u013250861
u013250861

NLP-文本摘要：利用预训练模型进行文本摘要任务【transformers：pipeline、T5、BART、Pegasus】

一、pipeline

可以使用pipeline快速实现文本摘要

from transformers import pipeline

summarizer = pipeline(task="summarization", model='t5-small')

text = """
        summarize: (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

result = summarizer(text, max_length=130, min_length=30, do_sample=False)

print('result = ', result)

打印结果：

Token indices sequence length is longer than the specified maximum sequence length for this model (593 > 512). Running this sequence through the model will result in indexing errors
result =  [{'summary_text': 'a new group of bishops and archbishops will become cardinals . they come from 13 countries from every continent . the new cards are often referred to as the princes of the Catholic Church .'}]

Process finished with exit code 0

二、T5

方式01、from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/t5/modeling_t5.py
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained(r'D:\Pretrained_Model\t5-base')
model = AutoModelForSeq2SeqLM.from_pretrained(r'D:\Pretrained_Model\t5-base')

# 用T5做文本摘要任务，前面加 "summarize:"识别符
text = """
        summarize: (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

打印结果：

Ignored unknown kwarg option direction
inputs =  {'input_ids': tensor([[21603,    10,    41,   254, 17235,    61,  3809,     8,   511,    97,
           383,   112,     3, 16281,  4710,     6, 17384, 11065,    65,  2162,
             3,     9,   126,   563,    13, 25214,     7,    11, 11508, 11514,
         10776,     7,   356,    12,   582,   895, 10270,     7,  1636,    11,
            79,   369,    45,    66,   147,     8,   296,     5, 17384, 11065,
           243,  1771,    24,     3,    88,   133,  1520,     3,     9,  1338,
            13,   895, 10270,     7,    30,  2083,   968,    96,    26,  7920,
            84,    27,    56,   564,   627,   126, 21967,     7,   113,     6,
          1107,    45,  1179,  1440,    45,   334, 10829,     6,  6571,     8,
             3,  8482,     7, 26175,  2416,   344,     8,  2345,    13,  7332,
            11,     8,  1090,  2345,    15,     7,   915,    16,     8,   296,
           976,  1315,    12, 25770,  5061,     5,   368,   895, 10270,     7,
            33,   373,   359,   250,    79,   356,     8,  5739,    16,     8,
          2078,    11,    92, 11924,     8,   416,  2783,    15,     6, 19602,
          5523, 25770, 25224,  1079,   301,     5, 10618,   243,     5,   328,
            33,  1664,     3,  4822,    12,    38,     8, 22277,     7,    13,
             8,  6502,  2345,     5,    37,   126,   895, 10270,     7,   369,
            45,  1440,   224,    38, 22138,     6,   368,  5725,    11, 27274,
             5,    96,  3713,    19,     3,     9,  2783,    15,   113,   182,
           231,  2746,    12,  1535,    91,    12,   151,    30,     8,  6346,
             7,     6,    11,    25,  3133,   217,    24,    16,    48,   356,
           976, 10618,   243,     5,    96,  3774,    31,    60,  2508,    81,
           895, 10270,     7,    45,  3115, 20633,  1747,     6,   114,  9702,
           781,   221,     6,     8,  5824,  3368,    13,   304,  1725,     9,
             6, 21099,     6, 10508,     6, 30758,   535,   299,    21,     8,
           511,    97,   437, 11065,    31,  4356,     6,   150,  5452,   263,
             8,   570,     5,    96,   371,    52, 11389,     7,    31,  3275,
            19,   182,   964,    10,   216,  2746,    12,   281,    12,     8,
         20187,   158,  5082,    88,  2593,  1066,   145,  1747,    24,    33,
           641,   420,    18,    88, 19649,    28,   895, 10270,     7,   976,
         10618,   243,     5, 14702,  5377,   155,   235,     6,     3,     9,
          5812,    13,  2078,   892,    44,  2566,   152,   636,    16,   368,
          5092,     6,  4466,    24, 11065,  2162,   112,   126, 21079,    13,
           895, 10270,     7,    30,     8,  6502,   377, 11535,    13,     8,
         12741,  8237,    63,     6,    84, 18681,    15,     7,     8,   719,
            13,     8, 22673,    12,  1850,    31,  3879,  4687,    16, 15659,
           109,  6015,     5,    96,  7638, 18886,    13,   386,  7624,  1076,
            45,   623,   550,     6,     8, 17384,    31,     7,  3703,    21,
           895, 10270,   497,    24,   334,   415,  2078, 15314,     3,     9,
           286,    44,     8,   600,   953,   535,    86,   119,  1234,     6,
         11065,  2746,     3,     9,    72,    20, 21411,  2078,    11,  2746,
            12,  1616,  5139,   912,    45,   422,  2597,    24,  2561,   623,
            45,  6502,   159,    51,    31,     7,   579,  6881,     6,  5377,
           155,   235,   243,     5,   466,   744,    31,    17,  1243, 11065,
            19,     8,   166, 19068,  5982,    12,     3,     9,   102,  2700,
           895, 10270,     7,    45,     8,  2421,   296,     6,   713,     5,
         22738,    16,     8, 13978,     7,     6,    46,  3094,   381,    13,
          6271,   797,  2078,   904,   130,  2650,   895, 10270,     7,     6,
            11,    16,     8,  8754,     7,     6,   472,     5,  1079,     3,
             4,     4, 13671,     6,  4068, 11065,    54,   106,  1601,   336,
           215,     6,  7817,     8,   166,   895, 10270,     7,    45,  3411,
             6,     8, 12729,    11,  2648,     5,    86,   811,    12,     8,
           627,   126,   895, 10270,     7, 11065,  2650,    30,  1771,     6,
           874, 10611, 11508, 11514, 10776,     7,    11, 25214,     7,    56,
            92,    36, 13242,    38,   895, 10270,     7,     5,  2506,   215,
             6, 17384, 11065,  7817,   957,   126,   895, 10270,     7,     6,
           379, 25214,     7,    45, 22179,    11,  4152,  2917,     9,  1699,
             7,    32,     5, 19602,    31,     7,  4173, 27575,    11,  2144,
         10333,   109,   377,  8038,    76,  9859,    12,    48,   934,     5,
             1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    0,   126,   895, 10270,     7,   369,    45,  1179,  1440,    45,
           334, 10829,     3,     5,    79,    33,  1664,     3,  4822,    12]])

['new cardinals come from 13 countries from every continent . they are sometimes referred to']
['new cardinals come from 13 countries from every continent . they are sometimes referred to']

Process finished with exit code 0

方式02、from transformers import T5Tokenizer, T5ForConditionalGeneration

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/t5/modeling_t5.py
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained(r'D:\Pretrained_Model\t5-base')
model = T5ForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\t5-base')

