论文复现-2：代码部分

以CONLL03数据集为例

1 整体框架

任务是实体识别任务；
语义表示：双塔模型；
模型方法：1、span表示学习（span的hi , hj 和j-i这个span width）
2、span position表示学习 (start end position） 每个position位置的embedding是由当前token的表示作为的，然后每个类型的embedding是cls token的语义表示。
损失函数：对应于上边的模型方法。

可能涉及的细节：阈值确定、语义表示选择什么方式——CLS还是pooling。

2 数据结构

2.1 原始数据集

原始数据集中是以一整篇文档作为存储单位的。
关键词有doc_key表示文档序号。
ner表示句子中的实体列表，每个实体由start 和 end 和type三部分，是一个句子作为初始计数单位。
sentence表示文档中的所有句子，构成的二维列表。

{"doc_key": "train_01", "ners": [[[0, 0, "ORG"], [2, 2, "MISC"], [6, 6, "MISC"]], [[0, 1, "PER"]], [[0, 0, "LOC"]], [[1, 2, "ORG"], [9, 9, "MISC"], [15, 15, "MISC"]], [[0, 0, "LOC"], [5, 6, "ORG"], [10, 11, "PER"], [23, 23, "LOC"]], [[20, 20, "ORG"], [24, 27, "PER"]], [[22, 23, "ORG"]], [[7, 7, "ORG"], [10, 11, "PER"]], [[0, 0, "PER"], [2, 2, "MISC"], [7, 7, "LOC"], [9, 9, "LOC"], [17, 19, "MISC"], [21, 21, "MISC"]], [[1, 1, "PER"], [9, 9, "ORG"]], [[0, 0, "MISC"], [3, 5, "PER"], [9, 9, "PER"], [12, 12, "ORG"]], [], [[1, 1, "LOC"], [3, 3, "LOC"], [5, 5, "PER"]], [[1, 1, "ORG"]], [[14, 14, "MISC"]], [[0, 0, "MISC"], [20, 20, "MISC"], [27, 27, "MISC"], [33, 33, "LOC"]], [[17, 17, "LOC"], [22, 26, "ORG"], [28, 28, "ORG"], [31, 33, "PER"], [36, 37, "ORG"]], [[0, 0, "LOC"], [16, 16, "MISC"]], [[0, 0, "LOC"], [5, 5, "LOC"]], [[6, 6, "MISC"]]], "sentences": [["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."], ["Peter", "Blackburn"], ["BRUSSELS", "1996-08-22"], ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."], ["Germany", "'s", "representative", "to", "the", "European", "Union", "'s", "veterinary", "committee", "Werner", "Zwingmann", "said", "on", "Wednesday", "consumers", "should", "buy", "sheepmeat", "from", "countries", "other", "than", "Britain", "until", "the", "scientific", "advice", "was", "clearer", "."], ["\"", "We", "do", "n't", "support", "any", "such", "recommendation", "because", "we", "do", "n't", "see", "any", "grounds", "for", "it", ",", "\"", "the", "Commission", "'s", "chief", "spokesman", "Nikolaus", "van", "der", "Pas", "told", "a", "news", "briefing", "."], ["He", "said", "further", "scientific", "study", "was", "required", "and", "if", "it", "was", "found", "that", "action", "was", "needed", "it", "should", "be", "taken", "by", "the", "European", "Union", "."], ["He", "said", "a", "proposal", "last", "month", "by", "EU", "Farm", "Commissioner", "Franz", "Fischler", "to", "ban", "sheep", "brains", ",", "spleens", "and", "spinal", "cords", "from", "the", "human", "and", "animal", "food", "chains", "was", "a", "highly", "specific", "and", "precautionary", "move", "to", "protect", "human", "health", "."], ["Fischler", "proposed", "EU-wide", "measures", "after", "reports", "from", "Britain", "and", "France", "that", "under", "laboratory", "conditions", "sheep", "could", "contract", "Bovine", "Spongiform", "Encephalopathy", "(", "BSE", ")", "--", "mad", "cow", "disease", "."], ["But", "Fischler", "agreed", "to", "review", "his", "proposal", "after", "the", "EU", "'s", "standing", "veterinary", "committee", ",", "mational", "animal", "health", "officials", ",", "questioned", "if", "such", "action", "was", "justified", "as", "there", "was", "only", "a", "slight", "risk", "to", "human", "health", "."], ["Spanish", "Farm", "Minister", "Loyola", "de", "Palacio", "had", "earlier", "accused", "Fischler", "at", "an", "EU", "farm", "ministers", "'", "meeting", "of", "causing", "unjustified", "alarm", "through", "\"", "dangerous", "generalisation", ".", "\""], ["."], ["Only", "France", "and", "Britain", "backed", "Fischler", "'s", "proposal", "."], ["The", "EU", "'s", "scientific", "veterinary", "and", "multidisciplinary", "committees", "are", "due", "to", "re-examine", "the", "issue", "early", "next", "month", "and", "make", "recommendations", "to", "the", "senior", "veterinary", "officials", "."], ["Sheep", "have", "long", "been", "known", "to", "contract", "scrapie", ",", "a", "brain-wasting", "disease", "similar", "to", "BSE", "which", "is", "believed", "to", "have", "been", "transferred", "to", "cattle", "through", "feed", "containing", "animal", "waste", "."], ["British", "farmers", "denied", "on", "Thursday", "there", "was", "any", "danger", "to", "human", "health", "from", "their", "sheep", ",", "but", "expressed", "concern", "that", "German", "government", "advice", "to", "consumers", "to", "avoid", "British", "lamb", "might", "influence", "consumers", "across", "Europe", "."], ["\"", "What", "we", "have", "to", "be", "extremely", "careful", "of", "is", "how", "other", "countries", "are", "going", "to", "take", "Germany", "'s", "lead", ",", "\"", "Welsh", "National", "Farmers", "'", "Union", "(", "NFU", ")", "chairman", "John", "Lloyd", "Jones", "said", "on", "BBC", "radio", "."], ["Bonn", "has", "led", "efforts", "to", "protect", "public", "health", "after", "consumer", "confidence", "collapsed", "in", "March", "after", "a", "British", "report", "suggested", "humans", "could", "contract", "an", "illness", "similar", "to", "mad", "cow", "disease", "by", "eating", "contaminated", "beef", "."], ["Germany", "imported", "47,600", "sheep", "from", "Britain", "last", "year", ",", "nearly", "half", "of", "total", "imports", "."], ["It", "brought", "in", "4,275", "tonnes", "of", "British", "mutton", ",", "some", "10", "percent", "of", "overall", "imports", "."]]}

