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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch.nn as nn |
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name = ["negative","neutral","positive"] |
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def main_note(sentence,aspect): |
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tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa-v1.1") |
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model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-base-absa-v1.1") |
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input_str = "[CLS]" + sentence + "[SEP]" + aspect + "[SEP]" |
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inputs = tokenizer(input_str, return_tensors="pt") |
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outputs = model(**inputs) |
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softmax = nn.Softmax(dim=1) |
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outputs = softmax(outputs.logits) |
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result = [round(i,4) for i in outputs.tolist()[0]] |
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return dict(zip(name,result)) |
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iface = gr.Interface( |
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fn = main_note, |
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inputs=["text","text"], |
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outputs = gr.outputs.Label(), |
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examples=[["1.) Instead of being at the back of the oven, the cord is attached at the front right side.","cord"], |
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["The pan I received was not in the same league as my old pan, new is cheap feeling and does not have a plate on the bottom.","pan"], |
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["The pan I received was not in the same league as my old pan, new is cheap feeling and does not have a plate on the bottom.","bottom"], |
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["They seem much more durable and less prone to staining, retaining their white properties for a much longer period of time.","durability"], |
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["It took some time to clean and maintain, but totally worth it!","clean"], |
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["this means that not only will the smallest burner heat up the pan, but it will also vertically heat up 1\" of the handle.","handle"]]) |
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iface.launch() |
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