Spacy v3数据格式问题中textcat_多标签模型的训练

Question

尝试将csv中的数据转换为DocBin，以训练具有textcat_多标签组件的模型，如下所示：

def convert_cat_annontation_from_sentinelle_db(output_path, nlp, input_path, cats_empty):
    #Takes in a csv file resulting from the SQL query qry_export_rdp_annotations.sql
    db = db = DocBin() # create a DocBin object

    annotated_data = pd.read_csv(input_path)
    print(len(annotated_data))

    for idx, row in annotated_data.iterrows(): # data in previous format
        cats = dict(cats_empty)

        try:
            #fetch article full text from link
            article = newspaper.Article(row['link'])
            article.download()
            article.parse()

            doc = nlp.make_doc(article.text) # create doc object from text

            #Assign 1 to positive match for each category
            cats_list = row["GROUP_CONCAT(j.name)"].split(',')
            for cat in cats_list:
                cats[cat] = 1

            doc.cats = cats
            print(doc)
            print(cats)
            print(doc.cats)
            db.add(doc)
        except:
            cats_list = row["GROUP_CONCAT(j.name)"].split(',')
            continue

    db.to_disk(output_path) # save the docbin object

doc.cats的打印输出示例：

{'Santé': 1, 'Économie': 0, 'Infrastructure': 0, 'Politique fédérale': 0, 'Politique provinciale': 1, 'Politique municipale': 0, 'Éducation': 0, 'Faits divers': 0, 'Culture': 0}

运行spacy trainCLI命令时出现完整错误消息：

ℹ Using CPU

=========================== Initializing pipeline ===========================
[2021-08-18 06:09:46,242] [INFO] Set up nlp object from config
[2021-08-18 06:09:46,259] [INFO] Pipeline: ['tok2vec', 'textcat_multilabel', 'ner', 'parser']
[2021-08-18 06:09:46,266] [INFO] Created vocabulary
[2021-08-18 06:09:50,649] [INFO] Added vectors: fr_core_news_lg
[2021-08-18 06:09:56,557] [INFO] Finished initializing nlp object
[2021-08-18 06:10:07,714] [INFO] Initialized pipeline components: ['tok2vec', 'textcat_multilabel', 'ner', 'parser']
✔ Initialized pipeline

============================= Training pipeline =============================
ℹ Pipeline: ['tok2vec', 'textcat_multilabel', 'ner', 'parser']
ℹ Initial learn rate: 0.001
E    #       LOSS TOK2VEC  LOSS TEXTC...  LOSS NER  LOSS PARSER  CATS_SCORE  ENTS_F  ENTS_P  ENTS_R  DEP_UAS  DEP_LAS  SENTS_F  SCORE 
---  ------  ------------  -------------  --------  -----------  ----------  ------  ------  ------  -------  -------  -------  ------
Traceback (most recent call last):
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/training/loop.py", line 281, in evaluate
    scores = nlp.evaluate(dev_corpus(nlp))
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/language.py", line 1389, in evaluate
    results = scorer.score(examples)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/scorer.py", line 135, in score
    scores.update(component.score(examples, **self.cfg))
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/pipeline/textcat_multilabel.py", line 179, in score
    return Scorer.score_cats(
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/scorer.py", line 465, in score_cats
    auc_per_type[label].score_set(pred_score, gold_score)
KeyError: 'Politique fédérale'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/__main__.py", line 4, in <module>
    setup_cli()
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/cli/_util.py", line 69, in setup_cli
    command(prog_name=COMMAND)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/typer/main.py", line 497, in wrapper
    return callback(**use_params)  # type: ignore
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/cli/train.py", line 59, in train_cli
    train(nlp, output_path, use_gpu=use_gpu, stdout=sys.stdout, stderr=sys.stderr)
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/training/loop.py", line 122, in train
    raise e
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/training/loop.py", line 105, in train
    for batch, info, is_best_checkpoint in training_step_iterator:
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/training/loop.py", line 226, in train_while_improving
    score, other_scores = evaluate()
  File "/Users/alex/PycharmProjects/Ecosysteme_NLP_Sentinelle/python39clean/lib/python3.9/site-packages/spacy/training/loop.py", line 283, in evaluate
    raise KeyError(Errors.E900.format(pipeline=nlp.pipe_names)) from e
KeyError: "[E900] Could not run the full pipeline for evaluation. If you specified frozen components, make sure they were already initialized and trained. Full pipeline: ['tok2vec', 'textcat_multilabel', 'ner', 'parser']"

运行spacy debug dataCLI命令时的输出：

====================== Text Classification (Multilabel) ======================
ℹ Text Classification: 30 label(s)
⚠ Some model labels are not present in the train data. The model
performance may be degraded for these labels after training: 'v', 'F', 'm', 'f',
'É', 'l', 'c', 'q', 'o', ']', 'u', 'I', 'P', 'r', 'a', 'D', 'é', 'S', 't', ',',
'M', ' ', 's', ''', 'd', 'i', 'p', 'e', 'n', '['.

查看该输出中的标签列表，我很确定我的问题在于设置用于设置doc.cats的字典格式的方式，但我似乎找不到正确的格式设置方式。 我肯定它在世界文件的某个地方，但我似乎找不到它，觉得有点傻

Answer 1

来自doc.cats的示例输出看起来不错，但正如您所注意到的，debug data的输出表明有问题。看起来，当您认为在某处使用列表时，可能使用了字符串。您可能只对一两个文档执行此操作，但这会导致更一般的问题。还请注意，坏类别似乎只存在于您的开发数据中

在脚本中，您可以通过执行类似assert cat in cats_empty的操作来检查坏标签，这样您就不会在运行时意外地添加新类别