2.2 处理之后的数据集

将原始数据集转为文中数据集格式。
数据集的key如下《text是一个文档的所有句子构成的text文本》
entity type是文档中所有的实体构成的列表。
entity start chars是实体开始的char的起始位置
end chars 也是以每个字母作为一个计量单位的，指的实体的结束位置的char的位置索引
‘text’: text,
‘entity_types’: entity_types,
‘entity_start_chars’: entity_start_chars,
‘entity_end_chars’: entity_end_chars,
‘id’: example[‘doc_key’],
‘word_start_chars’: start_words,
‘word_end_chars’: end_words

{"text": "EU rejects German call to boycott British lamb . Peter Blackburn BRUSSELS 1996-08-22 The European Commission said on Thursday it disagreed with German advice to consumers to shun British lamb until scientists determine whether mad cow disease can be transmitted to sheep . Germany 's representative to the European Union 's veterinary committee Werner Zwingmann said on Wednesday consumers should buy sheepmeat from countries other than Britain until the scientific advice was clearer . \" We do n't support any such recommendation because we do n't see any grounds for it , \" the Commission 's chief spokesman Nikolaus van der Pas told a news briefing . He said further scientific study was required and if it was found that action was needed it should be taken by the European Union . He said a proposal last month by EU Farm Commissioner Franz Fischler to ban sheep brains , spleens and spinal cords from the human and animal food chains was a highly specific and precautionary move to protect human health . Fischler proposed EU-wide measures after reports from Britain and France that under laboratory conditions sheep could contract Bovine Spongiform Encephalopathy ( BSE ) -- mad cow disease . But Fischler agreed to review his proposal after the EU 's standing veterinary committee , mational animal health officials , questioned if such action was justified as there was only a slight risk to human health . Spanish Farm Minister Loyola de Palacio had earlier accused Fischler at an EU farm ministers ' meeting of causing unjustified alarm through \" dangerous generalisation . \" . Only France and Britain backed Fischler 's proposal . The EU 's scientific veterinary and multidisciplinary committees are due to re-examine the issue early next month and make recommendations to the senior veterinary officials . Sheep have long been known to contract scrapie , a brain-wasting disease similar to BSE which is believed to have been transferred to cattle through feed containing animal waste . British farmers denied on Thursday there was any danger to human health from their sheep , but expressed concern that German government advice to consumers to avoid British lamb might influence consumers across Europe . \" What we have to be extremely careful of is how other countries are going to take Germany 's lead , \" Welsh National Farmers ' Union ( NFU ) chairman John Lloyd Jones said on BBC radio . Bonn has led efforts to protect public health after consumer confidence collapsed in March after a British report suggested humans could contract an illness similar to mad cow disease by eating contaminated beef . Germany imported 47,600 sheep from Britain last year , nearly half of total imports . It brought in 4,275 tonnes of British mutton , some 10 percent of overall imports .", "entity_types": ["ORG", "MISC", "MISC", "PER", "LOC", "ORG", "MISC", "MISC", "LOC", "ORG", "PER", "LOC", "ORG", "PER", "ORG", "ORG", "PER", "PER", "MISC", "LOC", "LOC", "MISC", "MISC", "PER", "ORG", "MISC", "PER", "PER", "ORG", "LOC", "LOC", "PER", "ORG", "MISC", "MISC", "MISC", "MISC", "LOC", "LOC", "ORG", "ORG", "PER", "ORG", "LOC", "MISC", "LOC", "LOC", "MISC"], "entity_start_chars": [0, 11, 34, 49, 65, 89, 144, 179, 273, 306, 345, 437, 580, 610, 769, 819, 840, 1011, 1029, 1065, 1077, 1138, 1173, 1204, 1253, 1416, 1438, 1476, 1491, 1594, 1605, 1620, 1647, 1903, 1999, 2117, 2164, 2210, 2302, 2322, 2355, 2370, 2395, 2407, 2506, 2621, 2656, 2737], "entity_end_chars": [2, 17, 41, 64, 73, 108, 150, 186, 280, 320, 361, 444, 590, 630, 783, 821, 854, 1019, 1036, 1072, 1083, 1170, 1176, 1212, 1255, 1423, 1455, 1484, 1493, 1600, 1612, 1628, 1649, 1906, 2006, 2123, 2171, 2216, 2309, 2352, 2358, 2386, 2404, 2411, 2513, 2628, 2663, 2744], "id": "train_01", "word_start_chars": [0, 3, 11, 18, 23, 26, 34, 42, 47, 49, 55, 65, 74, 85, 89, 98, 109, 114, 117, 126, 129, 139, 144, 151, 158, 161, 171, 174, 179, 187, 192, 198, 209, 219, 227, 231, 235, 243, 247, 250, 262, 265, 271, 273, 281, 284, 299, 302, 306, 315, 321, 324, 335, 345, 352, 362, 367, 370, 380, 390, 397, 401, 411, 416, 426, 432, 437, 445, 451, 455, 466, 473, 477, 485, 487, 489, 492, 495, 499, 507, 511, 516, 531, 539, 542, 545, 549, 553, 557, 565, 569, 572, 574, 576, 580, 591, 594, 600, 610, 619, 623, 627, 631, 636, 638, 643, 652, 654, 657, 662, 670, 681, 687, 691, 700, 704, 707, 710, 714, 720, 725, 732, 736, 743, 746, 753, 756, 762, 765, 769, 778, 784, 786, 789, 794, 796, 805, 810, 816, 819, 822, 827, 840, 846, 855, 858, 862, 868, 875, 877, 885, 889, 896, 902, 907, 911, 917, 921, 928, 933, 940, 944, 946, 953, 962, 966, 980, 985, 988, 996, 1002, 1009, 1011, 1020, 1029, 1037, 1046, 1052, 1060, 1065, 1073, 1077, 1084, 1089, 1095, 1106, 1117, 1123, 1129, 1138, 1145, 1156, 1171, 1173, 1177, 1179, 1182, 1186, 1190, 1198, 1200, 1204, 1213, 1220, 1223, 1230, 1234, 1243, 1249, 1253, 1256, 1259, 1268, 1279, 1289, 1291, 1300, 1307, 1314, 1324, 1326, 1337, 1340, 1345, 1352, 1356, 1366, 1369, 1375, 1379, 1384, 1386, 1393, 1398, 1401, 1407, 1414, 1416, 1424, 1429, 1438, 1445, 1448, 1456, 1460, 1468, 1476, 1485, 1488, 1491, 1494, 1499, 1509, 1511, 1519, 1522, 1530, 1542, 1548, 1556, 1558, 1568, 1583, 1585, 1587, 1589, 1594, 1601, 1605, 1613, 1620, 1629, 1632, 1641, 1643, 1647, 1650, 1653, 1664, 1675, 1679, 1697, 1708, 1712, 1716, 1719, 1730, 1734, 1740, 1746, 1751, 1757, 1761, 1766, 1782, 1785, 1789, 1796, 1807, 1817, 1819, 1825, 1830, 1835, 1840, 1846, 1849, 1858, 1866, 1868, 1870, 1884, 1892, 1900, 1903, 1907, 1913, 1916, 1925, 1928, 1933, 1938, 1950, 1953, 1960, 1968, 1973, 1984, 1991, 1997, 1999, 2007, 2015, 2022, 2025, 2034, 2040, 2044, 2048, 2055, 2058, 2064, 2071, 2076, 2082, 2088, 2090, 2094, 2104, 2112, 2117, 2124, 2135, 2142, 2145, 2155, 2158, 2164, 2172, 2177, 2183, 2193, 2203, 2210, 2217, 2219, 2221, 2226, 2229, 2234, 2237, 2240, 2250, 2258, 2261, 2264, 2268, 2274, 2284, 2288, 2294, 2297, 2302, 2310, 2313, 2318, 