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

打印结果：

inputs =  {'input_ids': tensor([[   41,   254, 17235,    61,  3809,     8,   511,    97,   383,   112,
             3, 16281,  4710,     6, 17384, 11065,    65,  2162,     3,     9,
           126,   563,    13, 25214,     7,    11, 11508, 11514, 10776,     7,
           356,    12,   582,   895, 10270,     7,  1636,    11,    79,   369,
            45,    66,   147,     8,   296,     5, 17384, 11065,   243,  1771,
            24,     3,    88,   133,  1520,     3,     9,  1338,    13,   895,
         10270,     7,    30,  2083,   968,    96,    26,  7920,    84,    27,
            56,   564,   627,   126, 21967,     7,   113,     6,  1107,    45,
          1179,  1440,    45,   334, 10829,     6,  6571,     8,     3,  8482,
             7, 26175,  2416,   344,     8,  2345,    13,  7332,    11,     8,
          1090,  2345,    15,     7,   915,    16,     8,   296,   976,  1315,
            12, 25770,  5061,     5,   368,   895, 10270,     7,    33,   373,
           359,   250,    79,   356,     8,  5739,    16,     8,  2078,    11,
            92, 11924,     8,   416,  2783,    15,     6, 19602,  5523, 25770,
         25224,  1079,   301,     5, 10618,   243,     5,   328,    33,  1664,
             3,  4822,    12,    38,     8, 22277,     7,    13,     8,  6502,
          2345,     5,    37,   126,   895, 10270,     7,   369,    45,  1440,
           224,    38, 22138,     6,   368,  5725,    11, 27274,     5,    96,
          3713,    19,     3,     9,  2783,    15,   113,   182,   231,  2746,
            12,  1535,    91,    12,   151,    30,     8,  6346,     7,     6,
            11,    25,  3133,   217,    24,    16,    48,   356,   976, 10618,
           243,     5,    96,  3774,    31,    60,  2508,    81,   895, 10270,
             7,    45,  3115, 20633,  1747,     6,   114,  9702,   781,   221,
             6,     8,  5824,  3368,    13,   304,  1725,     9,     6, 21099,
             6, 10508,     6, 30758,   535,   299,    21,     8,   511,    97,
           437, 11065,    31,  4356,     6,   150,  5452,   263,     8,   570,
             5,    96,   371,    52, 11389,     7,    31,  3275,    19,   182,
           964,    10,   216,  2746,    12,   281,    12,     8, 20187,   158,
          5082,    88,  2593,  1066,   145,  1747,    24,    33,   641,   420,
            18,    88, 19649,    28,   895, 10270,     7,   976, 10618,   243,
             5, 14702,  5377,   155,   235,     6,     3,     9,  5812,    13,
          2078,   892,    44,  2566,   152,   636,    16,   368,  5092,     6,
          4466,    24, 11065,  2162,   112,   126, 21079,    13,   895, 10270,
             7,    30,     8,  6502,   377, 11535,    13,     8, 12741,  8237,
            63,     6,    84, 18681,    15,     7,     8,   719,    13,     8,
         22673,    12,  1850,    31,  3879,  4687,    16, 15659,   109,  6015,
             5,    96,  7638, 18886,    13,   386,  7624,  1076,    45,   623,
           550,     6,     8, 17384,    31,     7,  3703,    21,   895, 10270,
           497,    24,   334,   415,  2078, 15314,     3,     9,   286,    44,
             8,   600,   953,   535,    86,   119,  1234,     6, 11065,  2746,
             3,     9,    72,    20, 21411,  2078,    11,  2746,    12,  1616,
          5139,   912,    45,   422,  2597,    24,  2561,   623,    45,  6502,
           159,    51,    31,     7,   579,  6881,     6,  5377,   155,   235,
           243,     5,   466,   744,    31,    17,  1243, 11065,    19,     8,
           166, 19068,  5982,    12,     3,     9,   102,  2700,   895, 10270,
             7,    45,     8,  2421,   296,     6,   713,     5, 22738,    16,
             8, 13978,     7,     6,    46,  3094,   381,    13,  6271,   797,
          2078,   904,   130,  2650,   895, 10270,     7,     6,    11,    16,
             8,  8754,     7,     6,   472,     5,  1079,     3,     4,     4,
         13671,     6,  4068, 11065,    54,   106,  1601,   336,   215,     6,
          7817,     8,   166,   895, 10270,     7,    45,  3411,     6,     8,
         12729,    11,  2648,     5,    86,   811,    12,     8,   627,   126,
           895, 10270,     7, 11065,  2650,    30,  1771,     6,   874, 10611,
         11508, 11514, 10776,     7,    11, 25214,     7,    56,    92,    36,
         13242,    38,   895, 10270,     7,     5,  2506,   215,     6, 17384,
         11065,  7817,   957,   126,   895, 10270,     7,     6,   379, 25214,
             7,    45, 22179,    11,  4152,  2917,     9,  1699,     7,    32,
             5, 19602,    31,     7,  4173, 27575,    11,  2144, 10333,   109,
           377,  8038,    76,  9859,    12,    48,   934,     5,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    0,   126,   895, 10270,     7,   369,    45,  1179,  1440,    45,
           334, 10829,     3,     5,    79,    33,   557,     3,  4822,    12]])

['new cardinals come from 13 countries from every continent . they are often referred to']
['new cardinals come from 13 countries from every continent . they are often referred to']

Process finished with exit code 0

三、BART

方式01、from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/t5/modeling_t5.py
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained(r'D:\Pretrained_Model\bart-base')
model = AutoModelForSeq2SeqLM.from_pretrained(r'D:\Pretrained_Model\bart-base')

# 用T5做文本摘要任务，前面加 "summarize:"识别符
text = """
        summarize: (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

打印结果：

Ignored unknown kwarg option direction
inputs =  {'input_ids': tensor([[    0, 50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437, 40402,
            35,    36, 16256,    43,  2709,     5,   200,    86,   148,    39,
         13102,  5073,     6,  8509,  5075,    34,   585,    10,    92,   333,
             9, 19929,     8,  9599,   428, 32084,   278,     7,   555,  1886,
         17552,   480,     8,    51,   283,    31,    70,    81,     5,   232,
             4, 50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,  8509,
          5075,    26,   395,    14,    37,    74,   946,    10,   529,     9,
          1886, 17552,    15,   902,   501,    22, 37460,    61,    38,    40,
           766,   379,    92,  6293,    54,     6,   567,    31,   508,   749,
            31,   358,  9183,     6, 19318,     5,  9473,  3006,  1168, 26633,
          5678,   227,     5,  2197,     9,  8947,     8,     5,  1989, 37391,
          1455,    11,     5,   232,    60,   309,     7, 11484,  4611,     4,
         50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,   188,  1886,
         17552,    32,   460,   505,   142,    51,   278,     5,  6328,    11,
             5,  2352,     8,    67, 10371,     5,   220, 16627,     6,  3480,
          3596, 11484,  9821,   610,   226,     4,  3823,    26,     4,   252,
            32,  2128,  4997,     7,    25,     5, 39978,     9,     5,  4019,
          2197,     4, 50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,
            20,    92,  1886, 17552,   283,    31,   749,   215,    25, 13934,
             6,   188,  3324,     8,  7095,     4, 50118,  1437,  1437,  1437,
          1437,  1437,  1437,  1437,    22,   713,    16,    10, 16627,    54,
           182,   203,  1072,     7,  1338,    66,     7,    82,    15,     5,
          5510,     6,     8,    47,  2563,   192,    14,    11,    42,   278,
            60,  3823,    26,     4,    22,  1185,   214,  1686,    59,  1886,
         17552,    31,  3700, 16042,  2127,     6,   101,  6268,  3060,  2794,
             6,     5,  3073,  2946,     9, 17922,   102,     6, 12276,     6,
          6547,     6, 17609,    72, 50118,  1437,  1437,  1437,  1437,  1437,
          1437,  1437,   125,    13,     5,   200,    86,   187,  5075,   108,
           729,     6,   117,  1791,   156,     5,   889,     4, 50118,  1437,
          1437,  1437,  1437,  1437,  1437,  1437,    22, 38461,   354,   108,
          6184,    16,   182,   699,    35,    91,  1072,     7,   213,     7,
             5, 20456, 41464,   918,  1195,    87,  2127,    14,    32,   416,
           299,    12, 18888,    19,  1886, 17552,    60,  3823,    26,     4,
         50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,  5469,  3043,
         29765,     6,    10,  3097,     9,  2352,   750,    23,  3350,   260,
           589,    11,   188,  3123,     6,  1581,    14,  5075,   585,    39,
            92, 15777,     9,  1886, 17552,    15,     5,  4019, 37072,     9,
             5, 14230, 39880,     6,    61, 16293,  1626,     5,   825,     9,
             5,  3771,   118,     7,  5772,   108, 32357,    11, 26557,     4,
         50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,    22,  4148,
         23220,     9,   130, 11036,   604,    31,   444,   409,     6,     5,
          8509,    18,  5717,    13, 32533,   224,    14,   358,   400,  2352,
          8613,    10,   317,    23,     5,   380,  2103,    72, 50118,  1437,
          1437,  1437,  1437,  1437,  1437,  1437,    96,    97,  1617,     6,
          5075,  1072,    10,    55, 34930,  2352,     8,  1072,     7,  1798,
          3114,  2956,    31,   650,  1822,    14,  2662,   444,    31, 42580,
            18,   476,  5228,     6,  3043, 29765,    26,     4, 50118,  1437,
          1437,  1437,  1437,  1437,  1437,  1437,   280,   630,    75,  1266,
          5075,    16,     5,    78, 29476,  4822,     7,  9653,  1886, 17552,
            31,     5,  2623,   232,     6,   600,     4, 22856,    11,     5,
         18283,    29,     6,    41,  2284,   346,     9,  5862,   470,  2352,
          2262,    58,  1440,  1886, 17552,     6,     8,    11,     5,  7571,
            29,     6,   312,     4,   610, 26166, 24457,     6,  2661,  5075,
         32839,  1538,    94,    76,     6,  3873,     5,    78,  1886, 17552,
            31,  1429,     6,     5,  5639,     8,  1327,     4, 50118,  1437,
          1437,  1437,  1437,  1437,  1437,  1437,    96,  1285,     7,     5,
           379,    92,  1886, 17552,  5075,  1440,    15,   395,     6,   292,
          3562,  9599,   428, 32084,     8, 19929,    40,    67,    28,  7809,
            25,  1886, 17552,     4, 50118,  1437,  1437,  1437,  1437,  1437,
          1437,  1437,  1426,    76,     6,  8509,  5075,  3873,   753,    92,
          1886, 17552,     6,   217, 19929,    31, 17009,     8, 18294,  1243,
         21433,   139,     4, 50118,  1437,  1437,  1437,  1437,  1437,  1437,
          1437,  3480,    18,  3028, 12032,     8,  4845, 14286,   459,   274,
          5223,   257,  3162,     7,    42,   266,     4, 50118,     2]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    2,     0,  1640, 16256,    43,  1437,  1437,  1437,  2537,  1437,
          1437,    36, 16256,    43,  2709,     5,   200,    86,   148,     2]])

