2b4-lora-full-context-r16-concat(A,B) & 0.40 & 0.59 & 0.54 & 0.19 & 0.25 & 0.23 & 0.21 & 0.26 & 0.24 & 0.40 & 0.59 & 0.52 & 0.25 & 0.29 & 0.42 \\
-tucano2b4-lora-full-context-r16-onlyA & 0.37 & 0.54 & 0.47 & 0.14 & 0.25 & 0.16 & 0.18 & 0.28 & 0.25 & 0.35 & 0.58 & 0.44 & 0.28 & 0.33 & 0.43 \\
-tucano2b4-lora-full-context-r16-onlyB & 0.43 & 0.64 & 0.61 & 0.24 & 0.29 & 0.31 & 0.26 & 0.26 & 0.22 & 0.43 & 0.61 & 0.60 & 0.21 & 0.26 & 0.42 \\
-\hline
-tucano2b4-lora-full-context-r8-avg(A,B) & 0.43 & 0.56 & 0.57 & 0.21 & 0.35 & 0.28 & 0.24 & 0.31 & 0.24 & 0.37 & 0.51 & 0.52 & 0.19 & 0.24 & 0.24 \\
-tucano2b4-lora-full-context-r8-concat(A,B) & 0.38 & 0.56 & 0.57 & 0.21 & 0.33 & 0.28 & 0.24 & 0.29 & 0.23 & 0.34 & 0.50 & 0.52 & 0.20 & 0.23 & 0.24 \\
-tucano2b4-lora-full-context-r8-onlyA & 0.39 & 0.55 & 0.57 & 0.22 & 0.41 & 0.24 & 0.20 & 0.35 & 0.30 & 0.28 & 0.47 & 0.45 & 0.20 & 0.23 & 0.21 \\
-tucano2b4-lora-full-context-r8-onlyB & 0.46 & 0.58 & 0.57 & 0.21 & 0.28 & 0.32 & 0.29 & 0.27 & 0.18 & 0.46 & 0.54 & 0.59 & 0.19 & 0.24 & 0.26 \\
-\hline\hline
-deepseekR1-extractor-essay\_only-avg(A,B) & 0.07 & 0.12 & 0.21 & 0.01 & 0.01 & -0.01 & 0.27 & 0.34 & 0.41 & 0.33 & 0.53 & 0.53 & 0.35 & 0.39 & 0.62 \\
-deepseekR1-extractor-essay\_only-concat(A,B) & 0.07 & 0.12 & 0.21 & 0.01 & 0.01 & -0.01 & 0.25 & 0.32 & 0.40 & 0.28 & 0.53 & 0.53 & 0.36 & 0.38 & 0.62 \\
-deepseekR1-extractor-essay\_only-onlyA & 0.07 & 0.10 & 0.22 & 0.00 & 0.00 & -0.01 & 0.20 & 0.36 & 0.46 & 0.30 & 0.47 & 0.46 & 0.41 & 0.44 & 0.60 \\
-deepseekR1-extractor-essay\_only-onlyB & 0.07 & 0.14 & 0.20 & 0.02 & 0.03 & -0.01 & 0.34 & 0.32 & 0.36 & 0.36 & 0.58 & 0.60 & 0.29 & 0.34 & 0.64 \\
-deepseekR1-extractor-full\_context-avg(A,B) & 0.04 & 0.08 & 0.20 & 0.27 & 0.34 & 0.51 & 0.30 & 0.41 & 0.52 & 0.37 & 0.60 & 0.56 & 0.37 & 0.42 & 0.61 \\
-deepseekR1-extractor-full\_context-concat(A,B) & 0.03 & 0.08 & 0.21 & 0.29 & 0.31 & 0.51 & 0.32 & 0.40 & 0.52 & 0.31 & 0.59 & 0.56 & 0.38 & 0.42 & 0.61 \\
