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evaluation/README.md

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+## Overview
+
+The evaluation system consists of three main components:
+
+1. **`run_generation_hf.py`**: Runs inference for individual datasets
+2. **`get_scores.py`**: Modular evaluation script that calculates scores
+3. **`run_all_evaluation.py`**: Comprehensive wrapper for running full pipelines
+
+## Inference Step Customization
+
+**The inference step must be modified by users based on their specific model requirements.**
+
+As the model landscape continuously expands and evolves, the inference scripts provided are **reference implementations** that need to be adapted for your use case. Different models have different:
+- Loading mechanisms
+- Tokenization requirements
+- Generation parameters
+- API interfaces
+- Memory requirements
+
+### Sample Inference Implementations
+
+We provide two sample inference scripts - `run_generation_hf.py` and `run_generation_vllm.py`
+
+### How to Customize
+
+1. **Choose or create an inference script** that matches your model's requirements
+2. **Modify the model loading** section to work with your specific model
+3. **Adjust generation parameters** (temperature, top_p, max_tokens, etc.)
+4. **Update the prompt formatting** if your model uses a different template
+
+For comprehensive examples of different usage patterns, see **[`example_usage.sh`](./example_usage.sh)**, which includes:
+- Full pipeline execution
+- Inference-only runs
+- Evaluation-only runs
+
+**After generating predictions, the evaluation step (`get_scores.py`) remains the same across all models.**

evaluation/arguments.py

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+
+import argparse
+import os
+
+def get_args():
+    parser = argparse.ArgumentParser(description="ChatQA-HF")
+
+    ## model
+    parser.add_argument('--model-id', type=str, default='', help='model id')
+    parser.add_argument('--model-folder', type=str, default='', help='path to the folder containing the model')
+    parser.add_argument('--model-name', type=str, default='', help='name of the model')
+
+    ## dataset path
+    parser.add_argument('--data-folder', type=str, default='', help='path to the datafolder of ChatRAG Bench')
+    parser.add_argument('--output-folder', type=str, default='', help='path to the datafolder of ChatRAG Bench')
+    parser.add_argument('--eval-dataset', type=str, default='')
+    parser.add_argument('--doc2dial-path', type=str, default='doc2dial/test.json')
+    parser.add_argument('--convfinqa-path', type=str, default='convfinqa/test.json')
+    parser.add_argument('--quac-path', type=str, default='quac/test.json')
+    parser.add_argument('--qrecc-path', type=str, default='qrecc/test.json')
+    parser.add_argument('--doqa-cooking-path', type=str, default='doqa_cooking/test.json')
+    parser.add_argument('--doqa-travel-path', type=str, default='doqa_travel/test.json')
+    parser.add_argument('--doqa-movies-path', type=str, default='doqa_movies/test.json')
+    parser.add_argument('--hybridial-path', type=str, default='hybridial/test.json')
+    parser.add_argument('--inscit-path', type=str, default='inscit/test.json')
+
+    ## others
+    parser.add_argument('--out-seq-len', type=int, default=64)
+    parser.add_argument('--num-ctx', type=int, default=5)
+    parser.add_argument('--max-tokens', type=int, default=64)
+    parser.add_argument('--expected-samples', type=int, default=500, help='expected number of samples in dataset for completion check')
+    parser.add_argument('--stop-strings', type=str, nargs='+', default=["<|endoftext|>", "<extra_id_1>", "<extra_id_1>User"], help='stop strings for generation')
+    parser.add_argument('--device', type=str, default='cpu', help='device to run on: cpu or cuda')
+    parser.add_argument('--limit', type=int, default=None, help='limit the number of samples to process')
+
+    args = parser.parse_args()
+
+    return args
+
+def get_args_scores():
+    parser = argparse.ArgumentParser(description="ChatRAG Evaluation Scores")
+
+    # Directory paths
+    parser.add_argument('--results-dir', type=str, required=True, 
+                        help='Directory containing model prediction results (subdirectories for each model)')
+    parser.add_argument('--data-path', type=str, required=True,
+                        help='Path to ground truth data directory containing JSON files')
+    parser.add_argument('--output-csv', type=str, default=None,
+                        help='Output CSV file path (default: <results_dir>/scores.csv)')
+    
+    # Dataset options
+    parser.add_argument('--datasets', type=str, nargs='+',
+                        default=['doc2dial', 'quac', 'qrecc', 'topiocqa', 'inscit', 
+                                'coqa', 'hybridial', 'sqa', 'doqa_cooking', 
+                                'doqa_travel', 'doqa_movies', 'convfinqa'],
+                        help='List of datasets to evaluate')
+    
+    # Dataset file paths (relative to data-path)
+    parser.add_argument('--doc2dial-path', type=str, default='doc2dial/test.json')
+    parser.add_argument('--convfinqa-path', type=str, default='convfinqa/test.json')
+    parser.add_argument('--quac-path', type=str, default='quac/test.json')
+    parser.add_argument('--qrecc-path', type=str, default='qrecc/test.json')
+    parser.add_argument('--doqa-cooking-path', type=str, default='doqa_cooking/test.json')
+    parser.add_argument('--doqa-travel-path', type=str, default='doqa_travel/test.json')
+    parser.add_argument('--doqa-movies-path', type=str, default='doqa_movies/test.json')
+    parser.add_argument('--hybridial-path', type=str, default='hybridial/test.json')
+    parser.add_argument('--inscit-path', type=str, default='inscit/test.json')
+
+    args = parser.parse_args()
+    return args

evaluation/dataset.py

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+
+import json
+
+
+def load_data(datapath):
+    print("loading data from %s" % datapath)
+    with open(datapath, "r") as f:
+        data_list = json.load(f)
+
+    return data_list
+
+
+def reformat_question(turn_list, dataset_name):
+
+    ## only take the lastest 7 turns
+    turn_list = turn_list[-7:]
+    assert turn_list[-1]['role'] == 'user'
+
+    # ChatRAG-Hi available datasets - all use long answer format
+    long_answer_dataset_list = ["doc2dial", "quac", "qrecc", "inscit", "doqa_movies", "doqa_travel", "doqa_cooking", "hybridial", "convfinqa"]
+
+    if dataset_name in long_answer_dataset_list:
+        for item in turn_list:
+            if item['role'] == 'user':
+                ## only needs to add it on the first user turn
+                item['content'] = 'Please give a full and complete answer for the question. ' + item['content']
+                break
+    else:
+        raise Exception(f"Dataset '{dataset_name}' not supported in ChatRAG-Hi! Available datasets: {long_answer_dataset_list}")
+    
+    question = ""
+    for item in turn_list:
+        if item["role"] == "user":
+            question += "User: " + item["content"] + "\n\n"
+        else:
+            assert item["role"] == "assistant"
+            question += "Assistant: " + item["content"] + "\n\n"
+    
+    question += "Assistant:"
+    
+    return question
+
+
+def get_inputs(data_list, dataset_name, tokenizer, num_ctx, max_output_len, max_seq_length=4096):
+
+    system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context."
+
+    prompt_list = []
+    for item in data_list:
+        turn_list = item['messages']
+        question_formatted = reformat_question(turn_list, dataset_name)
+
+        ctx_list = ["title: " + ctx["title"] + ", source: " + ctx["text"] for ctx in item['ctxs'][:num_ctx]]
+        context = "\n\n".join(ctx_list)
+
+        context_tokens = tokenizer.encode(context)
+        question_tokens = tokenizer.encode(question_formatted)
+        system_tokens = tokenizer.encode(system)
+
+        if len(context_tokens) + len(question_tokens) + len(system_tokens) + max_output_len >= max_seq_length:
+            context_tokens = context_tokens[:max_seq_length - max_output_len - len(question_tokens) - len(system_tokens)]
+            context = tokenizer.decode(context_tokens, skip_special_tokens=True)
+
+        model_input = system + "\n\n" + context + "\n\n" + question_formatted
+
+        prompt_list.append(model_input)
+    
+    return prompt_list
+