['(CNN)   Â   (CNN)For the second time during']
['(CNN)   Â   (CNN)For the second time during']

Process finished with exit code 0

方式02、from transformers import BartTokenizer, BartForConditionalGeneration

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/t5/modeling_t5.py
from transformers import BartTokenizer, BartForConditionalGeneration

tokenizer = BartTokenizer.from_pretrained(r'D:\Pretrained_Model\bart-base')
model = BartForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\bart-base')

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

打印结果：

inputs =  {'input_ids': tensor([[    0, 50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,  1437,
            36, 16256,    43,  2709,     5,   200,    86,   148,    39, 13102,
          5073,     6,  8509,  5075,    34,   585,    10,    92,   333,     9,
         19929,     8,  9599,   428, 32084,   278,     7,   555,  1886, 17552,
           480,     8,    51,   283,    31,    70,    81,     5,   232,     4,
         50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,  8509,  5075,
            26,   395,    14,    37,    74,   946,    10,   529,     9,  1886,
         17552,    15,   902,   501,    22, 37460,    61,    38,    40,   766,
           379,    92,  6293,    54,     6,   567,    31,   508,   749,    31,
           358,  9183,     6, 19318,     5,  9473,  3006,  1168, 26633,  5678,
           227,     5,  2197,     9,  8947,     8,     5,  1989, 37391,  1455,
            11,     5,   232,    60,   309,     7, 11484,  4611,     4, 50118,
          1437,  1437,  1437,  1437,  1437,  1437,  1437,   188,  1886, 17552,
            32,   460,   505,   142,    51,   278,     5,  6328,    11,     5,
          2352,     8,    67, 10371,     5,   220, 16627,     6,  3480,  3596,
         11484,  9821,   610,   226,     4,  3823,    26,     4,   252,    32,
          2128,  4997,     7,    25,     5, 39978,     9,     5,  4019,  2197,
             4, 50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,    20,
            92,  1886, 17552,   283,    31,   749,   215,    25, 13934,     6,
           188,  3324,     8,  7095,     4, 50118,  1437,  1437,  1437,  1437,
          1437,  1437,  1437,    22,   713,    16,    10, 16627,    54,   182,
           203,  1072,     7,  1338,    66,     7,    82,    15,     5,  5510,
             6,     8,    47,  2563,   192,    14,    11,    42,   278,    60,
          3823,    26,     4,    22,  1185,   214,  1686,    59,  1886, 17552,
            31,  3700, 16042,  2127,     6,   101,  6268,  3060,  2794,     6,
             5,  3073,  2946,     9, 17922,   102,     6, 12276,     6,  6547,
             6, 17609,    72, 50118,  1437,  1437,  1437,  1437,  1437,  1437,
          1437,   125,    13,     5,   200,    86,   187,  5075,   108,   729,
             6,   117,  1791,   156,     5,   889,     4, 50118,  1437,  1437,
          1437,  1437,  1437,  1437,  1437,    22, 38461,   354,   108,  6184,
            16,   182,   699,    35,    91,  1072,     7,   213,     7,     5,
         20456, 41464,   918,  1195,    87,  2127,    14,    32,   416,   299,
            12, 18888,    19,  1886, 17552,    60,  3823,    26,     4, 50118,
          1437,  1437,  1437,  1437,  1437,  1437,  1437,  5469,  3043, 29765,
             6,    10,  3097,     9,  2352,   750,    23,  3350,   260,   589,
            11,   188,  3123,     6,  1581,    14,  5075,   585,    39,    92,
         15777,     9,  1886, 17552,    15,     5,  4019, 37072,     9,     5,
         14230, 39880,     6,    61, 16293,  1626,     5,   825,     9,     5,
          3771,   118,     7,  5772,   108, 32357,    11, 26557,     4, 50118,
          1437,  1437,  1437,  1437,  1437,  1437,  1437,    22,  4148, 23220,
             9,   130, 11036,   604,    31,   444,   409,     6,     5,  8509,
            18,  5717,    13, 32533,   224,    14,   358,   400,  2352,  8613,
            10,   317,    23,     5,   380,  2103,    72, 50118,  1437,  1437,
          1437,  1437,  1437,  1437,  1437,    96,    97,  1617,     6,  5075,
          1072,    10,    55, 34930,  2352,     8,  1072,     7,  1798,  3114,
          2956,    31,   650,  1822,    14,  2662,   444,    31, 42580,    18,
           476,  5228,     6,  3043, 29765,    26,     4, 50118,  1437,  1437,
          1437,  1437,  1437,  1437,  1437,   280,   630,    75,  1266,  5075,
            16,     5,    78, 29476,  4822,     7,  9653,  1886, 17552,    31,
             5,  2623,   232,     6,   600,     4, 22856,    11,     5, 18283,
            29,     6,    41,  2284,   346,     9,  5862,   470,  2352,  2262,
            58,  1440,  1886, 17552,     6,     8,    11,     5,  7571,    29,
             6,   312,     4,   610, 26166, 24457,     6,  2661,  5075, 32839,
          1538,    94,    76,     6,  3873,     5,    78,  1886, 17552,    31,
          1429,     6,     5,  5639,     8,  1327,     4, 50118,  1437,  1437,
          1437,  1437,  1437,  1437,  1437,    96,  1285,     7,     5,   379,
            92,  1886, 17552,  5075,  1440,    15,   395,     6,   292,  3562,
          9599,   428, 32084,     8, 19929,    40,    67,    28,  7809,    25,
          1886, 17552,     4, 50118,  1437,  1437,  1437,  1437,  1437,  1437,
          1437,  1426,    76,     6,  8509,  5075,  3873,   753,    92,  1886,
         17552,     6,   217, 19929,    31, 17009,     8, 18294,  1243, 21433,
           139,     4, 50118,  1437,  1437,  1437,  1437,  1437,  1437,  1437,
          3480,    18,  3028, 12032,     8,  4845, 14286,   459,   274,  5223,
           257,  3162,     7,    42,   266,     4, 50118,     2]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    2,     0,  1640, 16256,    43,   480,  1437,  1437,  2537,  1437,
          1437,  1437,    36, 16256,    43,  2709,     5,   200,    86,     2]])

['(CNN) --  Â    (CNN)For the second time']
['(CNN) --  Â    (CNN)For the second time']

Process finished with exit code 0

四、Pegasus

方式01、from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')
model = AutoModelForSeq2SeqLM.from_pretrained(r'D:\Pretrained_model\pegasus-cnn_dailymail')

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer.encode(text)
inputs = torch.tensor([inputs])

print('inputs = ', inputs)

summary_ids = model.generate(inputs)