-deepseekR1-extractor-full\_context-onlyA & 0.02 & 0.05 & 0.24 & 0.31 & 0.39 & 0.45 & 0.27 & 0.41 & 0.51 & 0.36 & 0.57 & 0.49 & 0.38 & 0.42 & 0.58 \\
-deepseekR1-extractor-full\_context-onlyB & 0.06 & 0.12 & 0.17 & 0.23 & 0.30 & 0.57 & 0.33 & 0.40 & 0.54 & 0.38 & 0.62 & 0.62 & 0.36 & 0.43 & 0.64 \\
-\hline
-deepseekR1-graderPrompt-essay\_only-avg(A,B) & 0.17 & 0.25 & 0.44 & 0.05 & 0.05 & 0.03 & 0.19 & 0.19 & 0.39 & 0.21 & 0.25 & 0.48 & 0.31 & 0.34 & 0.55 \\
-deepseekR1-graderPrompt-essay\_only-concat(A,B) & 0.16 & 0.25 & 0.44 & 0.04 & 0.05 & 0.03 & 0.18 & 0.18 & 0.40 & 0.18 & 0.24 & 0.47 & 0.32 & 0.33 & 0.55 \\
-deepseekR1-graderPrompt-essay\_only-onlyA & 0.17 & 0.25 & 0.45 & 0.08 & 0.08 & 0.02 & 0.12 & 0.17 & 0.34 & 0.15 & 0.24 & 0.40 & 0.30 & 0.31 & 0.52 \\
-deepseekR1-graderPrompt-essay\_only-onlyB & 0.17 & 0.25 & 0.43 & 0.02 & 0.03 & 0.04 & 0.26 & 0.22 & 0.44 & 0.26 & 0.25 & 0.55 & 0.33 & 0.37 & 0.58 \\
-deepseekR1-graderPrompt-full\_context-avg(A,B) & 0.05 & 0.06 & 0.20 & 0.32 & 0.43 & 0.53 & 0.30 & 0.30 & 0.60 & 0.29 & 0.46 & 0.55 & 0.35 & 0.38 & 0.60 \\
-deepseekR1-graderPrompt-full\_context-concat(A,B) & 0.06 & 0.06 & 0.20 & 0.36 & 0.40 & 0.51 & 0.31 & 0.29 & 0.60 & 0.25 & 0.46 & 0.55 & 0.36 & 0.37 & 0.60 \\
-deepseekR1-graderPrompt-full\_context-onlyA & 0.09 & 0.09 & 0.20 & 0.32 & 0.43 & 0.41 & 0.26 & 0.25 & 0.59 & 0.29 & 0.42 & 0.52 & 0.32 & 0.35 & 0.54 \\
-deepseekR1-graderPrompt-full\_context-onlyB & 0.02 & 0.03 & 0.20 & 0.33 & 0.43 & 0.65 & 0.33 & 0.36 & 0.61 & 0.30 & 0.49 & 0.58 & 0.38 & 0.41 & 0.65 \\
-\hline
-deepseekR1-studentPrompt-essay\_only-avg(A,B) & 0.11 & 0.17 & 0.36 & 0.13 & 0.26 & -0.02 & 0.32 & 0.34 & 0.38 & 0.27 & 0.39 & 0.52 & 0.37 & 0.43 & 0.55 \\
-deepseekR1-studentPrompt-essay\_only-concat(A,B) & 0.10 & 0.17 & 0.36 & 0.15 & 0.25 & -0.01 & 0.29 & 0.33 & 0.38 & 0.26 & 0.39 & 0.52 & 0.37 & 0.43 & 0.55 \\
-deepseekR1-studentPrompt-essay\_only-onlyA & 0.10 & 0.15 & 0.35 & 0.11 & 0.26 & -0.01 & 0.25 & 0.31 & 0.33 & 0.21 & 0.40 & 0.49 & 0.33 & 0.36 & 0.50 \\
