output_beam = model.generate(input_ids, max_length=max_length, num_beams=5, do_sample=False, no_repeat_ngram_size=2) logp = sequence_logprob(model, output_beam, input_len=len(input_ids[0])) print(tokenizer.decode(output_beam[0])) print(f"\nlog-prob: {logp:.2f}") 这还不算太...
add_special_tokens=False, return_tensors="pt") prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)) outputs = model.generate(inputs, max_length=250, do_sample=True, top_p
outputs = model.generate( input_ids=test_data["input_ids"], attention_mask=test_data["attention_mask"], max_length=args.max_rep_len, no_repeat_ngram_size=3, num_beams=10, ) if outputs.size(1) < args.max_rep_len: # 每个卡上可能decode出来的长度不一样,需要padding batch_pred_padding...
为了看看我们如何利用温度来影响生成的文本,让我们通过在generate()函数中设置温度参数,以T=2为例进行采样(我们将在下一节解释top_k参数的含义): output_temp = model.generate(input_ids, max_length=max_length, do_sample=True, temperature=2.0, top_k=0) print(tokenizer.decode(output_temp[0])) 1. ...
input_ids=tokenizer(input_txt,return_tensors="pt")["input_ids"].to(device)output_greedy=model.generate(input_ids,max_length=max_length,do_sample=False)print(tokenizer.decode(output_greedy[0])) 代码语言:javascript 复制 Setting`pad_token_id`to`eos_token_id`:50256foropen-end generation.In a...
max_lengthandmax_new_tokensin.generate()#20894 Closed 2 of 4 tasks bofenghuangopened this issueDec 25, 2022· 3 comments· Fixed by#20911 Closed opened this issueDec 25, 2022· 3 comments Hi@gante, I got some error related to the change ofmax_lengthandmax_new_tokensin this PR#2038...
In many batch generation for OPT models, model.generate() stops the generation once the longest sequence in the batch reaches max_length, even if shorter sequences in the same batch haven't reached the max_length yet. This leads to inconsistent behavior for generation when using batches of di...
predict_with_generate=True,fp16=False,)trainer=Seq2SeqTrainer(model,args,train_dataset=datasets["...
seq2seq or sequence-to-sequence: 序列到序列(模型)。从输入中生成新序列的模型,如翻译模型或总结模型(如Bart或T5)。models that generate a new sequence from an input, like translation models, or summarization models (such as Bart or T5).
response_tensors = ppo_trainer.generate(query_tensors, **generation_kwargs) batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors] ### Compute reward score texts = [q + r for q, r in zip(batch["query"], batch["response"])] pipe...