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

打印结果：

inputs =  tensor([[  143, 40155,   158,   581,   109,   453,   166,   333,   169, 95987,
           108, 11481,  7756,   148,  1487,   114,   177,   456,   113, 35712,
           111, 66941,   116,   323,   112,   460, 30726,   116,  1315,   111,
           157,   331,   135,   149,   204,   109,   278,   107, 11481,  7756,
           243,  1342,   120,   178,   192,  1137,   114,   988,   113, 30726,
           116,   124,  1538,  1265,   198, 35871,   162,   125,   138,   442,
           738,   177, 18345,   170,   108,   792,   135,  1428,  1105,   135,
           290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,   317,
           109,  1887,   113,  6807,   111,   109,   970, 24353,   799,   115,
           109,   278,   745,   992,   112, 20525,  4474,   107,   351, 30726,
           116,   127,   329,   356,   262,   157,   323,   109,  4104,   115,
           109,  1588,   111,   163, 14094,   109,   352, 32577,   108, 11869,
          4244, 20525, 18672,  1084,  1054,   107,  6611,   243,   107,   322,
           127,  1254,  3795,   112,   130,   109, 54407,   113,   109,  4569,
          1887,   107,   139,   177, 30726,   116,   331,   135,  1105,   253,
           130, 16958,   108,   351,  3571,   111, 14838,   107,   198,   287,
           117,   114, 32577,   170,   221,   249,  1728,   112,  1111,   165,
           112,   200,   124,   109, 11691,   108,   111,   119,  2312,   236,
           120,   115,   136,   323,   745,  6611,   243,   107,   198,   417,
           131,   216,  1767,   160, 30726,   116,   135,  2222, 10912,  1262,
           108,   172,  5365, 23288,   108,   109,  3755,  2273,   113, 43439,
           108, 14668,   108,  6398,   108, 32671,   496,   343,   118,   109,
           453,   166,   381,  7756,   131,  2974,   108,   220,  3361,   266,
           109,   467,   107,   198, 59883,   131,  2293,   117,   221,   786,
           151,   285,  1728,   112,   275,   112,   109, 12483, 26941, 30713,
          3317,   880,   197,  1262,   120,   127,   506,   349,   121, 22564,
           122, 30726,   116,   745,  6611,   243,   107,  8751,  5706,  1418,
           497,   108,   114,  4609,   113,  1588,   689,   134, 69328,   502,
           115,   351,  3477,   108,  3151,   120,  7756,  1487,   169,   177,
         11598,   113, 30726,   116,   124,   109,  4569, 26717,   113,   109,
         60574,   108,   162, 56784,   109,   558,   113,   109, 33806,   112,
          1694,   131, 25910,   115, 26163,   107,   198,  1189, 11733,   113,
           339,  5509,  1024,   135,   571,   429,   108,   109, 11481,   131,
           116,  2257,   118, 30726,   416,   120,   290,   391,  1588,  8068,
           114,   295,   134,   109,   461,   826,   496,   222,   176,   989,
           108,  7756,  1728,   114,   154, 24500,  1588,   111,  1728,   112,
          1232,  6243,   675,   135,   360,  1724,   120,  2051,   571,   135,
         52403,   131,   116,   484,  3853,   108,  5706,  1418,   497,   243,
           107,   485,   591,   131,   144,  1021,  7756,   117,   109,   211,
           110, 39619, 18827,   112, 17717, 30726,   116,   135,   109,  1690,
           278,   108,   577,   107, 16591,   115,   109,  8821,   116,   108,
           142,  2186,   344,   113,  5249,   655,  1588,  3635,   195,  1729,
         30726,   116,   108,   111,   115,   109,  6939,   116,   108,   873,
           107,  1084, 61939, 12964,   108,  2901,  7756, 24828,  3792,   289,
           232,   108,  4486,   109,   211, 30726,   116,   135,  2466,   108,
           109,  6802,   111,  1922,   107,   222,   663,   112,   109,   738,
           177, 30726,   116,  7756,  1729,   124,  1342,   108,   668,  5774,
         66941,   116,   111, 35712,   138,   163,   129,  7051,   130, 30726,
           116,   107,  2882,   232,   108, 11481,  7756,  4486,  1925,   177,
         30726,   116,   108,   330, 35712,   135, 17256,   111, 58499, 55600,
           107, 11869,   131,   116,  4767, 18834,   111,  2333, 65534, 15391,
         28929,  5674,   112,   136,   731,   107,     1]])

summary_ids =  tensor([[    0,   139,   177, 30726,   116,   331,   135,  1105,   253,   130,
         16958,   108,   351,  3571,   111, 14838,   110,   107,   106,  1667,
          3361,   266,   109,   467,   118,   109,   453,   166,   381,  7756,
           131,  2974,   110,   107,     1]])

["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .No Americans made the list for the second time since Francis' election ."]
["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .No Americans made the list for the second time since Francis' election ."]

Process finished with exit code 0

方式02、from transformers import PegasusTokenizer, PegasusForConditionalGeneration

# https://github.com/huggingface/transformers/blob/master/src/transformers/models/pegasus/modeling_pegasus.py
from transformers import PegasusTokenizer, PegasusForConditionalGeneration

tokenizer = PegasusTokenizer.from_pretrained(r'D:\Pretrained_Model\pegasus-cnn_dailymail')
model = PegasusForConditionalGeneration.from_pretrained(r'D:\Pretrained_Model\pegasus-cnn_dailymail')

text = """
         (CNN)For the second time during his papacy, Pope Francis has announced a new group of bishops and archbishops set to become cardinals -- and they come from all over the world.
        Pope Francis said Sunday that he would hold a meeting of cardinals on February 14 "during which I will name 15 new Cardinals who, coming from 13 countries from every continent, manifest the indissoluble links between the Church of Rome and the particular Churches present in the world," according to Vatican Radio.
        New cardinals are always important because they set the tone in the church and also elect the next pope, CNN Senior Vatican Analyst John L. Allen said. They are sometimes referred to as the princes of the Catholic Church.
        The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar.
        "This is a pope who very much wants to reach out to people on the margins, and you clearly see that in this set," Allen said. "You're talking about cardinals from typically overlooked places, like Cape Verde, the Pacific island of Tonga, Panama, Thailand, Uruguay."
        But for the second time since Francis' election, no Americans made the list.
        "Francis' pattern is very clear: He wants to go to the geographical peripheries rather than places that are already top-heavy with cardinals," Allen said.
        Christopher Bellitto, a professor of church history at Kean University in New Jersey, noted that Francis announced his new slate of cardinals on the Catholic Feast of the Epiphany, which commemorates the visit of the Magi to Jesus' birthplace in Bethlehem.
        "On feast of three wise men from far away, the Pope's choices for cardinal say that every local church deserves a place at the big table."
        In other words, Francis wants a more decentralized church and wants to hear reform ideas from small communities that sit far from Catholicism's power centers, Bellitto said.
        That doesn't mean Francis is the first pontiff to appoint cardinals from the developing world, though. Beginning in the 1920s, an increasing number of Latin American churchmen were named cardinals, and in the 1960s, St. John XXIII, whom Francis canonized last year, appointed the first cardinals from Japan, the Philippines and Africa.
        In addition to the 15 new cardinals Francis named on Sunday, five retired archbishops and bishops will also be honored as cardinals.
        Last year, Pope Francis appointed 19 new cardinals, including bishops from Haiti and Burkina Faso.
        CNN's Daniel Burke and Christabelle Fombu contributed to this report.
"""
# CNN/DM答案：
# @highlight
# The 15 new cardinals will be installed on February 14
# @highlight
# They come from countries such as Myanmar and Tonga
# @highlight
# No Americans made the list this time or the previous time in Francis' papacy

inputs = tokenizer(text, max_length=1024, truncation=True, return_tensors='pt')

print('inputs = ', inputs)

summary_ids = model.generate(inputs['input_ids'])

print('\nsummary_ids = ', summary_ids)