-deepseekR1-studentPrompt-essay\_only-onlyB & 0.11 & 0.18 & 0.37 & 0.15 & 0.26 & -0.03 & 0.39 & 0.37 & 0.43 & 0.33 & 0.38 & 0.54 & 0.41 & 0.50 & 0.59 \\
-deepseekR1-studentPrompt-full\_context-avg(A,B) & 0.04 & 0.06 & 0.35 & 0.26 & 0.37 & 0.42 & 0.44 & 0.47 & 0.73 & 0.22 & 0.37 & 0.49 & 0.37 & 0.40 & 0.58 \\
-deepseekR1-studentPrompt-full\_context-concat(A,B) & 0.04 & 0.06 & 0.35 & 0.30 & 0.35 & 0.41 & 0.46 & 0.46 & 0.73 & 0.20 & 0.36 & 0.49 & 0.39 & 0.40 & 0.58 \\
-deepseekR1-studentPrompt-full\_context-onlyA & 0.03 & 0.07 & 0.37 & 0.24 & 0.30 & 0.37 & 0.45 & 0.42 & 0.69 & 0.17 & 0.40 & 0.47 & 0.31 & 0.34 & 0.52 \\
-deepseekR1-studentPrompt-full\_context-onlyB & 0.05 & 0.06 & 0.34 & 0.28 & 0.43 & 0.47 & 0.43 & 0.53 & 0.77 & 0.28 & 0.33 & 0.51 & 0.43 & 0.46 & 0.64 \\
-\hline
-gpt4o-extractor-essay\_only-avg(A,B) & 0.25 & 0.51 & 0.55 & 0.12 & 0.14 & 0.15 & 0.23 & 0.28 & 0.39 & 0.34 & 0.48 & 0.56 & 0.26 & 0.28 & 0.51 \\
-gpt4o-extractor-essay\_only-concat(A,B) & 0.23 & 0.51 & 0.56 & 0.11 & 0.13 & 0.15 & 0.21 & 0.26 & 0.39 & 0.33 & 0.48 & 0.56 & 0.26 & 0.28 & 0.51 \\
-gpt4o-extractor-essay\_only-onlyA & 0.26 & 0.50 & 0.63 & 0.13 & 0.16 & 0.17 & 0.22 & 0.36 & 0.39 & 0.21 & 0.48 & 0.53 & 0.27 & 0.27 & 0.48 \\
-gpt4o-extractor-essay\_only-onlyB & 0.24 & 0.52 & 0.47 & 0.10 & 0.12 & 0.13 & 0.23 & 0.20 & 0.39 & 0.46 & 0.48 & 0.58 & 0.24 & 0.29 & 0.54 \\
-gpt4o-extractor-full\_context-avg(A,B) & 0.25 & 0.54 & 0.53 & 0.32 & 0.43 & 0.47 & 0.27 & 0.30 & 0.57 & 0.27 & 0.45 & 0.50 & 0.14 & 0.15 & 0.32 \\
-gpt4o-extractor-full\_context-concat(A,B) & 0.23 & 0.54 & 0.54 & 0.36 & 0.41 & 0.47 & 0.29 & 0.28 & 0.57 & 0.28 & 0.45 & 0.50 & 0.15 & 0.15 & 0.31 \\
-gpt4o-extractor-full\_context-onlyA & 0.25 & 0.54 & 0.58 & 0.35 & 0.49 & 0.44 & 0.29 & 0.34 & 0.55 & 0.20 & 0.44 & 0.49 & 0.14 & 0.14 & 0.33 \\
-gpt4o-extractor-full\_context-onlyB & 0.24 & 0.53 & 0.48 & 0.29 & 0.38 & 0.50 & 0.25 & 0.26 & 0.59 & 0.34 & 0.47 & 0.52 & 0.15 & 0.17 & 0.31 \\
-\hline
