提交运行脚本

app.py

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+from transformers import pipeline, M2M100ForConditionalGeneration, M2M100Tokenizer, AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+translator_1 = pipeline("translation", model = "penpen/novel-zh-en", max_time = 7)
+
+translator_2_model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B")
+translator_2_tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B")
+
+translator_3_model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+translator_3_tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+
+def model_1(text):
+    return translator_1(text)[0]["translation_text"]
+
+def model_2(text):
+    translator_2_tokenizer.src_lang = "zh"
+    encoded_zh = translator_2_tokenizer(text, return_tensors = "pt", truncation = True, max_length = 512)
+    generated_tokens = translator_2_model.generate(**encoded_zh, forced_bos_token_id = translator_2_tokenizer.get_lang_id("en"))
+    return translator_2_tokenizer.batch_decode(generated_tokens, skip_special_tokens = True)[0]
+
+def model_3(text):
+    batch = translator_3_tokenizer(text, return_tensors = "pt", truncation = True, max_length = 512)
+    generated_tokens = translator_3_model.generate(**batch)
+    return translator_3_tokenizer.batch_decode(generated_tokens, skip_special_tokens = True)[0]
+    
+def on_click(text):
+    print('input: ', chines_text)
+    res_1 = model_1(text)
+    print('model_1: ', res_1)
+    res_2 = model_2(text)
+    print('model_2: ', res_2)
+    res_3 = model_3(text)
+    print('model_3: ', res_3)
+    print('----------------------------')
+    return res_1, res_2, res_3
+
+with gr.Blocks() as block:
+    gr.Markdown("<center><h1>中文翻译英文对比</h1></center>")
+    tb_input = gr.Textbox(label = "输入", placeholder = "输入中文句子", lines = 1)
+    btn = gr.Button("翻译", variant = 'primary')
+    tb_trans_1 = gr.Textbox(label = "模型1（penpen/novel-zh-en）")
+    tb_trans_2 = gr.Textbox(label = "模型2（facebook/m2m100_1.2B）")
+    tb_trans_3 = gr.Textbox(label = "模型3（Helsinki-NLP/opus-mt-zh-en）")
+    btn.click(fn = on_click, inputs = tb_input, outputs = [tb_trans_1, tb_trans_2, tb_trans_3])
+gr.close_all()
+block.queue(concurrency_count = 5)
+block.launch()

requirements.txt

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+transformers
+accelerate
+datasets
+evaluate
+sacremoses
+SentencePiece