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""" |
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interface = gr.Interface.load( |
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"models/bert-base-uncased", api_key=None, alias="fill-mask" |
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) |
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""" |
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import gradio as gr |
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from transformers import FillMaskPipeline |
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from transformers import BertTokenizer |
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from modeling_kplug import KplugForMaskedLM |
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model_dir = "models/pretrain/" |
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tokenizer = BertTokenizer.from_pretrained(model_dir) |
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model = KplugForMaskedLM.from_pretrained(model_dir) |
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def fill_mask(text): |
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fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer) |
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outputs = fill_masker(text) |
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return {i["token_str"]: i["score"] for i in outputs} |
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mlm_examples = [ |
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"这款连[MASK]裙真漂亮", |
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"这是杨[MASK]同款包包，精选优质皮料制作", |
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"美颜去痘洁面[MASK]", |
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] |
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mlm_iface = gr.Interface( |
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fn=fill_mask, |
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inputs=gr.inputs.Textbox( |
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label="输入文本", |
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default="这款连[MASK]裙真漂亮"), |
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outputs=gr.Label( |
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label="填词", |
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show_label=False, |
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), |
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examples=mlm_examples, |
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title="文本填词", |
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description='电商领域文本摘要， 基于KPLUG预训练语言模型，' |
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'<a href=""> K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding' |
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' and Generation in E-Commerce (Findings of EMNLP 2021) </a>。' |
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) |
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if __name__ == "__main__": |
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mlm_iface.launch() |
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|