-gpt4o-graderPrompt-essay\_only-avg(A,B) & 0.21 & 0.45 & 0.49 & 0.16 & 0.22 & 0.28 & 0.20 & 0.27 & 0.28 & 0.29 & 0.39 & 0.51 & 0.32 & 0.35 & 0.54 \\
-gpt4o-graderPrompt-essay\_only-concat(A,B) & 0.19 & 0.45 & 0.49 & 0.20 & 0.19 & 0.29 & 0.18 & 0.26 & 0.28 & 0.26 & 0.39 & 0.51 & 0.32 & 0.34 & 0.54 \\
-gpt4o-graderPrompt-essay\_only-onlyA & 0.17 & 0.42 & 0.48 & 0.14 & 0.20 & 0.29 & 0.20 & 0.34 & 0.29 & 0.17 & 0.38 & 0.48 & 0.26 & 0.26 & 0.49 \\
-gpt4o-graderPrompt-essay\_only-onlyB & 0.25 & 0.47 & 0.50 & 0.17 & 0.24 & 0.28 & 0.19 & 0.21 & 0.28 & 0.40 & 0.40 & 0.54 & 0.37 & 0.43 & 0.59 \\
-gpt4o-graderPrompt-full\_context-avg(A,B) & 0.25 & 0.52 & 0.55 & 0.31 & 0.44 & 0.56 & 0.27 & 0.33 & 0.45 & 0.26 & 0.47 & 0.50 & 0.24 & 0.26 & 0.48 \\
-gpt4o-graderPrompt-full\_context-concat(A,B) & 0.23 & 0.52 & 0.55 & 0.34 & 0.41 & 0.56 & 0.26 & 0.32 & 0.45 & 0.24 & 0.47 & 0.50 & 0.25 & 0.26 & 0.48 \\
-gpt4o-graderPrompt-full\_context-onlyA & 0.19 & 0.45 & 0.55 & 0.37 & 0.52 & 0.56 & 0.23 & 0.37 & 0.47 & 0.19 & 0.44 & 0.46 & 0.23 & 0.22 & 0.44 \\
-gpt4o-graderPrompt-full\_context-onlyB & 0.31 & 0.59 & 0.56 & 0.24 & 0.37 & 0.57 & 0.31 & 0.29 & 0.44 & 0.33 & 0.50 & 0.54 & 0.26 & 0.31 & 0.51 \\
-\hline
-gpt4o-studentPrompt-essay\_only-avg(A,B) & 0.22 & 0.43 & 0.51 & 0.16 & 0.27 & 0.20 & 0.26 & 0.30 & 0.38 & 0.30 & 0.40 & 0.51 & 0.28 & 0.31 & 0.55 \\
-gpt4o-studentPrompt-essay\_only-concat(A,B) & 0.20 & 0.42 & 0.51 & 0.18 & 0.26 & 0.20 & 0.24 & 0.29 & 0.38 & 0.28 & 0.39 & 0.51 & 0.29 & 0.30 & 0.55 \\
-gpt4o-studentPrompt-essay\_only-onlyA & 0.19 & 0.40 & 0.51 & 0.16 & 0.31 & 0.19 & 0.22 & 0.30 & 0.36 & 0.18 & 0.39 & 0.46 & 0.25 & 0.25 & 0.52 \\
-gpt4o-studentPrompt-essay\_only-onlyB & 0.24 & 0.45 & 0.50 & 0.15 & 0.24 & 0.20 & 0.30 & 0.29 & 0.40 & 0.42 & 0.40 & 0.55 & 0.31 & 0.37 & 0.57 \\
-gpt4o-studentPrompt-full\_context-avg(A,B) & 0.21 & 0.42 & 0.48 & 0.34 & 0.43 & 0.52 & 0.27 & 0.30 & 0.53 & 0.26 & 0.37 & 0.49 & 0.17 & 0.19 & 0.35 \\
-gpt4o-studentPrompt-full\_context-concat(A,B) & 0.18 & 0.42 & 0.48 & 0.38 & 0.41 & 0.51 & 0.25 & 0.29 & 0.53 & 0.24 & 0.37 & 0.49 & 0.17 & 0.19 & 0.35 \\
