metadata

license: mit
language:
  - en
  - zh
  - de
  - fr
  - ja
  - ko
  - es
widget:
  - text: Hi assistant How can I help you
  - text: Guten Morgen! Wie kann ich Ihnen helfen?
  - text: >-
      どうすれば運動を続けられますか？
      運動を続けることは、健康的な生活を維持する上で非常に重要ですが、モチベーションを維持することが難しい場合があります。以下にいくつかの方法を紹介します。
  - text: >-
      세계 1차 대전은 1914년부터 1918년까지 전 세계적으로 벌어진 대규모 전쟁입니다. 주요한 참전국으로는 독일, 오스트리아-헝가리
      제국, 영국, 프랑스, 러시아, 이탈리아, 미국 등이 있었습니다.
  - text: こんにちは！お元気ですか？何かお手伝いできることがありますか？
  - text: user&#58; Python 和 C++ 哪个更好学？哪个更强大？我该怎么选择？
  - text: >-
      在大熱天裡，墨鏡的銷售與冰淇淋的銷售有著高度相關性。當天氣很熱的時候，兩個都十分熱賣，而天氣轉涼以後兩者的銷售就底落谷底。當有一天，墨鏡批發商車輛在上班途中拋錨，因此無法開業，導致墨鏡銷售變
      0 。請問當天冰淇淋的銷售如何？
  - text: >-
      user&#58; Good morning\n assistant&#58; Good morning! How can I assist you
      today?
pipeline_tag: text2text-generation
tags:
  - text-generation-inference

Generate title for conversation

How to use

model_name = "theblackcat102/alpaca-title-generator-mt0-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
question = 'Hi\nHow can I help you?'
encodes = tokenizer(question, return_tensors='pt')
outputs = model.generate(encodes.input_ids, 
    max_length=512,
    do_sample=True,
    repetition_penalty=1.2,
    top_k=50,
    num_return_sequences=1,
    early_stopping=True
)
for i, beam_output in enumerate(outputs):
    print('-----')
    print("{}".format(tokenizer.decode(beam_output, skip_special_tokens=True)))
# > Help requested.

Generate title data

data was generated using response pair from yahma/alpaca-cleaned and use openai turbo model for title.

""
user: {}
assistant: {}
""

Generate a very short title within 5 words of the conversation above, title must be as relevant as possible. Title language must be same as the context

TITLE: