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, ...
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,在我的实验中,outputs的类型是torch.int64,如...
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. ...
per_device_train_batch_size=2, per_device_eval_batch_size=2, predict_with_generate=True, logging_steps=2,# set to 1000 for full trainingsave_steps=64,# set to 500 for full trainingeval_steps=64,# set to 8000 for full trainingwarmup_steps=1,# set to 2000 for full trainingmax_steps...
model = 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, ...
model = 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, ...
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...
model = 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, ...
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. ...
(output_dir="./",evaluation_strategy="steps",per_device_train_batch_size=2,per_device_eval_batch_size=2,predict_with_generate=True,logging_steps=2,# set to 1000 for full trainingsave_steps=64,# set to 500 for full trainingeval_steps=64,# set to 8000 for full trainingwarmup_steps=1...