-gpt4o-studentPrompt-full\_context-onlyA & 0.15 & 0.36 & 0.48 & 0.36 & 0.48 & 0.47 & 0.26 & 0.36 & 0.50 & 0.17 & 0.37 & 0.43 & 0.14 & 0.16 & 0.37 \\
-gpt4o-studentPrompt-full\_context-onlyB & 0.26 & 0.48 & 0.48 & 0.31 & 0.37 & 0.57 & 0.28 & 0.25 & 0.56 & 0.35 & 0.37 & 0.55 & 0.21 & 0.22 & 0.34 \\
-\hline
-sabia3-extractor-essay\_only-avg(A,B) & 0.09 & 0.25 & 0.29 & 0.13 & 0.19 & 0.18 & 0.11 & 0.19 & 0.11 & 0.19 & 0.36 & 0.45 & 0.21 & 0.22 & 0.52 \\
-sabia3-extractor-essay\_only-concat(A,B) & 0.09 & 0.25 & 0.29 & 0.12 & 0.17 & 0.18 & 0.11 & 0.17 & 0.10 & 0.17 & 0.36 & 0.45 & 0.21 & 0.21 & 0.52 \\
-sabia3-extractor-essay\_only-onlyA & 0.09 & 0.24 & 0.29 & 0.15 & 0.23 & 0.20 & 0.11 & 0.29 & 0.16 & 0.13 & 0.30 & 0.44 & 0.21 & 0.20 & 0.52 \\
-sabia3-extractor-essay\_only-onlyB & 0.10 & 0.26 & 0.30 & 0.11 & 0.15 & 0.17 & 0.11 & 0.09 & 0.05 & 0.26 & 0.43 & 0.47 & 0.21 & 0.23 & 0.52 \\
-sabia3-extractor-full\_context-avg(A,B) & 0.10 & 0.26 & 0.31 & 0.16 & 0.24 & 0.28 & 0.20 & 0.29 & 0.21 & 0.18 & 0.38 & 0.38 & 0.22 & 0.24 & 0.51 \\
-sabia3-extractor-full\_context-concat(A,B) & 0.09 & 0.26 & 0.31 & 0.15 & 0.21 & 0.28 & 0.19 & 0.27 & 0.21 & 0.17 & 0.38 & 0.38 & 0.23 & 0.23 & 0.52 \\
-sabia3-extractor-full\_context-onlyA & 0.09 & 0.24 & 0.29 & 0.21 & 0.34 & 0.29 & 0.20 & 0.37 & 0.24 & 0.19 & 0.42 & 0.35 & 0.22 & 0.25 & 0.46 \\
-sabia3-extractor-full\_context-onlyB & 0.11 & 0.28 & 0.33 & 0.12 & 0.14 & 0.27 & 0.20 & 0.21 & 0.19 & 0.18 & 0.35 & 0.40 & 0.21 & 0.23 & 0.56 \\
-\hline
-sabia3-graderPrompt-essay\_only-avg(A,B) & 0.23 & 0.42 & 0.53 & 0.11 & 0.12 & 0.06 & 0.28 & 0.33 & 0.34 & 0.24 & 0.43 & 0.55 & 0.33 & 0.38 & 0.59 \\
-sabia3-graderPrompt-essay\_only-concat(A,B) & 0.21 & 0.42 & 0.53 & 0.09 & 0.11 & 0.06 & 0.25 & 0.32 & 0.34 & 0.22 & 0.43 & 0.55 & 0.33 & 0.37 & 0.59 \\
-sabia3-graderPrompt-essay\_only-onlyA & 0.23 & 0.44 & 0.51 & 0.15 & 0.10 & 0.09 & 0.22 & 0.34 & 0.30 & 0.15 & 0.42 & 0.50 & 0.26 & 0.30 & 0.54 \\
