sample_outputs = model.generate( input_ids, do_sample=True, max_length=50, num_return_sequences = 5, top_k=50 ) print("Output:\n") for i,sample_output in enumerate(sample_outputs): print(f'{i} : {tokenizer.decode(sample_output, skip_special_tokens=True)}') print('-'*100) top...
torch.manual_seed(0.)output = model.generate(input_ids, do_sample=True, max_length=512, top_p=0.95, top_k=)print("Output:\n" + 100 *'-')print(tokenizer.decode(output[], skip_special_tokens=True))print("" + 100 *'-')模型输出:Output:---In a shocking finding, scientist discove...
bfloat16, device_map="auto") output = pipe("This is a cool example!", do_sample=True, top_p=0.95) import torch from transformers import pipeline pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True}) output = pipe("This is a cool...
min_length:生成序列的最短长度,默认是10。 do_sample(bool, optional, defaults to False) — Whether or not to use sampling ; use greedy decoding otherwise. do_sample:是否开启采样,默认是False,即贪婪找最大条件概率的词。 early_stopping(bool, optional, defaults to False) — Whether to stop the ...
output = model.generate(input_ids, max_new_tokens=n_steps, do_sample=False)print(tokenizer.decode(output[0])) Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation. Transformers are the most popular toy line in the world, ...
do_sample=False) print(tokenizer.decode(output_greedy[0])) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation. In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, ...
print(text_generator("As far as I am concerned, I will", max_length=50, do_sample=False)) 1. 2. 3. 4. 2.直接使用模型 model_path="H:\\code\\Model\\xlnet-base-cased\\" #使用pytorch版本 from transformers import AutoModelWithLMHead, AutoTokenizer ...
do_sample (bool, 可选): 是否从预测分布中进行采样。默认为True。 top_p (float, 可选): 采用nucleus采样时的累积概率阈值。默认为0.8。 temperature (float, 可选): 控制生成文本的随机性的参数。默认为0.8。 logits_processor (LogitsProcessorList, 可选): 用于处理和修改生成步骤中的logits的对象。默认为...
将语言模型头用于模型的生成序列的 tokenID。<提示警告={true}>大多数生成控制参数都设置在 generation_config 中,如果没有传递,则将设置为模型的默认生成配置。您可以通过传递相应的参数来覆盖任何 generation_config,例如.generate(inputs,num_beams=4,do_sample=True)。
{ "dataset_class": None, "do_sample": False, "max_steps": -1, "evaluate_generated_text": False, "num_beams": 1, "max_length": 20, "repetition_penalty": 1.0, "length_penalty": 2.0, "top_k": None, "top_p": None, "num_return_sequences": 1, "early_stopping": True, "prep...