evaluation/evaluation_utils.py

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+
+
+## a index list of the sample where the correct context is found in the top-5 retrieved contexts
+quac_correct_retrieved_instance_idx_list = [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 169, 170, 171, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 216, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 243, 245, 246, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 284, 285, 287, 289, 290, 291, 292, 293, 294, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 344, 345, 346, 347, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 362, 363, 364, 365, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 415, 417, 419, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 440, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 466, 467, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 484, 485, 486, 488, 489, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 509, 510, 511, 512, 514, 515, 518, 519, 520, 521, 522, 523, 524, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 540, 541, 542, 543, 544, 547, 548, 549, 550, 551, 552, 554, 555, 557, 558, 560, 561, 562, 563, 564, 565, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 608, 609, 610, 611, 612, 613, 614, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 637, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 652, 653, 655, 656, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 692, 693, 695, 699, 700, 701, 703, 704, 705, 707, 708, 709, 711, 712, 715, 716, 717, 718, 719, 720, 721, 723, 724, 726, 728, 732, 733, 734, 738, 739, 740, 741, 742, 743, 744, 748, 749, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 773, 774, 775, 776, 777, 778, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 804, 805, 806, 807, 809, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 822, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 848, 849, 850, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 876, 877, 878, 879, 880, 883, 884, 885, 886, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 938, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 972, 974, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 995, 998, 999, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1067, 1068, 1069, 1070, 1071, 1073, 1074, 1075, 1076, 1077, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1093, 1094, 1095, 1096, 1098, 1099, 1100, 1102, 1103, 1105, 1106, 1107, 1109, 1110, 1113, 1114, 1118, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1130, 1131, 1133, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1223, 1225, 1226, 1227, 1228, 1229, 1230, 1232, 1233, 1234, 1235, 1237, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1267, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1283, 1284, 1285, 1287, 1288, 1289, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1342, 1343, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1402, 1403, 1404, 1405, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1447, 1448, 1449, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1467, 1468, 1469, 1471, 1472, 1474, 1475, 1476, 1477, 1478, 1479, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1495, 1498, 1504, 1505, 1507, 1508, 1509, 1512, 1513, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1536, 1537, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1608, 1609, 1611, 1612, 1613, 1614, 1615, 1616, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1639, 1640, 1641, 1642, 1643, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1655, 1656, 1657, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1678, 1679, 1681, 1682, 1683, 1686, 1687, 1688, 1689, 1690, 1691, 1694, 1695, 1696, 1697, 1699, 1700, 1701, 1702, 1703, 1704, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1723, 1724, 1725, 1726, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1747, 1748, 1749, 1750, 1751, 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1956, 1957, 1958, 1959, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1975, 1976, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2014, 2015, 2017, 2018, 2019, 2020, 2021, 2023, 2025, 2026, 2027, 2028, 2029, 2033, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2073, 2074, 2075, 2078, 2079, 2083, 2084, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2109, 2110, 2112, 2113, 2114, 2115, 2116, 2117, 2120, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2132, 2134, 2138, 2139, 2140, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2174, 2177, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2191, 2192, 2193, 2196, 2198, 2199, 2201, 2202, 2205, 2206, 2207, 2209, 2212, 2213, 2218, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2242, 2243, 2244, 2245, 2246, 2247, 2248, 2249, 2250, 2255, 2256, 2257, 2258, 2259, 2260, 2261, 2262, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2281, 2282, 2283, 2284, 2285, 2287, 2288, 2289, 2290, 2291, 2292, 2293, 2294, 2295, 2299, 2301, 2302, 2303, 2304, 2305, 2306, 2308, 2309, 2311, 2312, 2313, 2314, 2315, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325, 2326, 2327, 2330, 2331, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2342, 2343, 2344, 2346, 2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357, 2358, 2361, 2362, 2364, 2365, 2366, 2367, 2368, 2369, 2370, 2371, 2379, 2384, 2385, 2386, 2387, 2388, 2389, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409, 2410, 2411, 2412, 2413, 2414, 2419, 2420, 2421, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2462, 2463, 2464, 2466, 2467, 2469, 2470, 2472, 2473, 2475, 2476, 2477, 2478, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506, 2507, 2508, 2509, 2511, 2512, 2514, 2515, 2516, 2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2541, 2542, 2543, 2544, 2545, 2546, 2548, 2551, 2552, 2553, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2572, 2573, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2584, 2585, 2586, 2587, 2588, 2589, 2590, 2591, 2593, 2594, 2595, 2596, 2599, 2600, 2601, 2606, 2607, 2608, 2609, 2610, 2611, 2612, 2613, 2615, 2616, 2618, 2619, 2620, 2623, 2624, 2625, 2626, 2627, 2628, 2629, 2630, 2631, 2633, 2634, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2646, 2647, 2648, 2649, 2650, 2651, 2652, 2653, 2655, 2656, 2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2666, 2667, 2668, 2669, 2670, 2671, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2684, 2687, 2688, 2691, 2692, 2693, 2694, 2695, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2719, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2737, 2738, 2739, 2740, 2741, 2742, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2768, 2769, 2771, 2773, 2774, 2775, 2776, 2777, 2778, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2822, 2823, 2824, 2825, 2826, 2827, 2829, 2830, 2831, 2832, 2834, 2835, 2836, 2837, 2838, 2839, 2841, 2842, 2843, 2845, 2846, 2848, 2849, 2850, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2893, 2894, 2895, 2896, 2897, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2922, 2923, 2924, 2925, 2926, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2940, 2943, 2944, 2945, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2957, 2959, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2998, 2999, 3000, 3001, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3037, 3040, 3041, 3043, 3044, 3045, 3046, 3047, 3049, 3050, 3051, 3052, 3053, 3054, 3055, 3056, 3057, 3058, 3059, 3060, 3061, 3062, 3063, 3064, 3065, 3066, 3067, 3068, 3069, 3070, 3071, 3072, 3073, 3074, 3075, 3076, 3077, 3078, 3079, 3080, 3082, 3083, 3084, 3086, 3087, 3089, 3090, 3091, 3093, 3094, 3095, 3096, 3097, 3098, 3099, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3110, 3111, 3115, 3116, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140, 3141, 3142, 3144, 3145, 3146, 3147, 3148, 3149, 3150, 3151, 3152, 3153, 3154, 3155, 3156, 3157, 3158, 3159, 3160, 3161, 3162, 3163, 3164, 3165, 3166, 3167, 3168, 3169, 3171, 3172, 3175, 3176, 3177, 3178, 3179, 3180, 3181, 3182, 3183, 3185, 3186, 3187, 3189, 3190, 3191, 3192, 3193, 3194, 3195, 3196, 3197, 3198, 3199, 3200, 3201, 3203, 3204, 3205, 3206, 3207, 3208, 3209, 3210, 3211, 3212, 3214, 3215, 3216, 3217, 3218, 3220, 3221, 3222, 3223, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3308, 3309, 3310, 3311, 3313, 3314, 3316, 3317, 3318, 3319, 3320, 3321, 3323, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 3346, 3351, 3353, 3354, 3355, 3356, 3359, 3363, 3365, 3366, 3367, 3368, 3369, 3370, 3371, 3372, 3373, 3374, 3375, 3377, 3379, 3380, 3381, 3382, 3383, 3384, 3387, 3388, 3389, 3390, 3391, 3392, 3393, 3394, 3395, 3401, 3402, 3403, 3406, 3407, 3408, 3409, 3410, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419, 3420, 3422, 3423, 3424, 3425, 3426, 3427, 3428, 3430, 3431, 3432, 3433, 3434, 3436, 3437, 3438, 3439, 3440, 3441, 3442, 3443, 3444, 3445, 3447, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3456, 3457, 3459, 3460, 3461, 3462, 3463, 3464, 3465, 3467, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3498, 3499, 3503, 3504, 3505, 3508, 3509, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3518, 3519, 3520, 3521, 3522, 3523, 3524, 3525, 3526, 3527, 3528, 3529, 3530, 3531, 3532, 3533, 3534, 3535, 3536, 3537, 3538, 3539, 3540, 3541, 3542, 3543, 3544, 3545, 3548, 3549, 3550, 3551, 3552, 3553, 3554, 3555, 3556, 3557, 3558, 3559, 3560, 3561, 3562, 3563, 3564, 3565, 3566, 3567, 3568, 3569, 3570, 3571, 3572, 3573, 3574, 3575, 3576, 3577, 3578, 3579, 3582, 3583, 3584, 3586, 3587, 3588, 3589, 3590, 3591, 3592, 3593, 3594, 3595, 3596, 3597, 3598, 3600, 3601, 3602, 3604, 3609, 3610, 3611, 3612, 3614, 3615, 3616, 3617, 3618, 3619, 3622, 3623, 3625, 3626, 3628, 3630, 3631, 3632, 3633, 3634, 3635, 3636, 3637, 3638, 3639, 3640, 3641, 3643, 3644, 3645, 3646, 3647, 3648, 3649, 3650, 3651, 3652, 3653, 3654, 3655, 3656, 3657, 3659, 3660, 3661, 3662, 3663, 3664, 3665, 3666, 3670, 3671, 3672, 3673, 3674, 3675, 3676, 3677, 3678, 3679, 3680, 3682, 3683, 3684, 3685, 3686, 3687, 3688, 3689, 3690, 3691, 3692, 3693, 3694, 3695, 3696, 3697, 3698, 3699, 3700, 3704, 3705, 3706, 3708, 3709, 3710, 3711, 3712, 3713, 3714, 3716, 3718, 3719, 3720, 3722, 3724, 3726, 