import torch from transformers import RobertaTokenizer, RobertaModel, RobertaConfig import argparse parser = argparse.ArgumentParser() parser.add_argument("--dataset_path", required=True, metavar="/path/to/dataset/", help="Path of the input MoleculeNet datasets.") parser.add_argument("--model_file...
However, when I load the extended roberta-base tokenizer from the directory ./extended-roberta-base/, the library constructs a trie (see #13220) over the course of ca 20 minutes: >>> from transformers import RobertaTokenizer >>> >>> text_latex_tokenizer = RobertaTokenizer.from_pretrained('...
Install the required dependencies: Make sure you have thetransformerslibrary installed. You can install it usingpip: pip install transformers Choose a pre-trained model: Select a pre-trained model from Hugging Face's model hub that best suits your needs. Models like BERT, GPT, or RoBERTa are ...
# 需要導入模塊: from pytorch_transformers.tokenization_bert import BertTokenizer [as 別名]# 或者: from pytorch_transformers.tokenization_bert.BertTokenizer importfrom_pretrained[as 別名]def__init__(self, params):super(BiEncoderModule, self).__init__() ctxt_bert = BertModel.fro...
# 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__() ...
def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False): from transformers import AutoModelForTokenClassification from transformers import AutoTokenizer # download the model or load the model path weights_path = download_model('bert.ner', cache_dir, process_func=_unzip_process_func, ver...
覆盖45+ 个网络结构和 500+ 个预训练模型参数,既包括百度自研的预训练模型如ERNIE系列, PLATO, SKEP等,也涵盖业界主流的中文预训练模型如BERT,GPT,RoBERTa,T5等。使用AutoModel可以下载不同网络结构的预训练模型。欢迎开发者加入贡献更多预训练模型!🤗 from paddlenlp.transformers import * ernie = AutoModel.from...
>>> from random import randint >>> from transformers import pipeline >>> fillmask = pipeline("fill-mask", model="roberta-base") >>> mask_token = fillmask.tokenizer.mask_token >>> smaller_dataset = dataset.filter(lambda e, i: i<100, with_indices=True) 下面的函数会随机选择一个单词进...
import torch input_ids = torch.tensor(2, 3, 5, 1) # Token的ID vocab_size = 6 # 假设词汇表有6个单词 output_dim = 3 # 我们希望嵌入是3维向量 代码语言:javascript 复制 -input_ids 是一个张量,表示我们的输入文本。-vocab_size 是词汇表的大小,也就是可能的TokenID的总数。-output_dim 是嵌入...
Hugging Face maintains a large model zoo of these pre-trained transformers and makes them easily accessible even for novice users. However, fine-tuning these models still requires expert knowledge, because they’re quite sensitive to their hyperparameters, such as learnin...