print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

打印结果：

inputs =  {'input_ids': tensor([[  143, 40155,   158,   581,   109,   453,   166,   333,   169, 95987,
           108, 11481,  7756,   148,  1487,   114,   177,   456,   113, 35712,
           111, 66941,   116,   323,   112,   460, 30726,   116,  1315,   111,
           157,   331,   135,   149,   204,   109,   278,   107, 11481,  7756,
           243,  1342,   120,   178,   192,  1137,   114,   988,   113, 30726,
           116,   124,  1538,  1265,   198, 35871,   162,   125,   138,   442,
           738,   177, 18345,   170,   108,   792,   135,  1428,  1105,   135,
           290, 10156,   108, 14451,   109,   115,  8597, 32478,  1784,   317,
           109,  1887,   113,  6807,   111,   109,   970, 24353,   799,   115,
           109,   278,   745,   992,   112, 20525,  4474,   107,   351, 30726,
           116,   127,   329,   356,   262,   157,   323,   109,  4104,   115,
           109,  1588,   111,   163, 14094,   109,   352, 32577,   108, 11869,
          4244, 20525, 18672,  1084,  1054,   107,  6611,   243,   107,   322,
           127,  1254,  3795,   112,   130,   109, 54407,   113,   109,  4569,
          1887,   107,   139,   177, 30726,   116,   331,   135,  1105,   253,
           130, 16958,   108,   351,  3571,   111, 14838,   107,   198,   287,
           117,   114, 32577,   170,   221,   249,  1728,   112,  1111,   165,
           112,   200,   124,   109, 11691,   108,   111,   119,  2312,   236,
           120,   115,   136,   323,   745,  6611,   243,   107,   198,   417,
           131,   216,  1767,   160, 30726,   116,   135,  2222, 10912,  1262,
           108,   172,  5365, 23288,   108,   109,  3755,  2273,   113, 43439,
           108, 14668,   108,  6398,   108, 32671,   496,   343,   118,   109,
           453,   166,   381,  7756,   131,  2974,   108,   220,  3361,   266,
           109,   467,   107,   198, 59883,   131,  2293,   117,   221,   786,
           151,   285,  1728,   112,   275,   112,   109, 12483, 26941, 30713,
          3317,   880,   197,  1262,   120,   127,   506,   349,   121, 22564,
           122, 30726,   116,   745,  6611,   243,   107,  8751,  5706,  1418,
           497,   108,   114,  4609,   113,  1588,   689,   134, 69328,   502,
           115,   351,  3477,   108,  3151,   120,  7756,  1487,   169,   177,
         11598,   113, 30726,   116,   124,   109,  4569, 26717,   113,   109,
         60574,   108,   162, 56784,   109,   558,   113,   109, 33806,   112,
          1694,   131, 25910,   115, 26163,   107,   198,  1189, 11733,   113,
           339,  5509,  1024,   135,   571,   429,   108,   109, 11481,   131,
           116,  2257,   118, 30726,   416,   120,   290,   391,  1588,  8068,
           114,   295,   134,   109,   461,   826,   496,   222,   176,   989,
           108,  7756,  1728,   114,   154, 24500,  1588,   111,  1728,   112,
          1232,  6243,   675,   135,   360,  1724,   120,  2051,   571,   135,
         52403,   131,   116,   484,  3853,   108,  5706,  1418,   497,   243,
           107,   485,   591,   131,   144,  1021,  7756,   117,   109,   211,
           110, 39619, 18827,   112, 17717, 30726,   116,   135,   109,  1690,
           278,   108,   577,   107, 16591,   115,   109,  8821,   116,   108,
           142,  2186,   344,   113,  5249,   655,  1588,  3635,   195,  1729,
         30726,   116,   108,   111,   115,   109,  6939,   116,   108,   873,
           107,  1084, 61939, 12964,   108,  2901,  7756, 24828,  3792,   289,
           232,   108,  4486,   109,   211, 30726,   116,   135,  2466,   108,
           109,  6802,   111,  1922,   107,   222,   663,   112,   109,   738,
           177, 30726,   116,  7756,  1729,   124,  1342,   108,   668,  5774,
         66941,   116,   111, 35712,   138,   163,   129,  7051,   130, 30726,
           116,   107,  2882,   232,   108, 11481,  7756,  4486,  1925,   177,
         30726,   116,   108,   330, 35712,   135, 17256,   111, 58499, 55600,
           107, 11869,   131,   116,  4767, 18834,   111,  2333, 65534, 15391,
         28929,  5674,   112,   136,   731,   107,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}

summary_ids =  tensor([[    0,   139,   177, 30726,   116,   331,   135,  1105,   253,   130,
         16958,   108,   351,  3571,   111, 14838,   110,   107,   106,  1667,
          3361,   266,   109,   467,   118,   109,   453,   166,   381,  7756,
           131,  2974,   110,   107,     1]])

["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .No Americans made the list for the second time since Francis' election ."]
["The new cardinals come from countries such as Ethiopia, New Zealand and Myanmar .No Americans made the list for the second time since Francis' election ."]

Process finished with exit code 0

参考资料：
Transformers预训练模型使用：文本摘要 Summarization

你可能感兴趣的:(#,NLP应用/文本摘要,#,Pytorch,文本摘要,预训练模型,T5,pipeline,Pegasus)