3727, 3730, 3731, 3732, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741, 3742, 3744, 3745, 3746, 3747, 3748, 3749, 3753, 3754, 3755, 3756, 3757, 3758, 3759, 3760, 3762, 3763, 3764, 3765, 3766, 3770, 3771, 3772, 3773, 3774, 3775, 3776, 3777, 3778, 3779, 3780, 3781, 3782, 3783, 3784, 3785, 3786, 3787, 3788, 3790, 3791, 3792, 3793, 3794, 3795, 3796, 3797, 3798, 3799, 3800, 3802, 3803, 3804, 3805, 3806, 3807, 3808, 3809, 3810, 3811, 3812, 3813, 3814, 3815, 3816, 3817, 3818, 3819, 3820, 3821, 3822, 3826, 3828, 3829, 3830, 3831, 3833, 3834, 3835, 3836, 3837, 3838, 3839, 3840, 3841, 3842, 3843, 3845, 3846, 3850, 3851, 3852, 3853, 3854, 3856, 3857, 3858, 3859, 3860, 3862, 3863, 3864, 3865, 3866, 3867, 3868, 3869, 3871, 3873, 3874, 3876, 3877, 3878, 3879, 3881, 3882, 3886, 3887, 3888, 3889, 3890, 3891, 3892, 3893, 3894, 3895, 3896, 3897, 3898, 3899, 3900, 3901, 3902, 3903, 3904, 3905, 3906, 3907, 3908, 3911, 3912, 3913, 3915, 3916, 3917, 3918, 3919, 3921, 3922, 3923, 3924, 3925, 3926, 3927, 3929, 3930, 3931, 3932, 3933, 3934, 3935, 3938, 3939, 3940, 3941, 3943, 3944, 3945, 3946, 3947, 3948, 3949, 3950, 3951, 3952, 3953, 3954, 3956, 3957, 3958, 3959, 3960, 3963, 3965, 3969, 3970, 3971, 3972, 3973, 3974, 3975, 3976, 3978, 3981, 3982, 3983, 3984, 3985, 3986, 3987, 3988, 3989, 3990, 3991, 3992, 3995, 3996, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4040, 4043, 4045, 4047, 4049, 4050, 4051, 4052, 4053, 4054, 4055, 4056, 4057, 4059, 4060, 4064, 4065, 4066, 4067, 4073, 4074, 4075, 4076, 4077, 4078, 4079, 4080, 4081, 4082, 4083, 4084, 4085, 4086, 4087, 4088, 4089, 4090, 4091, 4092, 4093, 4094, 4095, 4096, 4097, 4098, 4099, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108, 4109, 4110, 4111, 4112, 4113, 4116, 4117, 4120, 4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131, 4132, 4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 4146, 4147, 4148, 4149, 4150, 4153, 4154, 4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4168, 4169, 4170, 4171, 4172, 4174, 4175, 4177, 4178, 4179, 4180, 4181, 4184, 4186, 4188, 4189, 4190, 4191, 4193, 4194, 4195, 4196, 4197, 4198, 4199, 4200, 4201, 4202, 4203, 4205, 4206, 4207, 4208, 4209, 4210, 4211, 4212, 4213, 4214, 4216, 4218, 4219, 4220, 4221, 4222, 4223, 4224, 4225, 4226, 4227, 4228, 4230, 4231, 4232, 4233, 4234, 4235, 4236, 4237, 4238, 4239, 4240, 4241, 4242, 4243, 4246, 4247, 4248, 4249, 4250, 4252, 4253, 4255, 4257, 4258, 4259, 4260, 4261, 4262, 4263, 4264, 4265, 4266, 4267, 4268, 4269, 4270, 4271, 4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4281, 4282, 4283, 4284, 4285, 4288, 4289, 4290, 4291, 4292, 4294, 4295, 4296, 4297, 4298, 4300, 4301, 4303, 4305, 4306, 4307, 4308, 4309, 4310, 4311, 4312, 4313, 4314, 4315, 4316, 4317, 4318, 4319, 4320, 4321, 4322, 4323, 4324, 4325, 4326, 4327, 4329, 4330, 4331, 4332, 4333, 4334, 4335, 4339, 4340, 4341, 4342, 4343, 4345, 4346, 4347, 4349, 4350, 4352, 4353, 4355, 4356, 4357, 4358, 4359, 4360, 4361, 4362, 4363, 4364, 4365, 4366, 4367, 4368, 4370, 4371, 4372, 4373, 4374, 4376, 4377, 4378, 4379, 4380, 4381, 4382, 4383, 4384, 4385, 4387, 4388, 4389, 4390, 4391, 4392, 4393, 4394, 4395, 4396, 4397, 4398, 4400, 4402, 4403, 4404, 4405, 4406, 4407, 4408, 4409, 4411, 4412, 4413, 4414, 4415, 4416, 4417, 4418, 4419, 4420, 4421, 4422, 4423, 4424, 4425, 4426, 4427, 4428, 4429, 4430, 4431, 4432, 4433, 4434, 4435, 4436, 4437, 4438, 4439, 4440, 4442, 4444, 4445, 4446, 4447, 4449, 4450, 4451, 4452, 4453, 4454, 4455, 4456, 4457, 4458, 4462, 4463, 4464, 4465, 4466, 4467, 4469, 4470, 4471, 4472, 4473, 4474, 4475, 4476, 4477, 4478, 4479, 4480, 4481, 4482, 4483, 4484, 4485, 4486, 4487, 4488, 4489, 4490, 4491, 4492, 4493, 4494, 4495, 4496, 4497, 4498, 4499, 4500, 4501, 4502, 4505, 4506, 4507, 4508, 4509, 4510, 4511, 4512, 4513, 4514, 4515, 4516, 4517, 4518, 4519, 4520, 4521, 4522, 4523, 4524, 4525, 4526, 4527, 4528, 4529, 4530, 4531, 4532, 4533, 4534, 4535, 4536, 4537, 4538, 4539, 4540, 4541, 4542, 4543, 4544, 4545, 4546, 4547, 4548, 4552, 4553, 4554, 4555, 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4756, 4757, 4759, 4761, 4763, 4764, 4765, 4766, 4767, 4768, 4769, 4770, 4771, 4772, 4773, 4774, 4775, 4776, 4777, 4778, 4779, 4780, 4781, 4782, 4783, 4784, 4785, 4786, 4787, 4788, 4789, 4790, 4795, 4796, 4797, 4798, 4799, 4800, 4801, 4802, 4803, 4804, 4805, 4806, 4807, 4808, 4809, 4810, 4811, 4812, 4815, 4816, 4817, 4818, 4819, 4820, 4821, 4822, 4823, 4825, 4826, 4827, 4829, 4830, 4831, 4833, 4834, 4835, 4836, 4837, 4838, 4840, 4841, 4842, 4843, 4844, 4846, 4847, 4848, 4849, 4850, 4851, 4852, 4853, 4854, 4855, 4856, 4857, 4858, 4859, 4860, 4861, 4862, 4865, 4866, 4867, 4869, 4870, 4872, 4873, 4874, 4875, 4876, 4877, 4878, 4881, 4882, 4885, 4886, 4888, 4890, 4891, 4892, 4893, 4894, 4896, 4897, 4898, 4900, 4901, 4904, 4905, 4906, 4907, 4909, 4910, 4911, 4912, 4913, 4914, 4915, 4916, 4917, 4918, 4919, 4920, 4921, 4923, 4924, 4925, 4926, 4927, 4928, 4929, 4930, 4931, 4932, 4933, 4934, 4935, 4936, 4937, 4938, 4939, 4940, 4941, 4942, 4943, 4944, 4945, 4946, 4947, 4948, 4949, 4950, 4951, 4953, 4954, 4955, 4956, 4957, 4958, 4960, 4963, 4964, 4965, 4966, 4967, 4968, 4969, 4970, 4971, 4972, 4973, 4974, 4977, 4978, 4979, 4981, 4982, 4983, 4984, 4985, 4988, 4989, 4993, 4994, 4995, 4996, 4997, 4998, 4999, 5000, 5001, 5002, 5003, 5004, 5005, 5006, 5010, 5011, 5016, 5019, 5020, 5021, 5022, 5023, 5024, 5025, 5026, 5027, 5028, 5029, 5030, 5031, 5032, 5033, 5034, 5035, 5036, 5037, 5038, 5039, 5040, 5041, 5044, 5045, 5046, 5047, 5048, 5049, 5051, 5052, 5053, 5054, 5055, 5057, 5058, 5059, 5060, 5061, 5062, 5063, 5064, 5065, 5066, 5067, 5068, 5069, 5070, 5071, 5072, 5073, 5074, 5075, 5076, 5077, 5080, 5081, 5082, 5083, 5084, 5085, 5086, 5087, 5088, 5090, 5091, 5092, 5093, 5094, 5095, 5096, 5097, 5098, 5099, 5100, 5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5113, 5115, 5118, 5120, 5123, 5124, 5126, 5127, 5128, 5129, 5130, 5134, 5135, 5136, 5137, 5138, 5139, 5140, 5141, 5142, 5143, 5144, 5145, 5146, 5147, 5148, 5149, 5150, 5151, 5152, 5153, 5154, 5155, 5156, 5157, 5158, 5162, 5163, 5164, 5165, 5166, 5167, 5168, 5170, 5171, 5172, 5173, 5174, 5175, 5176, 5177, 5178, 5179, 5180, 5181, 5182, 5183, 5185, 5186, 5187, 5188, 5189, 5191, 5192, 5193, 5194, 5195, 5196, 5197, 5198, 5199, 5200, 5201, 5202, 5203, 5204, 5205, 5206, 5208, 5210, 5211, 5212, 5213, 5214, 5215, 5216, 5217, 5218, 5221, 5222, 5223, 5224, 5225, 5226, 5227, 5228, 5229, 5230, 5231, 5232, 5233, 5234, 5235, 5237, 5240, 5241, 5244, 5245, 5246, 5247, 5248, 5249, 5250, 5251, 5252, 5253, 5254, 5255, 5256, 5257, 5258, 5260, 5261, 5262, 5263, 5264, 5265, 5266, 5267, 5268, 5269, 5270, 5271, 5272, 5273, 5278, 5280, 5281, 5282, 5283, 5284, 5285, 5286, 5287, 5288, 5289, 5290, 5291, 5293, 5294, 5302, 5303, 5304, 5305, 5306, 5307, 5308, 5309, 5310, 5311, 5312, 5313, 5314, 5315, 5316, 5317, 5318, 5319, 5320, 5321, 5322, 5323, 5324, 5325, 5326, 5327, 5328, 5338, 5340, 5342, 5343, 5344, 5345, 5346, 5347, 5348, 5349, 5350, 5351, 5352, 5353, 5354, 5356, 5357, 5358, 5360, 5361, 5362, 5363, 5364, 5365, 5366, 5367, 5369, 5370, 5371, 5372, 5373, 5374, 5375, 5376, 5377, 5378, 5379, 5380, 5381, 5382, 5383, 5384, 5386, 5389, 5390, 5393, 5394, 5395, 5396, 5397, 5399, 5400, 5401, 5402, 5404, 5405, 5408, 5412, 5413, 5414, 5415, 5416, 5417, 5418, 5419, 5421, 5422, 5423, 5424, 5425, 5426, 5427, 5429, 5430, 5431, 5432, 5433, 5434, 5435, 5436, 5439, 5441, 5442, 5443, 5444, 5445, 5446, 5447, 5449, 5450, 5451, 5452, 5453, 5454, 5455, 5456, 5457, 5458, 5459, 5460, 5461, 5462, 5463, 5464, 5465, 5466, 5467, 5470, 5471, 5472, 5473, 5475, 5476, 5477, 5478, 5479, 5480, 5483, 5484, 5485, 5486, 5487, 5488, 5489, 5490, 5491, 5492, 5493, 5494, 5496, 5497, 5499, 5500, 5501, 5502, 5503, 5504, 5505, 5506, 5507, 5508, 5509, 5510, 5511, 5512, 5513, 5514, 5515, 5516, 5517, 5518, 5520, 5521, 5522, 5523, 5524, 5525, 5526, 5527, 5528, 5529, 5530, 5531, 5532, 5533, 5534, 5535, 5536, 5537, 5538, 5539, 5540, 5541, 5542, 5543, 5544, 5546, 5547, 5548, 5549, 5550, 5551, 5552, 5553, 5554, 5555, 5556, 5557, 5558, 5559, 5560, 5561, 5563, 5564, 5565, 5566, 5567, 5568, 5569, 5571, 5573, 5574, 5576, 5578, 5579, 5580, 5581, 5582, 5583, 5584, 5589, 5590, 5591, 5593, 5594, 5595, 5596, 5597, 5598, 5599, 5600, 5601, 5602, 5603, 5604, 5605, 5606, 5607, 5608, 5609, 5611, 5612, 5613, 5614, 5616, 5617, 5618, 5619, 5620, 5622, 5623, 5624, 5625, 5626, 5627, 5628, 5629, 5630, 5631, 5632, 5633, 5634, 5635, 5636, 5637, 5638, 5639, 5640, 5641, 5642, 5643, 5644, 5646, 5647, 5648, 5649, 5650, 5651, 5652, 5653, 5654, 5655, 5656, 5657, 5658, 5659, 5660, 5661, 5662, 5663, 5664, 5665, 5666, 5667, 5668, 5669, 5670, 5671, 5673, 5674, 5675, 5676, 5677, 5678, 5679, 5680, 5681, 5682, 5683, 5684, 5685, 5687, 5688, 5689, 5690, 5691, 5692, 5693, 5700, 5701, 5702, 5703, 5704, 5705, 5706, 5708, 5709, 5710, 5711, 5712, 5713, 5714, 5717, 5718, 5719, 5720, 5721, 5722, 5723, 5724, 5725, 5726, 5727, 5728, 5734, 5736, 5737, 5738, 5739, 5740, 5741, 5742, 5743, 5744, 5745, 5746, 5747, 5748, 5749, 5750, 5751, 5752, 5753, 5754, 5755, 5756, 5757, 5758, 5759, 5760, 5761, 5762, 5763, 5764, 5765, 5766, 5767, 5768, 5769, 5771, 5772, 5776, 5777, 5778, 5779, 5780, 5781, 5782, 5783, 5784, 5785, 5786, 5787, 5788, 5789, 5790, 5791, 5792, 5794, 5795, 5796, 5797, 5798, 5799, 5801, 5802, 5804, 5807, 5808, 5809, 5813, 5814, 5815, 5816, 5819, 5821, 5822, 5823, 5824, 5825, 5826, 5827, 5828, 5829, 5832, 5834, 5835, 5836, 5837, 5839, 5840, 5841, 5842, 5843, 5844, 5845, 5846, 5847, 5848, 5850, 5851, 5852, 5853, 5856, 5859, 5862, 5863, 5864, 5865]
+
+
+unanswerable_keyphrases = ["cannot find", "can't find", "not able to", "unable to", "does not provide", "cannot provide", "cannot answer", "couldnot answer", "can't answer", "couldn't answer", "cannot be found", "cannot be determined", "do not have", "couldn't find", "no information", "does not mention", "doesn't mention", "not explicitly mentioned", "not explicitly explain", "can not find", "could not find", "does not specify", "doesn't provide", "doesn't specify", "there is no", "not mentioned", "don't have", "don't know", "does not include", "doesn't include", "does not contain", "doesn't contain", "not provided", "does not indicate", "doesn't indicate", "does not disclose", "doesn't disclose"]

