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. ...
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...
"""# 打开图像并转换为 RGB 格式image=Image.open(image_path).convert("RGB")# 定义预处理管道transform=transforms.Compose([transforms.Resize((256,256)),# 调整图像大小为 256x256transforms.ToTensor(),# 转换为 PyTorch 张量])# 应用预处理returntransform(image).unsqueeze(0)# 增加 batch 维度# 示例:...
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...
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, ...
defbatch_iterator(): batch_length =1000foriinrange(0,len(train), batch_length):yieldtrain[i : i + batch_length]["ro"] bpe_tokenizer.train_from_iterator( batch_iterator(), length=len(train), trainer=trainer )''' [00:00:18] Pre-processing sequences ███████████████...
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, ...
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, logging_steps=2, # set to 1000 for...
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, logging_steps=2,# set to 1000 for ...
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, ...