-sabia3-graderPrompt-essay\_only-onlyB & 0.22 & 0.40 & 0.55 & 0.07 & 0.13 & 0.03 & 0.33 & 0.33 & 0.38 & 0.32 & 0.45 & 0.60 & 0.41 & 0.46 & 0.63 \\
-sabia3-graderPrompt-full\_context-avg(A,B) & 0.24 & 0.50 & 0.50 & 0.33 & 0.43 & 0.50 & 0.33 & 0.37 & 0.54 & 0.31 & 0.49 & 0.53 & 0.28 & 0.32 & 0.50 \\
-sabia3-graderPrompt-full\_context-concat(A,B) & 0.23 & 0.50 & 0.50 & 0.37 & 0.41 & 0.49 & 0.37 & 0.36 & 0.54 & 0.29 & 0.49 & 0.53 & 0.29 & 0.31 & 0.50 \\
-sabia3-graderPrompt-full\_context-onlyA & 0.23 & 0.50 & 0.48 & 0.37 & 0.45 & 0.47 & 0.37 & 0.39 & 0.53 & 0.22 & 0.52 & 0.53 & 0.29 & 0.36 & 0.51 \\
-sabia3-graderPrompt-full\_context-onlyB & 0.26 & 0.51 & 0.51 & 0.29 & 0.41 & 0.52 & 0.29 & 0.35 & 0.55 & 0.40 & 0.47 & 0.53 & 0.27 & 0.28 & 0.48 \\
-\hline
-sabia3-studentPrompt-essay\_only-avg(A,B) & 0.35 & 0.66 & 0.69 & 0.11 & 0.11 & 0.01 & 0.23 & 0.32 & 0.30 & 0.31 & 0.53 & 0.52 & 0.29 & 0.35 & 0.51 \\
-sabia3-studentPrompt-essay\_only-concat(A,B) & 0.32 & 0.66 & 0.69 & 0.08 & 0.11 & 0.02 & 0.22 & 0.30 & 0.30 & 0.29 & 0.52 & 0.52 & 0.30 & 0.34 & 0.51 \\
-sabia3-studentPrompt-essay\_only-onlyA & 0.31 & 0.61 & 0.66 & 0.14 & 0.08 & 0.04 & 0.20 & 0.41 & 0.27 & 0.23 & 0.56 & 0.54 & 0.29 & 0.32 & 0.47 \\
-sabia3-studentPrompt-essay\_only-onlyB & 0.40 & 0.72 & 0.72 & 0.08 & 0.14 & -0.02 & 0.25 & 0.23 & 0.33 & 0.38 & 0.49 & 0.49 & 0.30 & 0.38 & 0.55 \\
-sabia3-studentPrompt-full\_context-avg(A,B) & 0.35 & 0.60 & 0.58 & 0.28 & 0.42 & 0.47 & 0.21 & 0.24 & 0.45 & 0.28 & 0.43 & 0.39 & 0.27 & 0.28 & 0.54 \\
-sabia3-studentPrompt-full\_context-concat(A,B) & 0.32 & 0.60 & 0.57 & 0.31 & 0.40 & 0.46 & 0.23 & 0.22 & 0.45 & 0.25 & 0.43 & 0.39 & 0.27 & 0.28 & 0.53 \\
-sabia3-studentPrompt-full\_context-onlyA & 0.33 & 0.58 & 0.54 & 0.30 & 0.38 & 0.35 & 0.23 & 0.28 & 0.43 & 0.18 & 0.46 & 0.41 & 0.25 & 0.25 & 0.54 \\
-sabia3-studentPrompt-full\_context-onlyB & 0.37 & 0.62 & 0.62 & 0.26 & 0.46 & 0.59 & 0.18 & 0.19 & 0.47 & 0.38 & 0.41 & 0.36 & 0.30 & 0.31 & 0.53 \\
-\hline\hline
-\end{tabular}%

