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text = """Dear Amazon, last week I ordered an Optimus Prime action figure |
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from your online store in Germany. Unfortunately, when I opened the package, |
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I discovered to my horror that I had been sent an action figure of Megatron |
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instead! As a lifelong enemy of the Decepticons, I hope you can understand my |
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dilemma. To resolve the issue, I demand an exchange of Megatron for the |
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Optimus Prime figure I ordered. Enclosed are copies of my records concerning |
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this purchase. I expect to hear from you soon. Sincerely, Bumblebee.""" |
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from transformers import pipeline |
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classifier = pipeline("text-classification") |
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import pandas as pd |
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outputs = classifier(text) |
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pd.DataFrame(outputs) |
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ner_tagger = pipeline("ner", aggregation_strategy="simple") |
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outputs = ner_tagger(text) |
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pd.DataFrame(outputs) |
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reader = pipeline("question-answering") |
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question = "What does the customer want?" |
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outputs = reader(question=question, context=text) |
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pd.DataFrame([outputs]) |
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summarizer = pipeline("summarization") |
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outputs = summarizer(text, max_length=45, clean_up_tokenization_spaces=True) |
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print(outputs[0]['summary_text']) |
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translator = pipeline("translation_en_to_de", |
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model="Helsinki-NLP/opus-mt-en-de") |
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outputs = translator(text, clean_up_tokenization_spaces=True, min_length=100) |
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print(outputs[0]['translation_text']) |
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generator = pipeline("text-generation") |
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response = "Dear Bumblebee, I am sorry to hear that your order was mixed up." |
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prompt = text + "\n\nCustomer service response:\n" + response |
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outputs = generator(prompt, max_length=200) |
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print(outputs[0]['generated_text']) |
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