2320, 2322, 2328, 2337, 2345, 2347, 2353, 2355, 2359, 2361, 2370, 2375, 2381, 2387, 2392, 2395, 2399, 2405, 2407, 2412, 2416, 2420, 2428, 2431, 2439, 2446, 2453, 2459, 2468, 2479, 2489, 2492, 2498, 2504, 2506, 2514, 2521, 2531, 2538, 2544, 2553, 2556, 2564, 2572, 2575, 2579, 2583, 2591, 2594, 2601, 2614, 2619, 2621, 2629, 2638, 2645, 2651, 2656, 2664, 2669, 2674, 2676, 2683, 2688, 2691, 2697, 2705, 2707, 2710, 2718, 2721, 2727, 2734, 2737, 2745, 2752, 2754, 2759, 2762, 2770, 2773, 2781, 2789], "word_end_chars": [2, 10, 17, 22, 25, 33, 41, 46, 48, 54, 64, 73, 84, 88, 97, 108, 113, 116, 125, 128, 138, 143, 150, 157, 160, 170, 173, 178, 186, 191, 197, 208, 218, 226, 230, 234, 242, 246, 249, 261, 264, 270, 272, 280, 283, 298, 301, 305, 314, 320, 323, 334, 344, 351, 361, 366, 369, 379, 389, 396, 400, 410, 415, 425, 431, 436, 444, 450, 454, 465, 472, 476, 484, 486, 488, 491, 494, 498, 506, 510, 515, 530, 538, 541, 544, 548, 552, 556, 564, 568, 571, 573, 575, 579, 590, 593, 599, 609, 618, 622, 626, 630, 635, 637, 642, 651, 653, 656, 661, 669, 680, 686, 690, 699, 703, 706, 709, 713, 719, 724, 731, 735, 742, 745, 752, 755, 761, 764, 768, 777, 783, 785, 788, 793, 795, 804, 809, 815, 818, 821, 826, 839, 845, 854, 857, 861, 867, 874, 876, 884, 888, 895, 901, 906, 910, 916, 920, 927, 932, 939, 943, 945, 952, 961, 965, 979, 984, 987, 995, 1001, 1008, 1010, 1019, 1028, 1036, 1045, 1051, 1059, 1064, 1072, 1076, 1083, 1088, 1094, 1105, 1116, 1122, 1128, 1137, 1144, 1155, 1170, 1172, 1176, 1178, 1181, 1185, 1189, 1197, 1199, 1203, 1212, 1219, 1222, 1229, 1233, 1242, 1248, 1252, 1255, 1258, 1267, 1278, 1288, 1290, 1299, 1306, 1313, 1323, 1325, 1336, 1339, 1344, 1351, 1355, 1365, 1368, 1374, 1378, 1383, 1385, 1392, 1397, 1400, 1406, 1413, 1415, 1423, 1428, 1437, 1444, 1447, 1455, 1459, 1467, 1475, 1484, 1487, 1490, 1493, 1498, 1508, 1510, 1518, 1521, 1529, 1541, 1547, 1555, 1557, 1567, 1582, 1584, 1586, 1588, 1593, 1600, 1604, 1612, 1619, 1628, 1631, 1640, 1642, 1646, 1649, 1652, 1663, 1674, 1678, 1696, 1707, 1711, 1715, 1718, 1729, 1733, 1739, 1745, 1750, 1756, 1760, 1765, 1781, 1784, 1788, 1795, 1806, 1816, 1818, 1824, 1829, 1834, 1839, 1845, 1848, 1857, 1865, 1867, 1869, 1883, 1891, 1899, 1902, 1906, 1912, 1915, 1924, 1927, 1932, 1937, 1949, 1952, 1959, 1967, 1972, 1983, 1990, 1996, 1998, 2006, 2014, 2021, 2024, 2033, 2039, 2043, 2047, 2054, 2057, 2063, 2070, 2075, 2081, 2087, 2089, 2093, 2103, 2111, 2116, 2123, 2134, 2141, 2144, 2154, 2157, 2163, 2171, 2176, 2182, 2192, 2202, 2209, 2216, 2218, 2220, 2225, 2228, 2233, 2236, 2239, 2249, 2257, 2260, 2263, 2267, 2273, 2283, 2287, 2293, 2296, 2301, 2309, 2312, 2317, 2319, 2321, 2327, 2336, 2344, 2346, 2352, 2354, 2358, 2360, 2369, 2374, 2380, 2386, 2391, 2394, 2398, 2404, 2406, 2411, 2415, 2419, 2427, 2430, 2438, 2445, 2452, 2458, 2467, 2478, 2488, 2491, 2497, 2503, 2505, 2513, 2520, 2530, 2537, 2543, 2552, 2555, 2563, 2571, 2574, 2578, 2582, 2590, 2593, 2600, 2613, 2618, 2620, 2628, 2637, 2644, 2650, 2655, 2663, 2668, 2673, 2675, 2682, 2687, 2690, 2696, 2704, 2706, 2709, 2717, 2720, 2726, 2733, 2736, 2744, 2751, 2753, 2758, 2761, 2769, 2772, 2780, 2788, 2790]}