evaluation/example_usage.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+
+MODEL_ID="nvidia/Nemotron-4-Mini-Hindi-4B-Instruct"
+DATA_FOLDER="ChatRAG-Hi/data"
+OUTPUT_FOLDER="ChatRAG-Hi/results"
+
+echo "Running full pipeline for all datasets"
+python run_all_evaluation.py \
+    --mode full \
+    --model-id "$MODEL_ID" \
+    --data-folder "$DATA_FOLDER" \
+    --output-folder "$OUTPUT_FOLDER" \
+    --all-datasets \
+    --device cuda \
+    --num-ctx 5 \
+    --max-tokens 64 \
+    --limit 10 
+
+echo "Evaluate specific dataset subset"
+python run_all_evaluation.py \
+    --mode full \
+    --model-id "$MODEL_ID" \
+    --data-folder "$DATA_FOLDER" \
+    --output-folder "$OUTPUT_FOLDER" \
+    --datasets doc2dial quac inscit \
+    --device cuda
+
+echo "Evaluation only (predictions already exist)"
+python run_all_evaluation.py \
+    --mode evaluation \
+    --model-id "$MODEL_ID" \
+    --data-folder "$DATA_FOLDER" \
+    --output-folder "$OUTPUT_FOLDER" \
+    --all-datasets
+
+
+echo "Direct evaluation with get_scores.py"
+python get_scores.py \
+    --results-dir "$OUTPUT_FOLDER" \
+    --data-path "$DATA_FOLDER" \
+    --datasets doc2dial quac qrecc inscit hybridial doqa_cooking doqa_travel doqa_movies convfinqa \
+    --output-csv "${OUTPUT_FOLDER}/scores.csv"

