model=GPT2LMHeadModel.from_pretrained(model_name).to(device)### Resize the embedding layer to the desired size model.resize_token_embeddings(len(tokenizer),desired_embedding_size)model=model.to(device)## save tokenizer and model to harddisk tokenizer.save_pretrained(result_dir)model.save_pretrain...
model = GPT2LMHeadModel.from_pretrained("distilgpt2") tokenizer 初始化 tokenizer= GPT2Tokenizer.from_pretrained("distilgpt2") eos_token, eos_token_id = tokenizer.eos_token, tokenizer.eos_token_id print(eos_token, eos_token_id) # <|endoftext|> 50256 数据预处理 1)文本数据转成id标号 def...
deep-neural-networksimage-to-textclipgpt2tokenizer UpdatedFeb 2, 2024 Python SynthWomb/Synthia: Python project using Hugging Face Transformers for GPT-2 NLP, TensorFlow for model management, and SDXL-Turbo for image processing. DeepFace predicts gender. Simple CLI offers Text-to-image and Image-to...
gpt2_type="gpt2", max_length=1024): self.tokenizer = GPT2Tokenizer.from_pretrained(gpt2_type) self.lyrics = [] for row in df['Lyric']: self.lyrics.append(torch.tensor( self.tokenizer.encode(f"<|{control_code}|>{row[:max_length]}<|endoftext|>") )) if tru...
tokenizer = GPT2Tokenizer.from_pretrained("gpt2") context = torch.tensor([tokenizer.encode("The planet earth")]) def generate(context, ntok=20): for _ in range(ntok): out = model(context) logits = out[:, -1, :] indices_to_remove = logits < torch.topk(logits, 10)[0][..., ...
使用PyTorch-Transformers 模型库,先设置好准备输入模型的例子,使用GPT2Tokenizer()建立分词器对象对原句编码。 代码语言:javascript 复制 importtorch from pytorch_transformersimportGPT2Tokenizerimportlogging logging.basicConfig(level=logging.INFO)# 载入预训练模型的分词器 ...
gpt2的语言模型为GPT2LMHeadModel, tokenlizer模型为GPT2Tokenizer, GPT2LMHeadModel的主要包含一个GPT2Model 和一个lm_head的头结构。 classGPT2LMHeadModel(GPT2PreTrainedModel):_keys_to_ignore_on_load_missing=[r"attn.masked_bias",r"attn.bias",r"lm_head.weight"]def__init__(self,config):super...
self.tokenizer.encode(f"<|{control_code}|>{row[:max_length]}<|endoftext|>") )) if truncate: self.lyrics = self.lyrics[:20000] self.lyrics_count = len(self.lyrics) def __len__(self): return self.lyrics_count def __getitem__(self, item): ...
其中,tokenizer将英文单词转换为对应的向量,而preprocess函数则调用tokenizer编码函数,返回符合输入格式要求的张量。 加载预训练模型 从Hugging Face下载预训练的GPT-2模型,并创建一个新模型来进行微调。 from transformers import GPT2LMHeadModel, GPT2Config config = GPT2Config.from_pretrained('gpt2') model = ...
下面引入 GPT-2 模型,我们将使用在 PyTorch-Transformers 模型库中封装好的 GPT2Tokenizer() 和 GPT2LMHeadModel() 类来实际看一下 GPT-2 在预训练后的对下一个词预测的能力。首先,需要安装 PyTorch-Transformers。 !pip install pytorch_transformers==1.0 # 安装 PyTorch-Transformer ...