  • 机器学习与深度学习间关系与区别 ℒℴѵℯ心·动ꦿ໊ོ꫞ 人工智能学习深度学习python
    一、机器学习概述定义机器学习（MachineLearning,ML）是一种通过数据驱动的方法，利用统计学和计算算法来训练模型，使计算机能够从数据中学习并自动进行预测或决策。机器学习通过分析大量数据样本，识别其中的模式和规律，从而对新的数据进行判断。其核心在于通过训练过程，让模型不断优化和提升其预测准确性。主要类型1.监督学习（SupervisedLearning）监督学习是指在训练数据集中包含输入
  • element实现动态路由+面包屑 软件技术NINI vue案例vue.js前端
    el-breadcrumb是ElementUI组件库中的一个面包屑导航组件，它用于显示当前页面的路径，帮助用户快速理解和导航到应用的各个部分。在Vue.js项目中，如果你已经安装了ElementUI，就可以很方便地使用el-breadcrumb组件。以下是一个基本的使用示例：安装ElementUI（如果你还没有安装的话）:你可以通过npm或yarn来安装ElementUI。bash复制代码npmi
  • 理解Gunicorn：Python WSGI服务器的基石 范范0825 ipythonlinux运维
    理解Gunicorn：PythonWSGI服务器的基石介绍Gunicorn，全称GreenUnicorn，是一个为PythonWSGI（WebServerGatewayInterface）应用设计的高效、轻量级HTTP服务器。作为PythonWeb应用部署的常用工具，Gunicorn以其高性能和易用性著称。本文将介绍Gunicorn的基本概念、安装和配置，帮助初学者快速上手。1.什么是Gunico
  • swagger访问路径 igotyback swagger
    Swagger2.x版本访问地址：http://{ip}:{port}/{context-path}/swagger-ui.html{ip}是你的服务器IP地址。{port}是你的应用服务端口，通常为8080。{context-path}是你的应用上下文路径，如果应用部署在根路径下，则为空。Swagger3.x版本对于Swagger3.x版本（也称为OpenAPI3）访问地址：http://{ip
  • 绘本讲师训练营【24期】8/21阅读原创《独生小孩》 1784e22615e0
    24016-孟娟《独生小孩》图片发自App今天我想分享一个蛮特别的绘本，讲的是一个特殊的群体，我也是属于这个群体，80后的独生小孩。这是一本中国绘本，作者郭婧，也是一个80厚。全书一百多页，均为铅笔绘制，虽然为黑白色调，但并不显得沉闷。全书没有文字，犹如“默片”，但并不影响读者对该作品的理解，反而显得神秘，梦幻，給读者留下想象的空间。作者在前蝴蝶页这样写到：“我更希望父母和孩子一起分享这本书，使他
  • 店群合一模式下的社区团购新发展——结合链动 2+1 模式、AI 智能名片与 S2B2C 商城小程序源码 说私域 人工智能小程序
    摘要：本文探讨了店群合一的社区团购平台在当今商业环境中的重要性和优势。通过分析店群合一模式如何将互联网社群与线下终端紧密结合，阐述了链动2+1模式、AI智能名片和S2B2C商城小程序源码在这一模式中的应用价值。这些创新元素的结合为社区团购带来了新的机遇，提升了用户信任感、拓展了营销渠道，并实现了线上线下的完美融合。一、引言随着互联网技术的不断发展，社区团购作为一种新兴的商业模式，在满足消费者日常需
  • 消息中间件有哪些常见类型 xmh-sxh-1314 java
    消息中间件根据其设计理念和用途，可以大致分为以下几种常见类型：点对点消息队列（Point-to-PointMessagingQueues）：在这种模型中，消息被发送到特定的队列中，消费者从队列中取出并处理消息。队列中的消息只能被一个消费者消费，消费后即被删除。常见的实现包括IBM的MQSeries、RabbitMQ的部分使用场景等。适用于任务分发、负载均衡等场景。发布/订阅消息模型（Pub/Sub
  • 腾讯云技术深度探索：构建高效云原生微服务架构 我的运维人生 云原生架构腾讯云运维开发技术共享
    腾讯云技术深度探索：构建高效云原生微服务架构在当今快速发展的技术环境中，云原生技术已成为企业数字化转型的关键驱动力。腾讯云作为行业领先的云服务提供商，不断推出创新的产品和技术，助力企业构建高效、可扩展的云原生微服务架构。本文将深入探讨腾讯云在微服务领域的最新进展，并通过一个实际案例展示如何在腾讯云平台上构建云原生应用。腾讯云微服务架构概览腾讯云微服务架构基于云原生理念，旨在帮助企业快速实现应用的容
  • LLM 词汇表 落难Coder LLMsNLP大语言模型大模型llama人工智能
    Contextwindow“上下文窗口”是指语言模型在生成新文本时能够回溯和参考的文本量。这不同于语言模型训练时所使用的大量数据集，而是代表了模型的“工作记忆”。较大的上下文窗口可以让模型理解和响应更复杂和更长的提示，而较小的上下文窗口可能会限制模型处理较长提示或在长时间对话中保持连贯性的能力。Fine-tuning微调是使用额外的数据进一步训练预训练语言模型的过程。这使得模型开始表示和模仿微调数
  • 将cmd中命令输出保存为txt文本文件 落难Coder Windowscmdwindow
    最近深度学习本地的训练中我们常常要在命令行中运行自己的代码，无可厚非，我们有必要保存我们的炼丹结果，但是复制命令行输出到txt是非常麻烦的，其实Windows下的命令行为我们提供了相应的操作。其基本的调用格式就是：运行指令>输出到的文件名称或者具体保存路径测试下，我打开cmd并且ping一下百度：pingwww.baidu.com>./data.txt看下相同目录下data.txt的输出：如果你再
  • 基于社交网络算法优化的二维最大熵图像分割 智能算法研学社（Jack旭） 智能优化算法应用图像分割算法php开发语言
    智能优化算法应用：基于社交网络优化的二维最大熵图像阈值分割-附代码文章目录智能优化算法应用：基于社交网络优化的二维最大熵图像阈值分割-附代码1.前言2.二维最大熵阈值分割原理3.基于社交网络优化的多阈值分割4.算法结果：5.参考文献：6.Matlab代码摘要：本文介绍基于最大熵的图像分割，并且应用社交网络算法进行阈值寻优。1.前言阅读此文章前，请阅读《图像分割：直方图区域划分及信息统计介绍》htt
  • 使用 FinalShell 进行远程连接（ssh 远程连接 Linux 服务器） 编程经验分享 开发工具服务器sshlinux
    目录前言基本使用教程新建远程连接连接主机自定义命令路由追踪前言后端开发，必然需要和服务器打交道，部署应用，排查问题，查看运行日志等等。一般服务器都是集中部署在机房中，也有一些直接是云服务器，总而言之，程序员不可能直接和服务器直接操作，一般都是通过ssh连接来登录服务器。刚接触远程连接时，使用的是XSHELL来远程连接服务器，连接上就能够操作远程服务器了，但是仅用XSHELL并没有上传下载文件的功能
  • 2020-04-12每天三百字之连接与替代 冷眼看潮
    不知道是不是好为人师，有时候还真想和别人分享一下我对某些现象的看法或者解释。人类社会不断发展进步的过程，就是不断连接与替代的过程。人类发现了火并应用火以后，告别了茹毛饮血的野兽般的原始生活（火烧、烹饪替代了生食）人类用石器代替了完全手工，工具的使用使人类进步一大步。类似这样的替代还有很多，随着科技的发展，有更多的原始的事物被替代，代之以更高效、更先进的技术。在近现代，汽车替代了马车，高速公路和铁路
  • 【加密社】Solidity 中的事件机制及其应用 加密社 闲侃区块链智能合约区块链
    加密社引言在Solidity合约开发过程中，事件（Events）是一种非常重要的机制。它们不仅能够让开发者记录智能合约的重要状态变更，还能够让外部系统（如前端应用）监听这些状态的变化。本文将详细介绍Solidity中的事件机制以及如何利用不同的手段来触发、监听和获取这些事件。事件存储的地方当我们在Solidity合约中使用emit关键字触发事件时，该事件会被记录在区块链的交易收据中。具体而言，事件
  • 探索OpenAI和LangChain的适配器集成：轻松切换模型提供商 nseejrukjhad langchaineasyui前端python
    #探索OpenAI和LangChain的适配器集成：轻松切换模型提供商##引言在人工智能和自然语言处理的世界中，OpenAI的模型提供了强大的能力。然而，随着技术的发展，许多人开始探索其他模型以满足特定需求。LangChain作为一个强大的工具，集成了多种模型提供商，通过提供适配器，简化了不同模型之间的转换。本篇文章将介绍如何使用LangChain的适配器与OpenAI集成，以便轻松切换模型提供商
  • 使用Faiss进行高效相似度搜索 llzwxh888 faisspython
    在现代AI应用中，快速和高效的相似度搜索是至关重要的。Faiss（FacebookAISimilaritySearch）是一个专门用于快速相似度搜索和聚类的库，特别适用于高维向量。本文将介绍如何使用Faiss来进行相似度搜索，并结合Python代码演示其基本用法。什么是Faiss？Faiss是一个由FacebookAIResearch团队开发的开源库，主要用于高维向量的相似性搜索和聚类。Faiss
  • 使用Apify加载Twitter消息以进行微调的完整指南 nseejrukjhad twittereasyui前端python
    #使用Apify加载Twitter消息以进行微调的完整指南##引言在自然语言处理领域，微调模型以适应特定任务是提升模型性能的常见方法。本文将介绍如何使用Apify从Twitter导出聊天信息，以便进一步进行微调。##主要内容###使用Apify导出推文首先，我们需要从Twitter导出推文。Apify可以帮助我们做到这一点。通过Apify的强大功能，我们可以批量抓取和导出数据，适用于各类应用场景。
  • 深入理解 MultiQueryRetriever：提升向量数据库检索效果的强大工具 nseejrukjhad 数据库python
    深入理解MultiQueryRetriever：提升向量数据库检索效果的强大工具引言在人工智能和自然语言处理领域，高效准确的信息检索一直是一个关键挑战。传统的基于距离的向量数据库检索方法虽然广泛应用，但仍存在一些局限性。本文将介绍一种创新的解决方案：MultiQueryRetriever，它通过自动生成多个查询视角来增强检索效果，提高结果的相关性和多样性。MultiQueryRetriever的工
  • 利用LangChain的StackExchange组件实现智能问答系统 nseejrukjhad langchainmicrosoft数据库python