3 代码部分

3.0 模型参数

调用了transformer中的默认参数对象，继承了相关的参数项。
ModelArguments, DataTrainingArguments, TrainingArguments
所以，整个模型中的参数是偏多的

3.1 数据预处理

1、数据加载使用的是
dataset中自带的函数：load_dataset（）
2、Tokenizer工具： 分词
add_prefix_space参数的默认值取决于所使用的tokenizer。对于大多数预训练模型，包括BERT和RoBERTa等模型，其默认值为False，因为它们不需要在输入序列前添加空格。而对于一些预训练模型，例如GPT-2和XLNet等模型，其默认值为True，因为它们需要在输入序列前添加空格。在使用tokenizer对象时，可以通过设置add_prefix_space参数来覆盖默认值，以指定是否在输入序列前添加空格。

3.2 模型方法

在整个方法中，用到的embedding有：
实体类型的embedding是：每个实体描述的CLS token对应的token。 h_CLS

text文本中每个token的embedding是：hidden state的最后一层的输出
span的embedding是：

entity type识别中，每个起始位置判断时，使用的向量模型：

综上，我们一共使用了
四个linear函数，做了representation的映射表示学习。

3.1.1 定义

表示的学习

# 实体类型start位置索引识别的linear层
self.type_start_linear = torch.nn.Linear(hf_config.hidden_size, config.linear_size)
# 实体类型end位置索引识别的linear层
self.type_end_linear = torch.nn.Linear(hf_config.hidden_size, config.linear_size)
# 整个span识别实体识别的embedding中linear层
self.type_span_linear = torch.nn.Linear(hf_config.hidden_size, config.linear_size)
# text中token的start位置索引识别的linear层
self.start_linear = torch.nn.Linear(hf_config.hidden_size, config.linear_size)
# text中token的end位置索引识别的linear层
self.end_linear = torch.nn.Linear(hf_config.hidden_size, config.linear_size)
# span识别中text的span的表示学习，span由起始位置。
if config.use_span_width_embedding:self.span_linear = torch.nn.Linear(hf_config.hidden_size * 2 + config.linear_size, config.linear_size)self.width_embeddings = torch.nn.Embedding(config.max_span_width, config.linear_size, padding_idx=0)
else:self.span_linear = torch.nn.Linear(hf_config.hidden_size * 2, config.linear_size)self.width_embeddings = None

权重项的学习

标绿部分的实现

self.start_logit_scale = torch.nn.Parameter(torch.ones([]) * np.log(1 / config.init_temperature))#单项损失函数中权重量，标量张量
self.end_logit_scale = torch.nn.Parameter(torch.ones([]) * np.log(1 / config.init_temperature))
self.span_logit_scale = torch.nn.Parameter(torch.ones([]) * np.log(1 / config.init_temperature))

这张图片中标绿部分的权重好像是直接给定的。

self.start_loss_weight = config.start_loss_weightself.end_loss_weight = config.end_loss_weightself.span_loss_weight = config.span_loss_weight

双塔模型

text和实体type 的encoder的学习

self.text_encoder = AutoModel.from_pretrained(config.pretrained_model_name_or_path,config=hf_config,add_pooling_layer=False)
self.type_encoder = AutoModel.from_pretrained(config.pretrained_model_name_or_path,config=hf_config,add_pooling_layer=False)

3.2.2 forward

每个位置部分是start或者end的索引概率：

# batch_size x num_types x seq_lengthstart_scores = self.start_logit_scale.exp() * type_start_output.unsqueeze(0) @ sequence_start_output.transpose(1, 2)# text中每个位置的token是对应type的起始位置的概率'''这段代码的意义是通过执行矩阵乘法来计算每个文本序列中每个位置作为答案起点的分数，即start_scores。具体来说，代码首先执行以下步骤：调用PyTorch中的exp()方法将self.start_logit_scale中的所有元素取指数。将上一步中得到的张量与type_start_output.unsqueeze(0)执行矩阵乘法，即将self.start_logit_scale中的每个元素分别乘以type_start_output中的每个元素，并将结果相加，得到一个形状为(1, seq_len)的张量。将上一步得到的张量与sequence_start_output.transpose(1, 2)执行矩阵乘法，即将上一步中得到的张量中的每个元素分别乘以sequence_start_output中的每个元素，并将结果相加，得到一个形状为(1, seq_len)的张量。最终得到的start_scores张量的形状为(1, seq_len)，其中第i个元素表示第i个位置作为答案起点的分数。我们可以使用softmax函数对start_scores进行归一化，得到每个位置作为答案起点的概率分布。在模型的训练和推理过程中，我们可以使用start_scores来选择最有可能的答案起点。'''end_scores = self.end_logit_scale.exp() * type_end_output.unsqueeze(0) @ sequence_end_output.transpose(1, 2)

3.3 损失函数

3.4 训练与推理

在代码中的训练和推理部分，使用的是集成的trainer工具包。

在使用Transformer模型进行自然语言处理任务时，可以使用Hugging Face提供的Trainer类来进行模型的训练和评估。Trainer类封装了训练和评估过程中的大量细节，使得用户可以更加方便地进行模型的训练和评估。下面是使用Trainer类进行模型训练和评估的一般步骤：

准备数据集：首先需要将数据集转换为可以被模型接受的格式。对于文本分类等任务，可以使用Hugging
Face提供的Dataset类和DataLoader类将数据集转换为可以被模型接受的格式。

定义模型和优化器：使用Hugging Face提供的AutoModel类加载预训练的Transformer模型，同时定义优化器和损失函数。

定义训练参数：使用Hugging Face提供的TrainingArguments类定义训练参数，例如学习率、训练轮数等。

创建Trainer实例：使用Hugging Face提供的Trainer类创建一个Trainer实例。

开始训练：使用Trainer实例的train()方法开始训练模型。

评估模型：训练完成后，可以使用Trainer实例的evaluate()方法评估模型的性能。

以下是一个简单的使用Trainer进行模型训练和评估的示例代码：

from transformers import AutoModel, AutoTokenizer, Trainer, TrainingArguments
import torch# 1. 准备数据集
train_dataset = ...
val_dataset = ...
data_collator = ...# 2. 定义模型和优化器
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
loss_fn = ...# 3. 定义训练参数
training_args = TrainingArguments(output_dir="./results",evaluation_strategy="steps",eval_steps=500,save_steps=500,num_train_epochs=3,learning_rate=5e-5,per_device_train_batch_size=16,per_device_eval_batch_size=64,warmup_steps=500,weight_decay=0.01,
)# 4. 创建Trainer实例
trainer = Trainer(model=model,args=training_args,train_dataset=train_dataset,eval_dataset=val_dataset,data_collator=data_collator,optimizer=optimizer,compute_metrics=compute_metrics,callbacks=[],
)# 5. 开始训练
trainer.train()# 6. 评估模型
trainer.evaluate()

