BERT(Bidirectional Encoder Representations from Transformers)是一个预训练的语言表示模型,BertTokenizer就是处理文本数据以适配BERT模型的工具。 GPT2LMHeadModel: GPT-2(Generative Pre-trained Transformer 2)是一个基于Transformer的自回归语言模型,用于生成文本。GPT2LMHeadModel是GPT-2模型的实现,可以用于文本生成...
# 需要导入模块: from transformers import GPT2Tokenizer [as 别名]# 或者: from transformers.GPT2Tokenizer importfrom_pretrained[as 别名]def__init__(self, class_size, pretrained_model="gpt2-medium", cached_mode=False, device="cpu"):super().__init__() self.tokenizer = GPT2Tokenizer.from_pr...
gpt2英文版本,gpt2 at main (hf-mirror.com) 三、工具 transformers 3.5.1,run_clm.py 不使用3.5之前的版本,和其他包有冲突。 四、参数设置 train_data_file=path/gpt2/data/wikitext-2-raw/wiki.train.txt #上述路径下载的wikitext-2-raw文件,并更换后缀名为txt eval_data_file=path/gpt2/data/wiki...
assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large','gpt2-xl'} from transformers import GPT2LMHeadModel print("loading weights from pretrained gpt: %s" % model_type) #n_layer, n_head and n_embd are determined from model_type config_args = { 'gpt2': dict(n_layer=12, ...
import wandb # 1. Start a new run run = wandb.init(project="gpt4") # 2. Save model inputs and hyperparameters config = run.config config.dropout = 0.01 # 3. Log gradients and model parameters run.watch(model) for batch_idx, (data, target) in enumerate(train_loader): ... if bat...
and facilitates understanding of the algorithms implemented by transformer models. We present four case studies where we investigate models ranging from small transformers to GPT-2. In these studies, we demonstrate the characteristics of our method, show the distincti...
from transformers import AutoModelForCausalLM, AutoTokenizer from trl import AutoModelForSequenceClassification, PPOConfig, PPOTrainer # Step 1: Model Instantiation model = AutoModelForSequenceClassification.from_pretrained("gpt2") model_ref = AutoModelForSequenceClassification.from_pretrained("gpt2") ...
# 需要导入模块: from transformers import BertModel [as 别名]# 或者: from transformers.BertModel importfrom_pretrained[as 别名]def__init__( self, class_size=None, pretrained_model="gpt2-medium", classifier_head=None, cached_mode=False, ...
importtorchfromtransformersimportGPT2LMHeadModel, GPT2Config, AutoModelForCausalLM# Step 1: Load the pre-trained GPT-2 XL modelpretrained_model = AutoModelForCausalLM.from_pretrained("gpt2-xl")# Step 2: Calculate the L2 norm of the weights for the pre-trained modelpretrained_weight_norm =0....
精选45+ 个网络结构和 500+ 个预训练模型参数,涵盖业界最全的中文预训练模型:既包括文心NLP大模型的ERNIE、PLATO等,也覆盖BERT、GPT、RoBERTa、T5等主流结构。通过AutoModelAPI一键⚡高速下载⚡。 frompaddlenlp.transformersimport*ernie=AutoModel.from_pretrained('ernie-3.0-medium-zh')bert=AutoModel.from_pret...