evaluation/get_scores.py

@@ -0,0 +1,499 @@
+#!/usr/bin/env python3
+from evaluation_utils import quac_correct_retrieved_instance_idx_list
+from evaluation_utils import unanswerable_keyphrases
+from arguments import get_args_scores
+import json
+from metrics import F1Metric
+import copy
+import re
+import pandas as pd
+import os
+import io
+import sys
+import argparse
+
+def compute_f1_score(predicted_answers, groundtruth_answer, exp_name="default"):
+    """Evaluating F1 Score"""
+    print(len(predicted_answers), len(groundtruth_answer))
+    if len(predicted_answers) != len(groundtruth_answer):
+        groundtruth_answer = groundtruth_answer[:len(predicted_answers)]
+
+    guess_list = []
+    for guess in predicted_answers:
+        guess = guess.strip()
+        if "</s>" in guess:
+            guess = guess.replace("</s>", "")
+        guess_list.append(guess)
+
+    answer_list = []
+    for answer in groundtruth_answer:
+        answer_list.append(answer)
+
+    assert len(guess_list) == len(answer_list), \
+        "lengths of guess and answer are different!"
+
+    precision, recall, f1 = F1Metric.compute_all_pairs(guess_list, answer_list)
+    print('Method: %s; Precision: %.4f; recall: %.4f; f1: %.4f' % (
+        exp_name, precision, recall, f1))
+    
+    return f1
+
+
+def load_groundtruth_file(data_file):
+    """Load ground truth answers from JSON file"""
+    with open(data_file, "r") as f:
+        examples = json.load(f)
+
+    data = []
+    for instance in examples:
+        if "answers" in instance:
+            answers = instance["answers"]
+        elif "answer" in instance:
+            if type(instance["answer"]) is str:
+                answers = [instance["answer"]]
+            elif type(instance["answer"]) is list:
+                answers = instance["answer"]
+            else:
+                answers = [str(instance["answer"])]
+        else:
+            raise ValueError("need to have answer or answers")
+        data.append(answers)
+
+    return data
+
+
+def load_prediction(data_file):
+    """Load predictions from text file"""
+    data = []
+    with open(data_file, "r") as f:
+        for line in f.readlines():
+            if "_on" in data_file or "_medium" in data_file or "_high" in data_file:
+                data.append(line.strip()[:300])
+            else:
+                data.append(line.strip())
+    return data
+
+
+def evaluate_f1(ground_truth_file, prediction_file):
+    """Evaluate F1 score for general QA datasets"""
+    groundtruth_answers = load_groundtruth_file(ground_truth_file)
+    
+    # Special handling for inscit dataset
+    if "inscit" in ground_truth_file:
+        groundtruth_answers_update = []
+        for answers in groundtruth_answers:
+            answers_update = []
+            for ans in answers:
+                # Remove default answer added to inscit dataset
+                if ans != "Sorry. I cannot find the answer based on the context.":
+                    answers_update.append(ans)
+            assert len(answers_update) > 0
+            groundtruth_answers_update.append(copy.deepcopy(answers_update))
+        groundtruth_answers = groundtruth_answers_update
+
+    predicted_answers = load_prediction(prediction_file)
+    
+    # Special handling for quac and doqa datasets (unanswerable questions)
+    if "quac" in prediction_file or "doqa" in prediction_file:
+        predicted_answers_new = []
+        for pred in predicted_answers:
+            pred = pred.lower()
+            for keyphrase in unanswerable_keyphrases:
+                if "उत्तर नहीं" in pred:
+                    pred = "क्षमा करें, मैं संदर्भ के आधार पर उत्तर नहीं ढूँढ पा रहा हूँ।"
+                    break
+            predicted_answers_new.append(pred)
+        predicted_answers = predicted_answers_new
+
+    f1_score = compute_f1_score(predicted_answers, groundtruth_answers)
+    return f1_score
+
+
+def evaluate_convfinqa(ground_truth_file, prediction_file):
+    """
+    Evaluate ConvFinQA dataset with special numeric matching logic.
+    Since the model gives a long answer output, while the gold answer for ConvFinQA 
+    are either an arithmetic formula or a final executed number.
+    We consider the output containing either the executed number or the arithmetic 
+    formula as correct.
+    """
+
+    def _is_float(string):
+        try:
+            float(string)
+            return True
+        except ValueError:
+            return False
+
+    with open(ground_truth_file, "r") as f:
+        gold_list = json.load(f)
+    
+    groundtruth_answers = [item['exe_answer'] for item in gold_list]
+    groundtruth_answers_formula = [item['answers'][0] for item in gold_list]
+
+    # Last turn question_list
+    question_list = [item['messages'][-1]['content'] for item in gold_list]
+    predicted_answers = load_prediction(prediction_file)
+
+    print(len(predicted_answers), len(groundtruth_answers))
+    if len(predicted_answers) != len(groundtruth_answers):
+        groundtruth_answers = groundtruth_answers[:len(predicted_answers)]
+
+    count_exact_match = 0
+    for question, pred, gold, gold_formula in zip(question_list, predicted_answers, 
+                                                   groundtruth_answers, groundtruth_answers_formula):
+
+        original_pred = pred
+        # Convert 1,000,000 into 1000000
+        original_pred = original_pred.replace(",", "")
+
+        # Convert $10 million + $20 million into 10 + 20
+        original_pred = original_pred.replace("$", "").replace("million", "").replace(
+            "billion", "").replace("मिलियन", "").replace("बिलियन ", "")
+
+        # Convert 10 (2017) + 20 (2018) into 10 + 20
+        pattern = r'\((\b\w+\b)\)'
+        original_pred = re.sub(pattern, '', original_pred)
+
+        # Make sure each token only has one space in between
+        original_pred = " ".join(original_pred.split())
+        
+        if str(gold) in original_pred:
+            count_exact_match += 1
+        elif str(gold_formula) in original_pred:
+            count_exact_match += 1
+        elif _is_float(gold) and (str(round(float(gold), 3)) in original_pred or 
+                                   str(round(float(gold), 2)) in original_pred):
+            count_exact_match += 1
+        elif "percent" in question and (str(float(gold)*100) in original_pred or 
+                                        str(round(float(gold)*100, 1)) in original_pred or 
+                                        str(round(float(gold)*100, 2)) in original_pred):
+            count_exact_match += 1
+        elif str(gold).endswith(".0") and str(int(gold)) in original_pred:
+            # Gold is an integer like 80.0 then convert it into 80
+            count_exact_match += 1
+        elif "decrease" in original_pred and _is_float(gold) and gold < 0 and (
+                str(-1 * gold) in original_pred):
+            # For the case where model generates something like a decrease of 10 million, 
+            # while gold is -10
+            count_exact_match += 1
+
+    accuracy = count_exact_match / len(predicted_answers)
+    print("accuracy of exact match: %.4f" % accuracy)
+    return accuracy
+
+
+def separate_cannot_answer(ground_truth_file, prediction_file):
+    """Separate answerable and unanswerable questions"""
+    # Load ground truth
+    with open(ground_truth_file, "r") as f:
+        groundtruth_answers = json.load(f)
+    # Load prediction
+    predicted_answers = load_prediction(prediction_file)
+    print(len(predicted_answers), len(groundtruth_answers))
+    if len(predicted_answers) != len(groundtruth_answers):
+        groundtruth_answers = groundtruth_answers[:len(predicted_answers)]
+
+    if "quac" in prediction_file:
+        """
+        For answerable cases, we want to make sure the retrieved context list contains the gold chunk.
+        For QuAC dataset, we use top-5 retrieved contexts as inputs, quac_correct_retrieved_instance_idx_list 
+        is the index list where the top-5 retrieved context contains the gold answer
+        """
+        answerable_instance_idx_list = quac_correct_retrieved_instance_idx_list
+    else:
+        answerable_instance_idx_list = None
+
+    predicted_answers_new = []
+    for pred in predicted_answers:
+        pred = pred.lower()
+        for keyphrase in unanswerable_keyphrases:
+            if keyphrase in pred:
+                pred = "Sorry. I cannot find the answer based on the context."
+                break
+        predicted_answers_new.append(pred)
+    predicted_answers = predicted_answers_new
+
+    cannot_answer_idx_list = []
+    answerable_idx_list = []
+    if answerable_instance_idx_list:
+        count_idx = 0
+    for idx, item in enumerate(groundtruth_answers):
+        if 'answers' in item:
+            answer = item["answers"][0]
+        else:
+            answer = item['answer']
+        noanswer_response = "Sorry. I cannot find the answer based on the context."
+
+        if answer == noanswer_response:
+            cannot_answer_idx_list.append(idx)
+            continue
+        
+        if answerable_instance_idx_list:
+            if count_idx in answerable_instance_idx_list:
+                answerable_idx_list.append(idx)
+            count_idx += 1
+        else:
+            answerable_idx_list.append(idx)
+
+    print("number of cannot answer cases: %d (out of %d)" % (len(cannot_answer_idx_list), len(groundtruth_answers)))
+    print("number of answerable cases: %d (out of %d)" % (len(answerable_idx_list), len(groundtruth_answers)))
+
+    return predicted_answers, cannot_answer_idx_list, answerable_idx_list
+
+
+def get_cannot_answer_and_answerable_acc(predicted_answers, cannot_answer_idx_list, answerable_idx_list):
+    """Calculate accuracy for answerable and unanswerable questions"""
+    # Cannot answer
+    noanswer_count = 0
+    for idx in cannot_answer_idx_list:
+        prediction = predicted_answers[idx]
+        prediction = prediction.lower()
+        if "sorry" in prediction and "cannot find the answer" in prediction:
+            noanswer_count += 1
+    cannot_answer_acc = noanswer_count / len(cannot_answer_idx_list) if len(cannot_answer_idx_list) > 0 else 0.0
+    print("accuracy of cannot answer cases: %.4f" % cannot_answer_acc)
+
+    # Answerable
+    answerable_count = 0
+    for idx in answerable_idx_list:
+        prediction = predicted_answers[idx]
+        prediction = prediction.lower()
+        if "sorry" in prediction and "cannot find the answer" in prediction:
+            continue
+        answerable_count += 1
+    answerable_acc = answerable_count / len(answerable_idx_list) if len(answerable_idx_list) > 0 else 0.0
+    print("accuracy of answerable cases: %.4f" % answerable_acc)
+
+
+def evaluate_cannot_answer_acc(ground_truth_file, prediction_file):
+    """Evaluate accuracy for answerable and unanswerable questions"""
+    predicted_answers, cannot_answer_idx_list, answerable_idx_list = \
+                                separate_cannot_answer(ground_truth_file, prediction_file)
+
+    get_cannot_answer_and_answerable_acc(predicted_answers, cannot_answer_idx_list, answerable_idx_list)
+
+def get_dataset_config(args):
+    """
+    Returns configuration for all datasets using paths from args
+    
+    Args:
+        args: Arguments object with dataset path configurations
+        
+    Returns:
+        dict: Dataset configuration mapping
+    """
+    return {
+        'doc2dial': {
+            'file_suffix': 'doc2dial',
+            'ground_truth_path': args.doc2dial_path,
+            'eval_function': evaluate_f1
+        },
+        'quac': {
+            'file_suffix': 'quac',
+            'ground_truth_path': args.quac_path,
+            'eval_function': evaluate_f1
+        },
+        'qrecc': {
+            'file_suffix': 'qrecc',
+            'ground_truth_path': args.qrecc_path,
+            'eval_function': evaluate_f1
+        },
+        'inscit': {
+            'file_suffix': 'inscit',
+            'ground_truth_path': args.inscit_path,
+            'eval_function': evaluate_f1
+        },
+        'hybridial': {
+            'file_suffix': 'hybridial',
+            'ground_truth_path': args.hybridial_path,
+            'eval_function': evaluate_f1
+        },
+        'doqa_cooking': {
+            'file_suffix': 'doqa_cooking',
+            'ground_truth_path': args.doqa_cooking_path,
+            'eval_function': evaluate_f1
+        },
+        'doqa_travel': {
+            'file_suffix': 'doqa_travel',
+            'ground_truth_path': args.doqa_travel_path,
+            'eval_function': evaluate_f1
+        },
+        'doqa_movies': {
+            'file_suffix': 'doqa_movies',
+            'ground_truth_path': args.doqa_movies_path,
+            'eval_function': evaluate_f1
+        },
+        'convfinqa': {
+            'file_suffix': 'convfinqa',
+            'ground_truth_path': args.convfinqa_path,
+            'eval_function': evaluate_convfinqa
+        }
+    }
+
+def evaluate_single_dataset(dataset_name, prediction_file, ground_truth_file, eval_function):
+    """
+    Evaluate a single dataset and return the score
+    
+    Args:
+        dataset_name: Name of the dataset
+        prediction_file: Path to prediction file
+        ground_truth_file: Path to ground truth file
+        eval_function: Function to use for evaluation
+        
+    Returns:
+        float: Evaluation score
+    """
+    print("-" * 80)
+    print(f"Evaluating {dataset_name}")
+    print(f"Prediction file: {prediction_file}")
+    print(f"Ground truth file: {ground_truth_file}")
+    
+    if not os.path.exists(prediction_file):
+        print(f"Warning: Prediction file not found: {prediction_file}")
+        return None
+    
+    if not os.path.exists(ground_truth_file):
+        print(f"Warning: Ground truth file not found: {ground_truth_file}")
+        return None
+    
+    try:
+        # Capture stdout to extract score
+        buffer = io.StringIO()
+        sys.stdout = buffer
+        score_value = eval_function(ground_truth_file, prediction_file)
+        sys.stdout = sys.__stdout__
+        
+        # If the function already returns the score, use it
+        if score_value is not None:
+            return float(score_value)
+        
+        # Otherwise, parse from output
+        output = buffer.getvalue()
+        if "f1:" in output:
+            score = output.split("f1:")[-1].strip()
+        elif "accuracy of exact match:" in output:
+            score = output.split("accuracy of exact match:")[-1].strip()
+        else:
+            print(f"Warning: Could not parse score from output: {output}")
+            return None
+            
+        return float(score)
+    except Exception as e:
+        print(f"Error evaluating {dataset_name}: {e}")
+        sys.stdout = sys.__stdout__
+        return None
+
+
+def evaluate_single_model(model_name, results_dir, data_path, datasets, args):
+    """
+    Evaluate a single model across all specified datasets
+    
+    Args:
+        model_name: Name of the model
+        results_dir: Directory containing model results
+        data_path: Path to ground truth data
+        datasets: List of dataset names to evaluate
+        args: Arguments object with configuration
+        
+    Returns:
+        dict: Dictionary mapping dataset names to scores
+    """
+    print(f"\n{'='*80}")
+    print(f"Evaluating Model: {model_name}")
+    print(f"{'='*80}\n")
+    
+    output_dir = os.path.join(results_dir, model_name)
+    dataset_config = get_dataset_config(args)
+    scores = {'model': model_name}
+    
+    for dataset_name in datasets:
+        if dataset_name not in dataset_config:
+            print(f"Warning: Unknown dataset {dataset_name}, skipping...")
+            continue
+        
+        config = dataset_config[dataset_name]
+        prediction_file = os.path.join(output_dir, f"{config['file_suffix']}.txt")
+        ground_truth_file = os.path.join(data_path, config['ground_truth_path'])
+        
+        score = evaluate_single_dataset(
+            dataset_name,
+            prediction_file,
+            ground_truth_file,
+            config['eval_function']
+        )
+        
+        scores[dataset_name] = score
+    
+    return scores
+
+
+def evaluate_all_models(results_dir, data_path, datasets, args, output_csv=None):
+    """
+    Evaluate all models in the results directory
+    
+    Args:
+        results_dir: Directory containing model results (subdirectories for each model)
+        data_path: Path to ground truth data directory
+        datasets: List of dataset names to evaluate
+        args: Arguments object with configuration
+        output_csv: Path to output CSV file (default: <results_dir>/scores.csv)
+        
+    Returns:
+        pd.DataFrame: DataFrame containing all evaluation scores
+    """    
+    # Get all model subdirectories
+    model_names = [d for d in os.listdir(results_dir) 
+                   if os.path.isdir(os.path.join(results_dir, d))]
+    
+    if not model_names:
+        print(f"Warning: No model directories found in {results_dir}")
+        return pd.DataFrame()
+    
+    print(f"\nFound {len(model_names)} model(s): {model_names}\n")
+    
+    # Initialize DataFrame
+    columns = ['model'] + datasets
+    df = pd.DataFrame(columns=columns)
+    
+    # Evaluate each model
+    all_scores = []
+    for model_name in model_names:
+        scores = evaluate_single_model(model_name, results_dir, data_path, datasets, args)
+        all_scores.append(scores)
+    
+    # Convert to DataFrame
+    df = pd.DataFrame(all_scores)
+    
+    # Calculate average across datasets (excluding model name column)
+    numeric_cols = [col for col in df.columns if col != 'model']
+    df['average'] = df[numeric_cols].mean(axis=1, skipna=True)
+    
+    # Save to CSV
+    if output_csv is None:
+        output_csv = os.path.join(results_dir, 'scores.csv')
+    
+    df.to_csv(output_csv, index=False)
+    print(f"\nScores saved to: {output_csv}")
+    print("\nFinal Results:")
+    print(df.to_string(index=False))
+    
+    return df
+
+def main():
+    """Main function to run evaluation pipeline"""
+    args = get_args_scores()
+    
+    # Evaluate all models
+    df = evaluate_all_models(
+        results_dir=args.results_dir,
+        data_path=args.data_path,
+        datasets=args.datasets,
+        args=args,
+        output_csv=args.output_csv
+    )
+    
+    return df
+
+if __name__ == "__main__":
+    main()