Ablation study

训练实例

D:\Anocada\envs\pythonProject\python.exe E:/PythonProject_2023/pythonProject_SIM/binder-main/run_ner.py
03/02/2023 22:39:10 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: True
03/02/2023 22:39:10 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=1,
dataloader_pin_memory=True,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=True,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=100,
evaluation_strategy=steps,
fp16=True,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
greater_is_better=True,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=['ner'],
label_smoothing_factor=0.0,
learning_rate=3e-05,
length_column_name=length,
load_best_model_at_end=True,
local_rank=-1,
log_level=passive,
log_level_replica=passive,
log_on_each_node=True,
logging_dir=/tmp/conll03\runs\Mar02_22-39-10_DESKTOP-44EL6IH,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=10,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=f1,
mp_parameters=,
no_cuda=False,
num_train_epochs=20,
optim=adamw_torch,
optim_args=None,
output_dir=/tmp/conll03,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=base-run,
save_on_each_node=False,
save_steps=100,
save_strategy=steps,
save_total_limit=1,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
xpu_backend=None,
)

03/02/2023 22:39:18 - WARNING - datasets.builder - Using custom data configuration default-a33b9b3f9ebc66c0
03/02/2023 22:39:18 - INFO - datasets.builder - Overwrite dataset info from restored data version.
03/02/2023 22:39:18 - INFO - datasets.info - Loading Dataset info from E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model\json\default-a33b9b3f9ebc66c0\0.0.0\e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab
03/02/2023 22:39:18 - WARNING - datasets.builder - Found cached dataset json (E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-a33b9b3f9ebc66c0/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab)
03/02/2023 22:39:18 - INFO - datasets.info - Loading Dataset info from E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-a33b9b3f9ebc66c0/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab
03/02/2023 22:39:18 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-a33b9b3f9ebc66c0/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab\cache-3dd16664e4cd502f.arrow
03/02/2023 22:39:18 - WARNING - datasets.arrow_dataset - Loading cached sorted indices for dataset at E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-a33b9b3f9ebc66c0/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab\cache-9b3867899aa5d37f.arrow
03/02/2023 22:39:18 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-a33b9b3f9ebc66c0/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab\cache-cd523ff19679cbd4.arrow
Running tokenizer on train dataset:   0%|          | 0/1 [00:00<?, ?ba/s]03/02/2023 22:39:43 - INFO - datasets.arrow_dataset - Caching processed dataset at E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-c212c66a12ff2e9f/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab\cache-e0af4479030ebcb8.arrow
Running tokenizer on train dataset: 100%|██████████| 1/1 [01:51<00:00, 111.38s/ba]
Running tokenizer on validation dataset:   0%|          | 0/1 [00:00<?, ?ba/s]03/02/2023 22:41:12 - INFO - datasets.arrow_dataset - Caching processed dataset at E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-c212c66a12ff2e9f/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab\cache-ba5b02e5239fff1d.arrow
Running tokenizer on validation dataset: 100%|██████████| 1/1 [00:00<00:00,  1.62ba/s]
Running tokenizer on prediction dataset:   0%|          | 0/1 [00:00<?, ?ba/s]03/02/2023 22:41:13 - INFO - datasets.arrow_dataset - Caching processed dataset at E:/PythonProject_2023/pythonProject_SIM/binder-main/pretrained model/json/default-c212c66a12ff2e9f/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab\cache-318430783f03d51b.arrow
Running tokenizer on prediction dataset: 100%|██████████| 1/1 [00:00<00:00,  1.98ba/s]