    利用LangChain的StackExchange组件实现智能问答系统引言在当今的软件开发世界中，StackOverflow已经成为程序员解决问题的首选平台之一。而LangChain作为一个强大的AI应用开发框架，提供了StackExchange组件，使我们能够轻松地将StackOverflow的海量知识库集成到我们的应用中。本文将详细介绍如何使用LangChain的StackExchange组件
  • 如何部分格式化提示模板:LangChain中的高级技巧 nseejrukjhad langchainjava服务器python
    标题:如何部分格式化提示模板:LangChain中的高级技巧内容:如何部分格式化提示模板:LangChain中的高级技巧引言在使用大型语言模型(LLM)时,提示工程是一个关键环节。LangChain提供了强大的提示模板功能,让我们能更灵活地构建和管理提示。本文将介绍LangChain中一个高级特性-部分格式化提示模板,这个技巧可以让你的提示管理更加高效和灵活。什么是部分格式化提示模板?部分格式化提
  • 在一台Ubuntu计算机上构建Hyperledger Fabric网络 落叶无声9 区块链超级账本Hyperledgerfabric区块链ubuntu构建hyperledgerfabric
    在一台Ubuntu计算机上构建HyperledgerFabric网络Hyperledgerfabric是一个开源的区块链应用程序平台，为开发基于区块链的应用程序提供了一个起点。当我们提到HyperledgerFabric网络时，我们指的是使用HyperledgerFabric的正在运行的系统。即使只使用最少数量的组件，部署Fabric网络也不是一件容易的事。Fabric社区创建了一个名为Cello
  • 人工智能时代，程序员如何保持核心竞争力？ jmoych 人工智能
    随着AIGC（如chatgpt、midjourney、claude等）大语言模型接二连三的涌现，AI辅助编程工具日益普及，程序员的工作方式正在发生深刻变革。有人担心AI可能取代部分编程工作，也有人认为AI是提高效率的得力助手。面对这一趋势,程序员应该如何应对?是专注于某个领域深耕细作，还是广泛学习以适应快速变化的技术环境?又或者，我们是否应该将重点转向AI无法轻易替代的软技能？让我们一起探讨程序员
  • MongoDB Oplog 窗口 喝醉酒的小白 MongoDB运维
    在MongoDB中，oplog（操作日志）是一个特殊的日志系统，用于记录对数据库的所有写操作。oplog允许副本集成员（通常是从节点）应用主节点上已经执行的操作，从而保持数据的一致性。它是MongoDB副本集实现数据复制的基础。MongoDBOplog窗口oplog窗口是指在MongoDB副本集中，从节点可以用来同步数据的时间范围。这个窗口通常由以下因素决定：Oplog大小：oplog的大小是有限
  • pyecharts——绘制柱形图折线图 2224070247 信息可视化pythonjava数据可视化
    一、pyecharts概述自2013年6月百度EFE(ExcellentFrontEnd）数据可视化团队研发的ECharts1.0发布到GitHub网站以来，ECharts一直备受业界权威的关注并获得广泛好评，成为目前成熟且流行的数据可视化图表工具，被应用到诸多数据可视化的开发领域。Python作为数据分析领域最受欢迎的语言，也加入ECharts的使用行列，并研发出方便Python开发者使用的数据
  • CX8836：小体积大功率升降压方案推荐（附Demo设计指南） 诚芯微科技 社交电子
    CX8836是一颗同步四开关单向升降压控制器，在4.5V-40V宽输入电压范围内稳定工作，持续负载电流10A，能够在输入高于或低于输出电压时稳定调节输出电压，可适用于USBPD快充、车载充电器、HUB、汽车启停系统、工业PC电源等多种升降压应用场合，为大功率TYPE-CPD车载充电器提供最优解决方案。提供CX8836Demo测试、CX8836样品申请及CX8836方案开发技术支持。CX8836同升
  • 穷人做什么生意最赚钱？10个适合穷人赚钱的路子？ 氧惠爱高省
    不管在什么地方，一般都是穷人占大量数，而富人只有少数，但是它们却掌握着大量的财富。对于穷人来说，想要买车、买房等奢侈品就难如登天，因为他们只能通过打工来赚取几千元的月薪。➤推荐网购返利app“氧惠”，一个领隐藏优惠券+现金返利的平台。氧惠只提供领券返利链接，下单全程都在淘宝、京东、拼多多等原平台，更支持抖音、快手电商、外卖红包返利等。（应用市场搜“氧惠”下载，邀请码:521521，全网优惠上氧惠！
  • 数据仓库——维度表一致性 墨染丶eye 背诵数据仓库
    数据仓库基础笔记思维导图已经整理完毕，完整连接为：数据仓库基础知识笔记思维导图维度一致性问题从逻辑层面来看，当一系列星型模型共享一组公共维度时，所涉及的维度称为一致性维度。当维度表存在不一致时，短期的成功难以弥补长期的错误。维度时确保不同过程中信息集成起来实现横向钻取货活动的关键。造成横向钻取失败的原因维度结构的差别，因为维度的差别，分析工作涉及的领域从简单到复杂，但是都是通过复杂的报表来弥补设计
  • 01-Git初识 Meereen Gitgit
    01-Git初识概念：一个免费开源，分布式的代码版本控制系统，帮助开发团队维护代码作用：记录代码内容。切换代码版本，多人开发时高效合并代码内容如何学：个人本机使用：Git基础命令和概念多人共享使用：团队开发同一个项目的代码版本管理Git配置用户信息配置：用户名和邮箱，应用在每次提交代码版本时表明自己的身份命令：查看git版本号git-v配置用户名gitconfig--globaluser.name
  • 展现思维导图魅力，不断挖掘人生宝藏 思维导图讲师Mandy
    第13期最强思维导图训练营已经结束一周了，但是我依旧是感觉所有学员还在努力的学习，这些学员中有教师、学生、白领、公务员、宝妈等等，只要你努力，只要你想改变自己，任何行业，任何岗位都可以参与进来，28天足以让你见成效，在这28天中，我们的学员不仅仅是收获了一枚毕业证，最重要的是让自己的思维方式得到升级，今天的你为自己投资，明天的你就会感谢你今天的付出，我们来听一听来自13期最强思维导图训练营优秀学员
  • 2019-05-13 王健_100a
    【撒下18:2】大卫打发军兵出战，分为三队：一队在约押手下，一队在洗鲁雅的儿子约押兄弟亚比筛手下，一队在迦特人以太手下。大卫对军兵说：“我必与你们一同出战。”解释：大卫检阅部队，将它分成三队，每队由一位元帅统领；约押与兄弟亚比筛，并迦特人以太共同指挥。大卫想与他们一同出战！应用：作为领袖与军兵一起出战是很重要。领袖在事奉中与信徒一起，领袖在任何的环境里与信徒一起走过。我们要同心协力为主而战。祷告：
  • java数字签名三种方式 知了ing javajdk
    以下3钟数字签名都是基于jdk7的 1，RSA String password="test"; // 1.初始化密钥 KeyPairGenerator keyPairGenerator = KeyPairGenerator.getInstance("RSA"); keyPairGenerator.initialize(51
  • Hibernate学习笔记 caoyong Hibernate
    1>、Hibernate是数据访问层框架，是一个ORM(Object Relation Mapping)框架，作者为:Gavin King 2>、搭建Hibernate的开发环境      a>、添加jar包:      aa>、hibernatte开发包中/lib/required/所
  • 设计模式之装饰器模式Decorator（结构型） 漂泊一剑客 Decorator
    1. 概述        若你从事过面向对象开发，实现给一个类或对象增加行为，使用继承机制，这是所有面向对象语言的一个基本特性。如果已经存在的一个类缺少某些方法，或者须要给方法添加更多的功能（魅力），你也许会仅仅继承这个类来产生一个新类—这建立在额外的代码上。      
  • 读取磁盘文件txt，并输入String 一炮送你回车库 String
    public static void main(String[] args) throws IOException {    String fileContent = readFileContent("d:/aaa.txt");    System.out.println(fileContent);   
  • js三级联动下拉框 3213213333332132 三级联动
    //三级联动 省/直辖市<select id="province"></select> 市/省直辖<select id="city"></select> 县/区 <select id="area"></select>
  • erlang之parse_transform编译选项的应用 616050468 parse_transform游戏服务器属性同步abstract_code
             最近使用erlang重构了游戏服务器的所有代码，之前看过C++/lua写的服务器引擎代码，引擎实现了玩家属性自动同步给前端和增量更新玩家数据到数据库的功能，这也是现在很多游戏服务器的优化方向，在引擎层面去解决数据同步和数据持久化，数据发生变化了业务层不需要关心怎么去同步给前端。由于游戏过程中玩家每个业务中玩家数据更改的量其实是很少
  • JAVA JSON的解析 darkranger java
    // { // “Total”：“条数”， // Code: 1, // // “PaymentItems”:[ // { // “PaymentItemID”:”支款单ID”, // “PaymentCode”:”支款单编号”, // “PaymentTime”:”支款日期”, // ”ContractNo”:”合同号”， //
  • POJ-1273-Drainage Ditches aijuans ACM_POJ
    POJ-1273-Drainage Ditches http://poj.org/problem?id=1273 基本的最大流，按LRJ的白书写的 #include<iostream> #include<cstring> #include<queue> using namespace std; #define INF 0x7fffffff int ma
  • 工作流Activiti5表的命名及含义 atongyeye 工作流Activiti
    activiti5 - http://activiti.org/designer/update在线插件安装 activiti5一共23张表 Activiti的表都以ACT_开头。 第二部分是表示表的用途的两个字母标识。 用途也和服务的API对应。 ACT_RE_*: 'RE'表示repository。 这个前缀的表包含了流程定义和流程静态资源 （图片，规则，等等）。 A
