导入配置文件 model_config = transformers.BertConfig.from_pretrained(MODEL_PATH) # 修改配置 model_config.output_hidden_states = True model_config.output_attentions = True # 通过配置和路径导入模型 model = transformers.BertModel.from_pretrained(MODEL_PATH,config = model_config) 1. 2. 3. 4. 5. ...
model = AutoModelForCausalLM.from_pretrained("distilgpt2") inputs = tok(["The"], return_tensors="pt") generated = model.generate(**inputs, do_sample=False, max_new_tokens=10) forward_confirmation = model(generated).logits.argmax(-1) # We exclude the opposing tips from each sequence:...
os.environ['MASTER_PORT']='8888' mp.spawn(test_model_generation, nprocs=args.gpus, args=(args, )) 几个需要注意的坑: 数据类型。由于generate生成的结果只按其处理的batch里最长的padding,这会导致不同卡上outputs的长度不一样,需要手动padding。padding时一定要保证padding的数据和outputs的类型一致,在我的...
定义LuduanForCausalLM, 继承PtreTrainedModel, 里面包含LuduanModel和lm_head, 实现预测下一个词的loss,同时实现prepare_inputs_for_generation函数 ,保证可以调用model.generate函数。 代码实现并且训练完成后,可以将模型发布到 HuggingFace上,注意一下自己的开源协议和代码风格,因为很多常用的模块在tranformers里面都已经...
What does do_sample parameter of the generate method of the Hugging face model do? Generates sequences for models with a language modeling head. The method currently supports greedy decoding, multinomial sampling, beam-search decoding, and beam-search multinomial sampling. do_sample (bool, optional...
#load the distilbert model distilbert=SentenceTransformer('distilbert-base-uncased')#generate the embeddingsforthe wine reviews embeddings=distilbert.encode(df['description'],convert_to_tensor=True) 注意:如果您以前从未下载过该模型,您将看到它下载并可能弹出一些消息。这是正常的。
output = model.generate(input_ids, max_new_tokens=n_steps, do_sample=False) print(tokenizer.decode(output[0])) 1. 2. 3. Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation. Transformers are the most popular toy line in the world, ...
outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) 模型完成了一个合理的补全,尽管有一些额外的 token: Quote:Imaginationismore important than knowledge. Knowledgeislimited. Imagination encircles the world. ...
迭代所有问题,构建context和当前问题的sequence(用正确的model-specific separators, token type ids and attention masks) 将这个sequence传入模型,输出整个sequence上每个token的得分(该token是start index或end index的可能性得分)。 计算输出的softmax结果,获得在各token上的概率值 ...
Seq2SeqTrainermodel = BartForConditionalGeneration.from_pretrained( "facebook/bart-base" )training_args = Seq2SeqTrainingArguments( output_dir="./", evaluation_strategy="steps", per_device_train_batch_size=2, per_device_eval_batch_size=2, predict_with_generate=True, logging...