[INFO|trainer.py:565] 2023-03-02 22:41:14,222 >> Using cuda_amp half precision backend
[INFO|trainer.py:711] 2023-03-02 22:41:14,223 >> The following columns in the training set don't have a corresponding argument in `Binder.forward` and have been ignored: token_start_mask, token_end_mask. If token_start_mask, token_end_mask are not expected by `Binder.forward`,  you can safely ignore this message.
[INFO|trainer.py:1650] 2023-03-02 22:41:14,232 >> ***** Running training *****
[INFO|trainer.py:1651] 2023-03-02 22:41:14,232 >>   Num examples = 1547
[INFO|trainer.py:1652] 2023-03-02 22:41:14,232 >>   Num Epochs = 20
[INFO|trainer.py:1653] 2023-03-02 22:41:14,232 >>   Instantaneous batch size per device = 8
[INFO|trainer.py:1654] 2023-03-02 22:41:14,232 >>   Total train batch size (w. parallel, distributed & accumulation) = 8
[INFO|trainer.py:1655] 2023-03-02 22:41:14,232 >>   Gradient Accumulation steps = 1
[INFO|trainer.py:1656] 2023-03-02 22:41:14,233 >>   Total optimization steps = 3880
[INFO|trainer.py:1658] 2023-03-02 22:41:14,237 >>   Number of trainable parameters = 2161775390%|          | 10/3880 [00:16<1:01:16,  1.05it/s]{'loss': 5.772, 'learning_rate': 2.9938144329896908e-05, 'epoch': 0.05}1%|          | 20/3880 [00:25<57:31,  1.12it/s]{'loss': 3.2224, 'learning_rate': 2.9860824742268043e-05, 'epoch': 0.1}1%|          | 30/3880 [00:34<56:55,  1.13it/s]{'loss': 2.7578, 'learning_rate': 2.9783505154639177e-05, 'epoch': 0.15}1%|          | 40/3880 [00:43<1:05:21,  1.02s/it]{'loss': 2.397, 'learning_rate': 2.9706185567010312e-05, 'epoch': 0.21}1%|▏         | 50/3880 [00:52<59:42,  1.07it/s]{'loss': 2.1432, 'learning_rate': 2.9628865979381447e-05, 'epoch': 0.26}2%|▏         | 60/3880 [01:01<57:31,  1.11it/s]{'loss': 1.9551, 'learning_rate': 2.955154639175258e-05, 'epoch': 0.31}2%|▏         | 70/3880 [01:11<58:53,  1.08it/s]{'loss': 1.9147, 'learning_rate': 2.947422680412371e-05, 'epoch': 0.36}2%|▏         | 80/3880 [01:20<57:13,  1.11it/s]{'loss': 1.8418, 'learning_rate': 2.9396907216494844e-05, 'epoch': 0.41}2%|▏         | 90/3880 [01:29<59:00,  1.07it/s]{'loss': 1.4955, 'learning_rate': 2.931958762886598e-05, 'epoch': 0.46}3%|▎         | 100/3880 [01:38<55:58,  1.13it/s][INFO|trainer.py:711] 2023-03-02 22:42:52,527 >> The following columns in the evaluation set don't have a corresponding argument in `Binder.forward` and have been ignored: token_start_mask, token_end_mask, example_id, offset_mapping, split. If token_start_mask, token_end_mask, example_id, offset_mapping, split are not expected by `Binder.forward`,  you can safely ignore this message.
{'loss': 1.467, 'learning_rate': 2.9242268041237113e-05, 'epoch': 0.52}
[INFO|trainer.py:2964] 2023-03-02 22:42:52,528 >> ***** Running Evaluation *****
[INFO|trainer.py:2966] 2023-03-02 22:42:52,528 >>   Num examples = 384
[INFO|trainer.py:2969] 2023-03-02 22:42:52,529 >>   Batch size = 80%|          | 0/48 [00:00<?, ?it/s]6%|▋         | 3/48 [00:00<00:02, 19.40it/s]10%|█         | 5/48 [00:00<00:02, 17.02it/s]15%|█▍        | 7/48 [00:00<00:02, 15.75it/s]19%|█▉        | 9/48 [00:00<00:02, 15.45it/s]23%|██▎       | 11/48 [00:00<00:02, 15.26it/s]27%|██▋       | 13/48 [00:00<00:02, 14.99it/s]31%|███▏      | 15/48 [00:00<00:02, 14.95it/s]35%|███▌      | 17/48 [00:01<00:02, 14.48it/s]40%|███▉      | 19/48 [00:01<00:02, 13.92it/s]44%|████▍     | 21/48 [00:01<00:01, 14.21it/s]48%|████▊     | 23/48 [00:01<00:01, 13.71it/s]52%|█████▏    | 25/48 [00:01<00:01, 13.55it/s]56%|█████▋    | 27/48 [00:01<00:01, 13.92it/s]60%|██████    | 29/48 [00:01<00:01, 14.16it/s]65%|██████▍   | 31/48 [00:02<00:01, 14.42it/s]69%|██████▉   | 33/48 [00:02<00:01, 14.55it/s]73%|███████▎  | 35/48 [00:02<00:00, 14.48it/s]77%|███████▋  | 37/48 [00:02<00:00, 14.02it/s]81%|████████▏ | 39/48 [00:02<00:00, 14.26it/s]85%|████████▌ | 41/48 [00:02<00:00, 14.47it/s]90%|████████▉ | 43/48 [00:02<00:00, 14.60it/s]94%|█████████▍| 45/48 [00:03<00:00, 14.72it/s]