runs/api_models/compute_bootstrap_ci.py CHANGED Viewed

@@ -16,7 +16,7 @@ def extract_config_from_log(log_path: Path) -> Dict:
     Parse the experiment configuration from run_experiment.log file.
     The config is in YAML format at the beginning of the log file.
     """
-    with open(log_path, 'r', encoding="latin1") as f:
         lines = f.readlines()
     # Find the start of the YAML config (after the first log line)
@@ -58,12 +58,9 @@ def load_inference_results_by_grader(jsonl_path: Path) -> Tuple[Dict[str, Dict],
         for line in f:
             data = json.loads(line.strip())
             essay_id = (data['id'], data['id_prompt'], data['essay_text'])
-            # Determine prediction field based on model type
-            model_types = ["gpt", "sabia", "deepseek"]
-            prediction_field = "pontuacao" if any(model in jsonl_path.name for model in model_types) else "prediction"
-            prediction = data[prediction_field]
             essay_data = {
-                'prediction': prediction,
                 'label': data['label']
             }

     Parse the experiment configuration from run_experiment.log file.
     The config is in YAML format at the beginning of the log file.
     """
+    with open(log_path, 'r') as f:
         lines = f.readlines()
     # Find the start of the YAML config (after the first log line)
         for line in f:
             data = json.loads(line.strip())
             essay_id = (data['id'], data['id_prompt'], data['essay_text'])
             essay_data = {
+                'prediction': data['pontuacao'],
                 'label': data['label']
             }

runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C1-essay_only/.hydra/config.yaml DELETED Viewed

@@ -1,33 +0,0 @@
-cache_dir: /tmp/
-dataset:
-  name: kamel-usp/aes_enem_dataset
-  split: JBCS2025
-training_params:
-  seed: 42
-  num_train_epochs: 20
-  logging_steps: 100
-  metric_for_best_model: QWK
-  bf16: true
-bootstrap:
-  enabled: true
-  n_bootstrap: 10000
-  bootstrap_seed: 42
-  metrics:
-  - QWK
-  - Macro_F1
-  - Weighted_F1
-post_training_results:
-  model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
-experiments:
-  model:
-    name: deepseek-reasoner
-    type: deepseek_r1
-    api_url: https://api.deepseek.com
-    prompt_type: zero-shot
-    stop:
-    - <|end_of_sequence|>
-    number_repetition_eval: 10
-  dataset:
-    grade_index: 0
-    use_full_context: false
-    training_id: deepseek-extractionPrompt-essay-only-zero-shot-C1

runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C1-essay_only/.hydra/hydra.yaml DELETED Viewed

@@ -1,155 +0,0 @@
-hydra:
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-    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}
-  sweep:
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-  sweeper:
-    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
-    max_batch_size: null
-    params: null
-  help:
-    app_name: ${hydra.job.name}
-    header: '${hydra.help.app_name} is powered by Hydra.
-      '
-    footer: 'Powered by Hydra (https://hydra.cc)
-      Use --hydra-help to view Hydra specific help
-      '
-    template: '${hydra.help.header}
-      == Configuration groups ==
-      Compose your configuration from those groups (group=option)
-      $APP_CONFIG_GROUPS
-      == Config ==
-      Override anything in the config (foo.bar=value)
-      $CONFIG
-      ${hydra.help.footer}
-      '
-  hydra_help:
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-      See https://hydra.cc for more info.
-      == Flags ==
-      $FLAGS_HELP
-      == Configuration groups ==
-      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
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-      Use ''--cfg hydra'' to Show the Hydra config.
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-    hydra_help: ???
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-  runtime:
-    version: 1.3.2
-    version_base: '1.1'
-    cwd: C:\Users\Igor\Documents\jbcs2025-u-extractor
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-      schema: pkg
-      provider: hydra
-    - path: C:\Users\Igor\Documents\jbcs2025-u-extractor\configs
-      schema: file
-      provider: main
-    - path: ''
-      schema: structured
-      provider: schema
-    output_dir: C:\Users\Igor\Documents\jbcs2025-u-extractor\outputs\2025-07-12\17-02-43
-    choices:
-      experiments: api_models_reasoning/C1
-      hydra/env: default
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-      hydra/job_logging: default
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-      hydra/hydra_help: default
-      hydra/help: default
-      hydra/sweeper: basic
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runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C1-essay_only/.hydra/overrides.yaml DELETED Viewed

	@@ -1 +0,0 @@
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runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C1-essay_only/deepseek-reasoner-extractor-zero-shot-C1-essay_only_inference_results.jsonl DELETED Viewed

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runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C1-full_context/.hydra/config.yaml DELETED Viewed

