Regarding the token limit, fromhttps://platform.openai.com/docs/guides/chat/managing-tokens ... as total tokens must be below the model’s maximum limit (4096 tokens forgpt-3.5-turbo-0301) Both input and output tokens count toward these quantities. Each model has its own capacity and each ...
input_text = "This is a long piece of text that we want to summarize." input_ids = tokenizer.encode(input_text, return_tensors='tf') generated_output = model.generate( input_ids, max_length=100, temperature=0.7, do_sample=True, num_return_sequences=1, no_repeat_ngram_size=2, earl...
1. 明确目标:在使用ChatGPT之前,我们需要明确自己的需求和目标,这样才能更有针对性地提问或生成文本。
Issue 1: The text is too long Limit the number of words/sentences/characters. 第一次修改的提示语:因为得到的结果太冗长,明确字数不超过50。 提示语也可以是不超过3句话。 Issue 2. Text focuses on the wrong details Ask it to focus on the aspects that are relevant to the intended audience. 第...
Input Data: 你的代码 Output Data: 最后输出代码语法为 TypeScript 这里比较重要的是 Context,我们需要尽可能描述清楚需求信息,让 GPT 能够看懂我们需要什么。 CRISPE 提示框架 这个框架更适合于进阶 Prompt 使用,面对的需求更加具体和复杂。 CR: Capacity and Role(能力与角色)。你希望 ChatGPT 扮演怎样的角色。
model="text-embedding-ada-002", input=text ) """ 因为提示词的长度有限,所以我只取了搜索结果的前三个,如果想要更多的搜索结果,可以把limit设置为更大的值 """ search_result = client.search( collection_name=collection_name, query_vector=sentence_embeddings["data"][0]["embedding"], ...
ChatGPT is a conversational software that uses three advanced models called generative pre-trained transformers (GPT) to generate text and code based on user input. These models have been trained on vast amounts of text data from various sources like books, social media, websites, and Reddit ...
<tr><td colspan="2" style="text-align:center"><input type="submit" value="确定" style="padding:8px 16px;" onClick="this.value='稍等,请求中……';this.disable=true"></td></tr> </table> </form> </body> </html>
针对Q-Former的三个训练任务分别是 Image-Text Contrastive Learning(ITC),Image-grounded Text Generation(ITG),Image-Text Matching(ITM)。其中 ITC 和 ITM 任务,与ALBEF中的实现类似,只不过图像特征改为了Query的特征,具体可以参考代码实现(ITC[5]和ITM[6])。这里比较特别的是ITG任务,与ALBEF中的MLM不同,这...
max_input_size =4096 num_outputs =512 max_chunk_overlap =20 chunk_size_limit =600 prompt_helper = PromptHelper(max_input_size,num_outputs,max_chunk_overlap,chunk_size_limit=chunk_size_limit) llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7,model_name="text-davinci-003",max_tokens=...