truncation=True),batched=True,remove_columns=["dialogue","summary"])input_lengths=[len(x)forxintokenized_inputs["input_ids"]]# 为了更好的利用,取最大长度的85百分位max_source_length=int(np.percentile(input_lengths,85))print(f"最大源长度: {max_source_length}")#...
defcompute_metrics(eval_preds):preds,labels=eval_preds# decode preds and labelslabels=np.where(labels!=-100,labels,tokenizer.pad_token_id)decoded_preds=tokenizer.batch_decode(preds,skip_special_tokens=True)decoded_labels=tokenizer.batch_decode(labels,skip_special_tokens=True)# rougeLSum expects ne...
一、Flan-T5简介 Flan-T5是一个基于Transformer的NLP模型,具有强大的文本生成和理解能力。它通过对大量文本数据进行学习,可以完成多种NLP任务,包括文本分类、实体识别、问答系统等。Flan-T5的独特之处在于其灵活的模型结构和高效的训练方式,使得它在处理复杂NLP问题时表现出色。 二、Flan-T5环境搭建 在使用Flan-T5之前...
LLMs are models composed of billions of parameters trained on extensive corpora of text, up to hundreds of billions or even a trillion tokens. These models have proven extremely effective for a wide range of text-based tasks, from question answering to sentiment analysi...
outs = model.generate(input_ids=batch['input_ids'], # attention_mask=batch['attention_mask'], max_new_tokens=128,**kwargs) # num_beams=8, early_stopping=True) dec = [tokenizer.decode(ids, skip_special_tokens=True) for ids in outs] ...
"max_new_tokens":256}) 加载T5 flan_t5 = HuggingFaceHub( repo_id="google/flan-t5-xl", model_kwargs={"temperature":0 } ) 这也是在上一个文章中讨论的关于添加记忆 (memory)和其他功能的基础上进行的。 from langchain.chains.conversation.memory import ConversationBufferMemory ...
with the text example you want to summarize. All tasks in this table used the same payload parameters:max_length=150to provide an upper limit on the number of response tokens,no_repeat_ngram_size=5to discourage n-gram repetition, anddo_sample=Falseto disable sampling for ...
if name == "decoder.embed_tokens.weight" or name == "encoder.embed_tokens.weight": logger.debug(f"Skipping tensor {name!r} in safetensors so that convert can end normally.") return [] # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_toke...
add_special_tokens=False).input_ids # 模型的输入格式为：<s>input1</s>tar...
inputs = tokenizer("List a few tips to get good scores in math.", return_tensors="pt") outputs = peft_model.generate(**inputs, max_length=128, do_sample=True) print(tokenizer.batch_decode(outputs, skip_special_tokens=True))About...