evaluation/metrics.py

@@ -0,0 +1,85 @@
+
+# The following code is adapted from
+# https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/metrics.py, 
+# which is licensed under the MIT license. More details on the license can be 
+# found at https://github.com/facebookresearch/ParlAI/blob/master/LICENSE.
+
+"""Provides standard metric evaluations for dialog."""
+
+from collections import Counter
+from typing import List
+import numpy as np
+import re
+
+re_art = re.compile(r'\b(a|an|the)\b')
+re_punc = re.compile(r'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']')
+
+
+def normalize_answer(s):
+    """
+    Lower text and remove punctuation, articles and extra whitespace.
+    """
+    s = s.lower()
+    s = re_punc.sub(' ', s)
+    s = re_art.sub(' ', s)
+    s = ' '.join(s.split())
+    return s
+
+
+class F1Metric:
+    """
+    Helper class which computes token-level F1.
+    """
+
+    @staticmethod
+    def _prec_recall_f1_score(pred_items, gold_items):
+        """
+        Compute precision, recall and f1 given a set of gold and prediction items.
+        :param pred_items: iterable of predicted values
+        :param gold_items: iterable of gold values
+        :return: tuple (p, r, f1) for precision, recall, f1
+        """
+        common = Counter(gold_items) & Counter(pred_items)
+        num_same = sum(common.values())
+        if num_same == 0:
+            return 0, 0, 0
+        precision = 1.0 * num_same / len(pred_items)
+        recall = 1.0 * num_same / len(gold_items)
+        f1 = (2 * precision * recall) / (precision + recall)
+        return precision, recall, f1
+
+    @staticmethod
+    def compute_each_pair(guess: str, answer: str):
+        if answer == "":
+            return None, None, None
+        if guess == "":
+            return 0, 0, 0
+        g_tokens = normalize_answer(guess).split()
+        a_tokens = normalize_answer(answer).split()
+
+        precision, recall, f1 = F1Metric._prec_recall_f1_score(g_tokens, a_tokens)
+        return precision, recall, f1
+        
+    @staticmethod
+    def compute_all_pairs(guesses: List[str], answers: List[list]):
+        assert len(guesses) == len(answers)
+        precision_list, recall_list, f1_list = [], [], []
+        for guess, answer in zip(guesses, answers):
+            assert type(answer) == list
+            f1_list_tmp = []
+            for answer_each in answer:
+                answer_each = answer_each.strip()
+                if answer_each == "":
+                    continue
+                precision, recall, f1 = F1Metric.compute_each_pair(guess, answer_each)
+                f1_list_tmp.append(f1)
+            
+            if len(f1_list_tmp) > 0:
+                f1 = max(f1_list_tmp)
+                if precision is None or recall is None or f1 is None:
+                    continue
+                precision_list.append(precision)
+                recall_list.append(recall)
+                f1_list.append(f1)
+        
+        return np.mean(precision_list), np.mean(recall_list), np.mean(f1_list)