@@ -1,32 +0,0 @@
-cache_dir: /tmp/
-dataset:
-  name: kamel-usp/aes_enem_dataset
-  split: JBCS2025
-training_params:
-  seed: 42
-  num_train_epochs: 20
-  logging_steps: 100
-  metric_for_best_model: QWK
-  bf16: true
-bootstrap:
-  enabled: true
-  n_bootstrap: 10000
-  bootstrap_seed: 42
-  metrics:
-  - QWK
-  - Macro_F1
-  - Weighted_F1
-post_training_results:
-  model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
-experiments:
-  model:
-    name: deepseek-reasoner
-    type: deepseek_r1
-    api_url: https://api.deepseek.com
-    prompt_type: zero-shot
-    stop:
-    - <|end_of_sequence|>
-    number_repetition_eval: 10
-  dataset:
-    grade_index: 0
-    use_full_context: true

runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C1-full_context/.hydra/hydra.yaml DELETED Viewed

@@ -1,155 +0,0 @@
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-    max_batch_size: null
-    params: null
-  help:
-    app_name: ${hydra.job.name}
-    header: '${hydra.help.app_name} is powered by Hydra.
-      '
-    footer: 'Powered by Hydra (https://hydra.cc)
-      Use --hydra-help to view Hydra specific help
-      '
-    template: '${hydra.help.header}
-      == Configuration groups ==
-      Compose your configuration from those groups (group=option)
-      $APP_CONFIG_GROUPS
-      == Config ==
-      Override anything in the config (foo.bar=value)
-      $CONFIG
-      ${hydra.help.footer}
-      '
-  hydra_help:
-    template: 'Hydra (${hydra.runtime.version})
-      See https://hydra.cc for more info.
-      == Flags ==
-      $FLAGS_HELP
-      == Configuration groups ==
-      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
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-      $HYDRA_CONFIG_GROUPS
-      Use ''--cfg hydra'' to Show the Hydra config.
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-    hydra_help: ???
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-      file:
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-    root:
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-    output_dir: C:\Users\Igor\Documents\jbcs2025-u-extractor\outputs\2025-07-07\18-40-41
-    choices:
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runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C1-full_context/.hydra/overrides.yaml DELETED Viewed

	@@ -1 +0,0 @@
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runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C2-essay_only/.hydra/config.yaml DELETED Viewed

@@ -1,33 +0,0 @@
-cache_dir: /tmp/
-dataset:
-  name: kamel-usp/aes_enem_dataset
-  split: JBCS2025
-training_params:
-  seed: 42
-  num_train_epochs: 20
-  logging_steps: 100
-  metric_for_best_model: QWK
-  bf16: true
-bootstrap:
-  enabled: true
-  n_bootstrap: 10000
-  bootstrap_seed: 42
-  metrics:
-  - QWK
-  - Macro_F1
-  - Weighted_F1
-post_training_results:
-  model_path: /workspace/jbcs2025/outputs/2025-03-24/20-42-59
-experiments:
-  model:
-    name: deepseek-reasoner
-    type: deepseek_r1
-    api_url: https://api.deepseek.com
-    prompt_type: zero-shot
-    stop:
-    - <|end_of_sequence|>
-    number_repetition_eval: 10
-  dataset:
-    grade_index: 1
-    use_full_context: false
-    training_id: deepseek-extractionPrompt-essay-only-zero-shot-C2

runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C2-essay_only/.hydra/hydra.yaml DELETED Viewed

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-    footer: 'Powered by Hydra (https://hydra.cc)
-      Use --hydra-help to view Hydra specific help
-      '
-    template: '${hydra.help.header}
-      == Configuration groups ==
-      Compose your configuration from those groups (group=option)
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-      Override anything in the config (foo.bar=value)
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runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C2-essay_only/.hydra/overrides.yaml DELETED Viewed

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runs/api_models/deepseek-r1/deepseek-reasoner-extractor-zero-shot-C2-full_context/.hydra/config.yaml DELETED Viewed

@@ -1,32 +0,0 @@
-cache_dir: /tmp/
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-  name: kamel-usp/aes_enem_dataset
-  split: JBCS2025
-training_params:
-  seed: 42
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-    api_url: https://api.deepseek.com
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