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""" |
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https://github.com/gradio-app/gradio/blob/299ba1bd1aed8040b3087c06c10fedf75901f91f/gradio/external.py#L484 |
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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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## TODO: |
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1. json_output |
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2. 百分数换成小数 |
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3. |
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""" |
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import gradio as gr |
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from info import article |
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from transformers import FillMaskPipeline |
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from transformers import BertTokenizer |
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from kplug.modeling_kplug import KplugForMaskedLM |
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tokenizer = BertTokenizer.from_pretrained("eson/kplug-base-encoder") |
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model = KplugForMaskedLM.from_pretrained("eson/kplug-base-encoder") |
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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.Textbox( |
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label="输入文本", |
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value="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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), |
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examples=mlm_examples, |
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title="文本填词（Fill Mask）", |
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description='基于KPLUG预训练语言模型', |
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article=article |
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) |
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if __name__ == "__main__": |
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mlm_iface.launch() |
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