evaluation/run_all_evaluation.py

@@ -0,0 +1,366 @@
+#!/usr/bin/env python3
+"""
+This script provides a unified interface to:
+1. Run inference for all datasets using HuggingFace models
+2. Evaluate all predictions and generate scores
+"""
+
+import os
+import sys
+import argparse
+import subprocess
+from typing import List, Optional
+import pandas as pd
+
+ALL_DATASETS = [
+    'doc2dial', 'quac', 'qrecc', 'inscit',
+    'hybridial', 
+    'doqa_cooking', 'doqa_travel', 'doqa_movies', 
+    'convfinqa'
+]
+
+def run_inference_for_dataset(
+    model_id: str,
+    dataset: str,
+    data_folder: str,
+    output_folder: str,
+    device: str = 'cuda',
+    num_ctx: int = 5,
+    max_tokens: int = 64,
+    expected_samples: int = 500,
+    limit: Optional[int] = None
+) -> bool:
+    """
+    Run inference for a single dataset
+    
+    Args:
+        model_id: Model identifier or path
+        dataset: Dataset name
+        data_folder: Path to data folder
+        output_folder: Path to output folder
+        device: Device to run on (cuda/cpu)
+        num_ctx: Number of contexts
+        max_tokens: Maximum number of tokens to generate
+        expected_samples: Expected number of samples
+        limit: Limit number of samples to process
+        
+    Returns:
+        bool: True if successful, False otherwise
+    """
+    print(f"\n{'='*80}")
+    print(f"Running inference for dataset: {dataset}")
+    print(f"{'='*80}\n")
+    
+    cmd = [
+        'python', 'run_generation_hf.py',
+        '--model-id', model_id,
+        '--data-folder', data_folder,
+        '--output-folder', output_folder,
+        '--eval-dataset', dataset,
+        '--device', device,
+        '--num-ctx', str(num_ctx),
+        '--max-tokens', str(max_tokens),
+        '--expected-samples', str(expected_samples)
+    ]
+    
+    if limit is not None:
+        cmd.extend(['--limit', str(limit)])
+    
+    try:
+        result = subprocess.run(cmd, check=True, capture_output=False, text=True)
+        print(f"✓ Inference completed for {dataset}")
+        return True
+    except subprocess.CalledProcessError as e:
+        print(f"✗ Error running inference for {dataset}: {e}")
+        return False
+    except Exception as e:
+        print(f"✗ Unexpected error for {dataset}: {e}")
+        return False
+
+
+def run_inference_for_all_datasets(
+    model_id: str,
+    datasets: List[str],
+    data_folder: str,
+    output_folder: str,
+    device: str = 'cuda',
+    num_ctx: int = 5,
+    max_tokens: int = 64,
+    expected_samples: int = 500,
+    limit: Optional[int] = None
+) -> dict:
+    """
+    Run inference for all specified datasets
+    
+    Args:
+        model_id: Model identifier or path
+        datasets: List of dataset names
+        data_folder: Path to data folder
+        output_folder: Path to output folder
+        device: Device to run on (cuda/cpu)
+        num_ctx: Number of contexts
+        max_tokens: Maximum number of tokens to generate
+        expected_samples: Expected number of samples
+        limit: Limit number of samples to process
+        
+    Returns:
+        dict: Dictionary mapping dataset names to success status
+    """
+    print(f"\n{'#'*80}")
+    print(f"# Running Inference for Model: {model_id}")
+    print(f"# Total Datasets: {len(datasets)}")
+    print(f"{'#'*80}\n")
+    
+    results = {}
+    for dataset in datasets:
+        success = run_inference_for_dataset(
+            model_id=model_id,
+            dataset=dataset,
+            data_folder=data_folder,
+            output_folder=output_folder,
+            device=device,
+            num_ctx=num_ctx,
+            max_tokens=max_tokens,
+            expected_samples=expected_samples,
+            limit=limit
+        )
+        results[dataset] = success
+    
+    # Print summary
+    print(f"\n{'='*80}")
+    print("Inference Summary:")
+    print(f"{'='*80}")
+    successful = sum(1 for v in results.values() if v)
+    print(f"✓ Successful: {successful}/{len(datasets)}")
+    print(f"✗ Failed: {len(datasets) - successful}/{len(datasets)}")
+    
+    if successful < len(datasets):
+        print("\nFailed datasets:")
+        for dataset, success in results.items():
+            if not success:
+                print(f"  - {dataset}")
+    
+    return results
+
+
+def run_evaluation(
+    results_dir: str,
+    data_path: str,
+    datasets: List[str],
+    output_csv: Optional[str] = None
+) -> pd.DataFrame:
+    """
+    Run evaluation for all models and datasets
+    
+    Args:
+        results_dir: Directory containing model results
+        data_path: Path to ground truth data
+        datasets: List of dataset names to evaluate
+        output_csv: Path to output CSV file
+        
+    Returns:
+        pd.DataFrame: Evaluation results
+    """
+    print(f"\n{'#'*80}")
+    print(f"# Running Evaluation")
+    print(f"# Results Directory: {results_dir}")
+    print(f"# Data Path: {data_path}")
+    print(f"{'#'*80}\n")
+    
+    cmd = [
+        'python', 'get_scores.py',
+        '--results-dir', results_dir,
+        '--data-path', data_path,
+        '--datasets'
+    ] + datasets
+    
+    if output_csv:
+        cmd.extend(['--output-csv', output_csv])
+    
+    try:
+        result = subprocess.run(cmd, check=True, capture_output=False, text=True)
+        print(f"\n✓ Evaluation completed successfully")
+        
+        # Load and return the results
+        if output_csv:
+            csv_path = output_csv
+        else:
+            csv_path = os.path.join(results_dir, 'scores.csv')
+        
+        if os.path.exists(csv_path):
+            df = pd.read_csv(csv_path)
+            return df
+        else:
+            print(f"Warning: Output CSV not found at {csv_path}")
+            return pd.DataFrame()
+            
+    except subprocess.CalledProcessError as e:
+        print(f"✗ Error running evaluation: {e}")
+        return pd.DataFrame()
+    except Exception as e:
+        print(f"✗ Unexpected error during evaluation: {e}")
+        return pd.DataFrame()
+
+def run_full_pipeline(
+    model_id: str,
+    data_folder: str,
+    output_folder: str,
+    datasets: List[str] = ALL_DATASETS,
+    device: str = 'cuda',
+    num_ctx: int = 5,
+    max_tokens: int = 64,
+    expected_samples: int = 500,
+    limit: Optional[int] = None,
+    skip_inference: bool = False,
+    skip_evaluation: bool = False,
+    output_csv: Optional[str] = None
+) -> pd.DataFrame:
+    """
+    Run the complete pipeline: inference + evaluation
+    
+    Args:
+        model_id: Model identifier or path
+        data_folder: Path to data folder
+        output_folder: Path to output folder
+        datasets: List of dataset names
+        device: Device to run on (cuda/cpu)
+        num_ctx: Number of contexts
+        max_tokens: Maximum number of tokens to generate
+        expected_samples: Expected number of samples
+        limit: Limit number of samples to process
+        skip_inference: Skip inference step
+        skip_evaluation: Skip evaluation step
+        output_csv: Path to output CSV file
+        
+    Returns:
+        pd.DataFrame: Evaluation results
+    """
+    print(f"\n{'#'*80}")
+    print(f"# ChatRAG-Hi Full Evaluation Pipeline")
+    print(f"{'#'*80}\n")
+    print(f"Model: {model_id}")
+    print(f"Datasets: {', '.join(datasets)}")
+    print(f"Device: {device}")
+    print(f"Skip Inference: {skip_inference}")
+    print(f"Skip Evaluation: {skip_evaluation}")
+    
+    # Step 1: Run inference
+    if not skip_inference:
+        inference_results = run_inference_for_all_datasets(
+            model_id=model_id,
+            datasets=datasets,
+            data_folder=data_folder,
+            output_folder=output_folder,
+            device=device,
+            num_ctx=num_ctx,
+            max_tokens=max_tokens,
+            expected_samples=expected_samples,
+            limit=limit
+        )
+    else:
+        print("\n⊘ Skipping inference step")
+    
+    # Step 2: Run evaluation
+    if not skip_evaluation:
+        eval_results = run_evaluation(
+            results_dir=output_folder,
+            data_path=data_folder,
+            datasets=datasets,
+            output_csv=output_csv
+        )
+        return eval_results
+    else:
+        print("\n⊘ Skipping evaluation step")
+        return pd.DataFrame()
+
+def get_args():
+    """Parse command line arguments"""
+    parser = argparse.ArgumentParser(
+        description="Comprehensive wrapper for ChatRAG-Hi inference and evaluation"
+    )
+    
+    # Pipeline control
+    parser.add_argument('--mode', type=str, choices=['inference', 'evaluation', 'full'],
+                        default='full',
+                        help='Pipeline mode: inference only, evaluation only, or full pipeline')
+    
+    # Model configuration
+    parser.add_argument('--model-id', type=str, required=True,
+                        help='Model identifier or path')
+    
+    # Data paths
+    parser.add_argument('--data-folder', type=str, required=True,
+                        help='Path to data folder containing ground truth JSON files')
+    parser.add_argument('--output-folder', type=str, required=True,
+                        help='Path to output folder for predictions and scores')
+    
+    # Dataset selection
+    parser.add_argument('--datasets', type=str, nargs='+',
+                        default=ALL_DATASETS,
+                        help='List of datasets to process')
+    parser.add_argument('--all-datasets', action='store_true',
+                        help='Process all available datasets')
+    
+    # Inference parameters
+    parser.add_argument('--device', type=str, default='cuda',
+                        help='Device to run on: cpu or cuda')
+    parser.add_argument('--num-ctx', type=int, default=5,
+                        help='Number of contexts')
+    parser.add_argument('--max-tokens', type=int, default=64,
+                        help='Maximum number of tokens to generate')
+    parser.add_argument('--expected-samples', type=int, default=500,
+                        help='Expected number of samples per dataset')
+    parser.add_argument('--limit', type=int, default=None,
+                        help='Limit number of samples to process (for testing)')
+    
+    # Output options
+    parser.add_argument('--output-csv', type=str, default=None,
+                        help='Path to output CSV file for scores')
+    
+    args = parser.parse_args()
+    
+    # Use all datasets if specified
+    if args.all_datasets:
+        args.datasets = ALL_DATASETS
+    
+    return args
+
+
+def main():
+    """Main entry point"""
+    args = get_args()
+    
+    # Create output directory if it doesn't exist
+    os.makedirs(args.output_folder, exist_ok=True)
+    
+    # Determine what to skip based on mode
+    skip_inference = (args.mode == 'evaluation')
+    skip_evaluation = (args.mode == 'inference')
+    
+    # Run the pipeline
+    results = run_full_pipeline(
+        model_id=args.model_id,
+        data_folder=args.data_folder,
+        output_folder=args.output_folder,
+        datasets=args.datasets,
+        device=args.device,
+        num_ctx=args.num_ctx,
+        max_tokens=args.max_tokens,
+        expected_samples=args.expected_samples,
+        limit=args.limit,
+        skip_inference=skip_inference,
+        skip_evaluation=skip_evaluation,
+        output_csv=args.output_csv
+    )
+    
+    if not results.empty and args.mode != 'inference':
+        print(f"\n{'='*80}")
+        print("Final Evaluation Results:")
+        print(f"{'='*80}\n")
+        print(results.to_string(index=False))
+        print(f"\n{'='*80}\n")
+
+
+if __name__ == "__main__":
+    main()
+