  • android的广播机制和广播的简单使用 百合不是茶 android广播机制广播的注册
          Android广播机制简介 在Android中，有一些操作完成以后，会发送广播，比如说发出一条短信，或打出一个电话，如果某个程序接收了这个广播，就会做相应的处理。这个广播跟我们传统意义中的电台广播有些相似之处。之所以叫做广播，就是因为它只负责“说”而不管你“听不听”，也就是不管你接收方如何处理。另外，广播可以被不只一个应用程序所接收，当然也可能不被任何应
  • Spring事务传播行为详解 bijian1013 javaspring事务传播行为
            在service类前加上@Transactional，声明这个service所有方法需要事务管理。每一个业务方法开始时都会打开一个事务。         Spring默认情况下会对运行期例外(RunTimeException)进行事务回滚。这
  • eidtplus operate 征客丶 eidtplus
    开启列模式: Alt+C 鼠标选择   OR   Alt+鼠标左键拖动 列模式替换或复制内容(多行): 右键-->格式-->填充所选内容-->选择相应操作 OR Ctrl+Shift+V(复制多行数据,必须行数一致) -------------------------------------------------------
  • 【Kafka一】Kafka入门 bit1129 kafka
    这篇文章来自Spark集成Kafka(http://bit1129.iteye.com/blog/2174765)，这里把它单独取出来，作为Kafka的入门吧   下载Kafka http://mirror.bit.edu.cn/apache/kafka/0.8.1.1/kafka_2.10-0.8.1.1.tgz 2.10表示Scala的版本，而0.8.1.1表示Kafka
  • Spring 事务实现机制 BlueSkator spring代理事务
    Spring是以代理的方式实现对事务的管理。我们在Action中所使用的Service对象，其实是代理对象的实例，并不是我们所写的Service对象实例。既然是两个不同的对象，那为什么我们在Action中可以象使用Service对象一样的使用代理对象呢？为了说明问题，假设有个Service类叫AService，它的Spring事务代理类为AProxyService，AService实现了一个接口
  • bootstrap源码学习与示例：bootstrap-dropdown（转帖） BreakingBad bootstrapdropdown
    bootstrap-dropdown组件是个烂东西，我读后的整体感觉。 一个下拉开菜单的设计： <ul class="nav pull-right"> <li id="fat-menu" class="dropdown">
  • 读《研磨设计模式》-代码笔记-中介者模式-Mediator bylijinnan java设计模式
    声明： 本文只为方便我个人查阅和理解，详细的分析以及源代码请移步 原作者的博客http://chjavach.iteye.com/ /* * 中介者模式（Mediator）：用一个中介对象来封装一系列的对象交互。 * 中介者使各对象不需要显式地相互引用，从而使其耦合松散，而且可以独立地改变它们之间的交互。 * * 在我看来，Mediator模式是把多个对象（
  • 常用代码记录 chenjunt3 UIExcelJ#
      1、单据设置某行或某字段不能修改 //i是行号,"cash"是字段名称 getBillCardPanelWrapper().getBillCardPanel().getBillModel().setCellEditable(i, "cash", false); //取得单据表体所有项用以上语句做循环就能设置整行了 getBillC
  • 搜索引擎与工作流引擎 comsci 算法工作搜索引擎网络应用
          最近在公司做和搜索有关的工作，(只是简单的应用开源工具集成到自己的产品中)工作流系统的进一步设计暂时放在一边了，偶然看到谷歌的研究员吴军写的数学之美系列中的搜索引擎与图论这篇文章中的介绍，我发现这样一个关系(仅仅是猜想)   -----搜索引擎和流程引擎的基础--都是图论，至少像在我在JWFD中引擎算法中用到的是自定义的广度优先
  • oracle Health Monitor daizj oracleHealth Monitor
    About Health Monitor Beginning with Release 11g, Oracle Database includes a framework called Health Monitor for running diagnostic checks on the database. About Health Monitor Checks Health M
  • JSON字符串转换为对象 dieslrae javajson
        作为前言,首先是要吐槽一下公司的脑残编译部署方式,web和core分开部署本来没什么问题,但是这丫居然不把json的包作为基础包而作为web的包,导致了core端不能使用,而且我们的core是可以当web来用的(不要在意这些细节),所以在core中处理json串就是个问题.没办法,跟编译那帮人也扯不清楚,只有自己写json的解析了.   
  • C语言学习八结构体，综合应用，学生管理系统 dcj3sjt126com C语言
    实现功能的代码： # include <stdio.h> # include <malloc.h> struct Student { int age; float score; char name[100]; }; int main(void) { int len; struct Student * pArr; int i,
  • vagrant学习笔记 dcj3sjt126com vagrant
    想了解多主机是如何定义和使用的, 所以又学习了一遍vagrant   1. vagrant virtualbox 下载安装 https://www.vagrantup.com/downloads.html https://www.virtualbox.org/wiki/Downloads   查看安装在命令行输入vagrant     2.
  • 14.性能优化-优化-软件配置优化 frank1234 软件配置性能优化
    1.Tomcat线程池 修改tomcat的server.xml文件： <Connector port="8080" protocol="HTTP/1.1" connectionTimeout="20000" redirectPort="8443" maxThreads="1200" m
  • 一个不错的shell 脚本教程 入门级 HarborChung linuxshell
    一个不错的shell 脚本教程 入门级 建立一个脚本 　　Linux中有好多中不同的shell，但是通常我们使用bash (bourne again shell) 进行shell编程，因为bash是免费的并且很容易使用。所以在本文中笔者所提供的脚本都是使用bash（但是在大多数情况下，这些脚本同样可以在 bash的大姐，bourne shell中运行）。 　　如同其他语言一样
  • Spring4新特性——核心容器的其他改进 jinnianshilongnian spring动态代理spring4依赖注入
    Spring4新特性——泛型限定式依赖注入 Spring4新特性——核心容器的其他改进 Spring4新特性——Web开发的增强 Spring4新特性——集成Bean Validation 1.1(JSR-349)到SpringMVC  Spring4新特性——Groovy Bean定义DSL Spring4新特性——更好的Java泛型操作API  Spring4新
  • Linux设置tomcat开机启动 liuxingguome tomcatlinux开机自启动
    执行命令sudo gedit /etc/init.d/tomcat6 然后把以下英文部分复制过去。（注意第一句#!/bin/sh如果不写，就不是一个shell文件。然后将对应的jdk和tomcat换成你自己的目录就行了。 #!/bin/bash # # /etc/rc.d/init.d/tomcat # init script for tomcat precesses
  • 第13章 Ajax进阶（下） onestopweb Ajax
    index.html <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/
  • Troubleshooting Crystal Reports off BW blueoxygen BO
    http://wiki.sdn.sap.com/wiki/display/BOBJ/Troubleshooting+Crystal+Reports+off+BW#TroubleshootingCrystalReportsoffBW-TracingBOE   Quite useful, especially this part: SAP BW connectivity For t
  • Java开发熟手该当心的11个错误 tomcat_oracle javajvm多线程单元测试
    #1、不在属性文件或XML文件中外化配置属性。比如，没有把批处理使用的线程数设置成可在属性文件中配置。你的批处理程序无论在DEV环境中，还是UAT（用户验收 测试）环境中，都可以顺畅无阻地运行，但是一旦部署在PROD 上，把它作为多线程程序处理更大的数据集时，就会抛出IOException，原因可能是JDBC驱动版本不同，也可能是#2中讨论的问题。如果线程数目 可以在属性文件中配置，那么使它成为
  • 正则表达式大全 yang852220741 html编程正则表达式
    今天向大家分享正则表达式大全，它可以大提高你的工作效率 正则表达式也可以被当作是一门语言，当你学习一门新的编程语言的时候，他们是一个小的子语言。初看时觉得它没有任何的意义，但是很多时候，你不得不阅读一些教程，或文章来理解这些简单的描述模式。 一、校验数字的表达式 数字：^[0-9]*$ n位的数字：^\d{n}$ 至少n位的数字：^\d{n,}$ m-n位的数字：^\d{m,n}$
按字母分类： ABCDEFGHIJKLMNOPQRSTUVWXYZ其他