Deberta用于命名实体识别

我使用了预训练的Deberta-v3-base模型，并在Few-NERD上进行了微调。Few-NERD是一个包含超过18万个示例和超过460万个标记的NER数据集。

标记的类别有：人名、组织机构、地点、建筑物、事件、产品、艺术与杂项。

如何使用该模型

from transformers import pipeline

def print_ner(sentences):
    """Cleaning and printing NER results

    """
    for sentence in sentences:
        last_entity_type = sentence[0]['entity']
        last_index = sentence[0]['index']
        word = sentence[0]['word']
        for i, token in enumerate(sentence):
            if (i > 0):
                if (token['entity'] == last_entity_type) and (token['index'] == last_index + 1):
                    word = word + '' + token['word']

                else:
                    word = word.replace('▁', ' ')
                    print(f"{word[1:]} {last_entity_type}")
                    word = token['word']
                last_entity_type = token['entity']
                last_index = token['index']

                if i == len(sentence) - 1:
                    word = word.replace('▁', ' ')
                    print(f"{word[1:]} {last_entity_type}")


pipe = pipeline(model='RashidNLP/NER-Deberta')
sentence = pipe(["Elon Musk will be at SpaceX's Starbase facility in Boca Chica for the orbital launch of starship next month"])
print_ner(sentence)