evaluation/run_generation_hf.py

@@ -0,0 +1,210 @@
+import os
+import json
+import random
+
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from arguments import get_args
+
+random.seed(1234)
+
+
+def load_data(datapath):
+    """Load data from a JSON file."""
+    print("loading data from %s" % datapath)
+    with open(datapath, "r", encoding="utf-8") as f:
+        data_list = json.load(f)
+    return data_list
+
+
+def reformat_question(turn_list, dataset_name):
+    """Reformat question based on dataset type and keep last 7 turns."""
+    ## only take the lastest 7 turns
+    _turn_list = turn_list[-7:]
+    idx = -6
+    while _turn_list[0]['role'] != 'user':
+        _turn_list = turn_list[idx:]
+        idx += 1
+    turn_list = _turn_list
+    assert turn_list[-1]['role'] == 'user'
+    assert turn_list[0]['role'] == 'user'
+
+    long_answer_dataset_list = ["doc2dial", "quac", "qrecc", "inscit", "doqa_movies", "doqa_travel", "doqa_cooking", "hybridial", "convfinqa"]
+
+    if dataset_name in long_answer_dataset_list:
+        for item in turn_list:
+            if item['role'] == 'user':
+                ## only needs to add it on the first user turn
+                item['content'] = 'Please give a full and complete answer for the question: ' + item['content']
+                break
+    else:
+        raise Exception("please input a correct dataset name!")
+    
+    return turn_list
+
+
+def get_inputs_hf(data_list, dataset_name, num_ctx):
+    """
+    Get inputs formatted for HuggingFace chat template.
+    Returns a list of message lists (chat format).
+    """
+    system = "You are a helpful AI assistant that gives concise and detailed answers to the user's questions based on the given contexts. You should indicate when the answer cannot be found in any of the contexts. You should only respond with the answer."
+    prompt_list = []
+    
+    for item in data_list:
+        turn_list = item['messages']
+        turn_list = reformat_question(turn_list, dataset_name)
+
+        ctx_list = ["title: " + ctx["title"] + ", context: " + ctx["text"]
+                    if ctx["title"] else "context: " + ctx["text"] for ctx in item['ctxs'][:num_ctx]]
+        context = "\n\n".join(ctx_list)
+
+        turn_list[0]["content"] = f"{system}\n\n{context}\n\n{turn_list[0]['content']}"
+
+        # Clean consecutive assistant turns
+        cleaned_turn_list = []
+        for turn in turn_list:
+            try:
+                if turn["role"] != "assistant":
+                    cleaned_turn_list.append(turn)
+                else:
+                    if cleaned_turn_list[-1]["role"] == "assistant":
+                        cleaned_turn_list[-1]["content"] += ". " + turn["content"]
+                    else:
+                        cleaned_turn_list.append(turn)
+            except Exception as ex:
+                print(str(ex.args))
+                import pdb; pdb.set_trace()
+
+        prompt_list.append(cleaned_turn_list)
+    
+    return prompt_list
+
+
+def get_input_datapath(args):
+    """Get the input data path based on the eval_dataset."""
+    if args.eval_dataset == "doc2dial":
+        input_datapath = os.path.join(args.data_folder, args.doc2dial_path)
+    elif args.eval_dataset == "convfinqa":
+        input_datapath = os.path.join(args.data_folder, args.convfinqa_path)
+    elif args.eval_dataset == "quac":
+        input_datapath = os.path.join(args.data_folder, args.quac_path)
+    elif args.eval_dataset == "qrecc":
+        input_datapath = os.path.join(args.data_folder, args.qrecc_path)
+    elif args.eval_dataset == "doqa_cooking":
+        input_datapath = os.path.join(args.data_folder, args.doqa_cooking_path)
+    elif args.eval_dataset == "doqa_travel":
+        input_datapath = os.path.join(args.data_folder, args.doqa_travel_path)
+    elif args.eval_dataset == "doqa_movies":
+        input_datapath = os.path.join(args.data_folder, args.doqa_movies_path)
+    elif args.eval_dataset == "inscit":
+        input_datapath = os.path.join(args.data_folder, args.inscit_path)
+    elif args.eval_dataset == "hybridial":
+        input_datapath = os.path.join(args.data_folder, args.hybridial_path)
+    else:
+        raise Exception("please input a correct eval_dataset name!")
+    
+    return input_datapath
+
+
+def get_prompt_list(args):
+    """Get prompt list for the given dataset."""
+    input_datapath = get_input_datapath(args)
+    data_list = load_data(input_datapath)
+    print("number of samples in the dataset:", len(data_list))
+    
+    # Apply limit if specified
+    if args.limit is not None:
+        data_list = data_list[:args.limit]
+        print(f"limited to {args.limit} samples")
+    
+    prompt_list = get_inputs_hf(data_list, args.eval_dataset, num_ctx=args.num_ctx)
+    return prompt_list
+
+
+def run_inference(args, tokenizer, model):
+    """Run inference for a given dataset."""
+    # Get output filepath
+    model_name = args.model_id.replace('/', '_')
+    os.makedirs(os.path.join(args.output_folder, model_name), exist_ok=True)
+    output_filepath = os.path.join(args.output_folder, model_name, f"{args.eval_dataset}.txt")
+
+    # Check for existing results
+    existing_count = 0
+    if os.path.exists(output_filepath):
+        with open(output_filepath, "r") as f:
+            lines = f.readlines()
+            if len(lines) >= args.expected_samples:
+                print(f"Skipping as results exist ({len(lines)} samples)", "\n\n")
+                return
+            else:
+                existing_count = len(lines)
+                print(f"Resuming from {existing_count} existing samples")
+    
+    # Get prompt list
+    prompt_list = get_prompt_list(args)
+    
+    # Run generation
+    output_list = []
+    with open(output_filepath, "a", encoding='utf-8') as f:
+        for idx, messages in enumerate(tqdm(prompt_list, desc=f"Generating for {args.eval_dataset}")):
+            if idx < existing_count:
+                continue
+            
+            try:
+                # Apply chat template
+                text = tokenizer.apply_chat_template(
+                    messages,
+                    tokenize=False,
+                    add_generation_prompt=True
+                )
+                
+                # Generate
+                model_inputs = tokenizer([text], return_tensors="pt").to(args.device)
+                generated_ids = model.generate(
+                    model_inputs.input_ids,
+                    max_new_tokens=args.max_tokens,
+                    stop_strings=args.stop_strings,
+                    tokenizer=tokenizer
+                )
+                
+                # Decode
+                generated_ids = [
+                    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+                ]
+                response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+                generated_text = response.strip().replace("\n", " ").strip(" <extra_id_1>")
+                
+                output_list.append(generated_text)
+                f.write(generated_text + "\n")
+                
+            except Exception as ex:
+                print(f"Error at index {idx}: {str(ex)}")
+                break
+    
+    print(f"Generated {len(output_list)} responses for {args.eval_dataset}")
+
+
+def main():
+    """Main function to run HuggingFace model inference."""
+    args = get_args()
+    
+    print(f"Evaluating model: {args.model_id}")
+    print(f"Dataset: {args.eval_dataset}")
+    print(f"Device: {args.device}")
+    print(f"Num contexts: {args.num_ctx}")
+    print(f"Max tokens: {args.max_tokens}")
+    
+    # Load tokenizer and model
+    tokenizer = AutoTokenizer.from_pretrained(args.model_id, stop_strings=args.stop_strings)
+    model = AutoModelForCausalLM.from_pretrained(args.model_id)
+    model.to(args.device)
+    
+    # Run inference
+    run_inference(args, tokenizer, model)
+    
+    print("Inference completed!")
+
+
+if __name__ == "__main__":
+    main()

evaluation/run_generation_vllm.py

@@ -0,0 +1,89 @@
+
+
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+from arguments import get_args
+from dataset import load_data, get_inputs
+import torch
+import os
+
+def get_prompt_list(args):
+
+    ## get tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(args.model_id)
+
+    ## get input data
+    if args.eval_dataset == "doc2dial":
+        input_datapath = os.path.join(args.data_folder, args.doc2dial_path)
+    elif args.eval_dataset == "convfinqa":
+        input_datapath = os.path.join(args.data_folder, args.convfinqa_path)
+    elif args.eval_dataset == "quac":
+        input_datapath = os.path.join(args.data_folder, args.quac_path)
+    elif args.eval_dataset == "qrecc":
+        input_datapath = os.path.join(args.data_folder, args.qrecc_path)
+    elif args.eval_dataset == "doqa_cooking":
+        input_datapath = os.path.join(args.data_folder, args.doqa_cooking_path)
+    elif args.eval_dataset == "doqa_travel":
+        input_datapath = os.path.join(args.data_folder, args.doqa_travel_path)
+    elif args.eval_dataset == "doqa_movies":
+        input_datapath = os.path.join(args.data_folder, args.doqa_movies_path)
+    elif args.eval_dataset == "coqa":
+        input_datapath = os.path.join(args.data_folder, args.coqa_path)
+    elif args.eval_dataset == "sqa":
+        input_datapath = os.path.join(args.data_folder, args.sqa_path)
+    elif args.eval_dataset == "topiocqa":
+        input_datapath = os.path.join(args.data_folder, args.topiocqa_path)
+    elif args.eval_dataset == "inscit":
+        input_datapath = os.path.join(args.data_folder, args.inscit_path)
+    elif args.eval_dataset == "hybridial":
+        input_datapath = os.path.join(args.data_folder, args.hybridial_path)
+
+    else:
+        raise Exception("please input a correct eval_dataset name!")
+    
+    data_list = load_data(input_datapath)
+    print("number of samples in the dataset:", len(data_list))
+    prompt_list = get_inputs(data_list, args.eval_dataset, tokenizer, num_ctx=args.num_ctx, max_output_len=args.out_seq_len)
+
+    return prompt_list
+
+
+def main():
+    args = get_args()
+    
+    ## bos token for llama-3
+    bos_token = "<|begin_of_text|>"
+
+    ## get model_path
+    model_path = os.path.join(args.model_folder, args.model_name)
+    
+    ## get prompt_list
+    prompt_list = get_prompt_list(args)
+    
+    ## get output_datapath
+    output_datapath = os.path.join(args.output_folder, "%s_output.txt" % args.eval_dataset)
+
+    ## run inference
+    sampling_params = SamplingParams(temperature=0, top_k=1, max_tokens=args.max_tokens)
+
+    ## This changes the GPU support to 8
+    model_vllm = LLM(model_path, tensor_parallel_size=8)
+
+    output_list = []
+    for prompt in prompt_list:
+        prompt = bos_token + prompt
+        output = model_vllm.generate([prompt], sampling_params)[0]
+        generated_text = output.outputs[0].text
+        generated_text = generated_text.strip().replace("\n", " ")
+        
+        # print("generated_text:", generated_text)
+        output_list.append(generated_text)
+
+    print("writing to %s" % output_datapath)
+    with open(output_datapath, "w") as f:
+        for output in output_list:
+            f.write(output + "\n")
+
+
+if __name__ == "__main__":
+    main()