03/02/2023 23:56:07 - INFO - __main__ - *** Evaluate ***98%|█████████▊| 47/48 [00:03<00:00, 15.04it/s]03/02/2023 23:56:16 - INFO - src.utils - Post-processing 216 example predictions split into 384 features.
03/02/2023 23:56:18 - INFO - src.utils - ***** all (5942) *****
03/02/2023 23:56:18 - INFO - src.utils - F1 =  95.8%, Precision =  96.2%, Recall =  95.5% (for span)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  96.5%, Precision =  96.8%, Recall =  96.1% (for start)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  96.5%, Precision =  96.9%, Recall =  96.2% (for end)
03/02/2023 23:56:18 - INFO - src.utils - ***** PER (1842) *****
03/02/2023 23:56:18 - INFO - src.utils - F1 =  97.6%, Precision =  98.0%, Recall =  97.2% (for span)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  98.3%, Precision =  98.7%, Recall =  97.9% (for start)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  98.3%, Precision =  98.7%, Recall =  97.9% (for end)
03/02/2023 23:56:18 - INFO - src.utils - ***** LOC (1837) *****
03/02/2023 23:56:18 - INFO - src.utils - F1 =  97.5%, Precision =  98.2%, Recall =  96.8% (for span)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  97.6%, Precision =  98.3%, Recall =  96.9% (for start)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  97.7%, Precision =  98.4%, Recall =  97.0% (for end)
03/02/2023 23:56:18 - INFO - src.utils - ***** ORG (1341) *****
03/02/2023 23:56:18 - INFO - src.utils - F1 =  94.6%, Precision =  94.8%, Recall =  94.5% (for span)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  95.5%, Precision =  95.7%, Recall =  95.4% (for start)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  95.2%, Precision =  95.4%, Recall =  95.1% (for end)
03/02/2023 23:56:18 - INFO - src.utils - ***** MISC (922) *****
03/02/2023 23:56:18 - INFO - src.utils - F1 =  90.9%, Precision =  90.6%, Recall =  91.1% (for span)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  92.0%, Precision =  91.8%, Recall =  92.3% (for start)
03/02/2023 23:56:18 - INFO - src.utils - F1 =  92.7%, Precision =  92.4%, Recall =  93.0% (for end)
03/02/2023 23:56:18 - INFO - src.utils - Saving predictions to /tmp/conll03\eval_predictions.json.
03/02/2023 23:56:18 - INFO - src.utils - Saving metrics to /tmp/conll03\eval_metrics.json.
***** eval metrics *****epoch          =   20.0eval_f1        = 0.9584eval_precision = 0.9617eval_recall    = 0.9551eval_samples   =    384
03/02/2023 23:56:18 - INFO - __main__ - *** Predict ***
100%|██████████| 48/48 [00:06<00:00,  7.73it/s]
[INFO|trainer.py:711] 2023-03-02 23:56:18,486 >> The following columns in the test set don't have a corresponding argument in `Binder.forward` and have been ignored: token_start_mask, token_end_mask, example_id, offset_mapping, split. If token_start_mask, token_end_mask, example_id, offset_mapping, split are not expected by `Binder.forward`,  you can safely ignore this message.
[INFO|trainer.py:2964] 2023-03-02 23:56:18,488 >> ***** Running Prediction *****
[INFO|trainer.py:2966] 2023-03-02 23:56:18,488 >>   Num examples = 373
[INFO|trainer.py:2969] 2023-03-02 23:56:18,488 >>   Batch size = 898%|█████████▊| 46/47 [00:02<00:00, 15.20it/s]03/02/2023 23:56:27 - INFO - src.utils - Post-processing 231 example predictions split into 373 features.
03/02/2023 23:56:29 - INFO - src.utils - ***** all (5648) *****
03/02/2023 23:56:29 - INFO - src.utils - F1 =  91.8%, Precision =  92.2%, Recall =  91.4% (for span)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  92.8%, Precision =  93.2%, Recall =  92.4% (for start)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  92.9%, Precision =  93.3%, Recall =  92.6% (for end)
03/02/2023 23:56:29 - INFO - src.utils - ***** LOC (1668) *****
03/02/2023 23:56:29 - INFO - src.utils - F1 =  93.1%, Precision =  93.7%, Recall =  92.6% (for span)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  93.7%, Precision =  94.2%, Recall =  93.1% (for start)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  93.4%, Precision =  94.0%, Recall =  92.9% (for end)
03/02/2023 23:56:29 - INFO - src.utils - ***** ORG (1661) *****
03/02/2023 23:56:29 - INFO - src.utils - F1 =  90.4%, Precision =  90.3%, Recall =  90.5% (for span)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  91.5%, Precision =  91.4%, Recall =  91.6% (for start)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  91.9%, Precision =  91.8%, Recall =  92.0% (for end)
03/02/2023 23:56:29 - INFO - src.utils - ***** PER (1617) *****
03/02/2023 23:56:29 - INFO - src.utils - F1 =  96.0%, Precision =  97.2%, Recall =  94.8% (for span)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  97.1%, Precision =  98.3%, Recall =  95.9% (for start)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  97.3%, Precision =  98.5%, Recall =  96.1% (for end)
03/02/2023 23:56:29 - INFO - src.utils - ***** MISC (702) *****
03/02/2023 23:56:29 - INFO - src.utils - F1 =  82.5%, Precision =  81.9%, Recall =  83.0% (for span)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  84.3%, Precision =  83.7%, Recall =  84.9% (for start)
03/02/2023 23:56:29 - INFO - src.utils - F1 =  84.4%, Precision =  83.8%, Recall =  85.0% (for end)
03/02/2023 23:56:29 - INFO - src.utils - Saving predictions to /tmp/conll03\predict_predictions.json.
03/02/2023 23:56:29 - INFO - src.utils - Saving metrics to /tmp/conll03\predict_metrics.json.
***** predict metrics *****epoch           =   20.0predict_samples =    373test_f1         =  0.918test_precision  = 0.9218test_recall     = 0.9143
[INFO|modelcard.py:449] 2023-03-02 23:56:30,832 >> Dropping the following result as it does not have all the necessary fields:
{'dataset': {'name': 'CoNLL2003 conf\\conll03.json', 'type': 'CoNLL2003', 'args': 'conf\\conll03.json'}}
100%|██████████| 47/47 [00:06<00:00,  6.72it/s]