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import gradio as gr |
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import torch |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained( |
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"salti/arabic-t5-small-question-paraphrasing", use_fast=True |
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) |
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model = AutoModelForSeq2SeqLM.from_pretrained( |
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"salti/arabic-t5-small-question-paraphrasing" |
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).eval() |
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prompt = "أعد صياغة: " |
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@torch.inference_mode() |
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def paraphrase(question, num_beams, encoder_no_repeat_ngram_size): |
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question = prompt + question |
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input_ids = tokenizer(question, return_tensors="pt").input_ids |
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generated_tokens = ( |
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model.generate( |
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input_ids, |
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num_beams=num_beams, |
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encoder_no_repeat_ngram_size=encoder_no_repeat_ngram_size, |
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) |
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.squeeze() |
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.cpu() |
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.numpy() |
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) |
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return tokenizer.decode(generated_tokens, skip_special_tokens=True) |
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question = gr.inputs.Textbox(label="اكتب سؤالاً باللغة العربية") |
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num_beams = gr.inputs.Slider(1, 10, step=1, default=1, label="Beam size") |
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encoder_no_repeat_ngram_size = gr.inputs.Slider( |
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0, |
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10, |
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step=1, |
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default=3, |
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label="N-grams of this size won't be copied from the input (forces more diverse outputs)", |
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) |
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outputs = gr.outputs.Textbox(label="السؤال بصيغة مختلفة") |
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examples = [ |
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[ |
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"متى تم اختراع الكتابة؟", |
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5, |
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3, |
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], |
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[ |
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"ما عدد حروف اللغة العربية؟", |
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5, |
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3, |
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], |
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[ |
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"ما هو الذكاء الصنعي؟", |
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5, |
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3, |
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], |
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] |
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iface = gr.Interface( |
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fn=paraphrase, |
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inputs=[question, num_beams, encoder_no_repeat_ngram_size], |
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outputs=outputs, |
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examples=examples, |
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title="Arabic question paraphrasing", |
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theme="huggingface", |
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) |
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iface.launch() |
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