[2023-07-26T12:45:49Z ERROR cached_path::cache] ETAG fetch for https://huggingface.co/t5-base/resolve/main/tokenizer.json failed with fatal error Traceback (most recent call last): File "/home/lq/ws_vima/VIMA/scripts/example.py", line 74, in tokenizer = Tokenizer.from_pretrained("t...
T5模型可以在HuggingFace网站下载到,比如,T5-Base模型可以从如下页面下载:https://huggingface.co/t5-base。 下面简单给出T5-Base模型的使用实例代码: from openai.embeddings_utils import cosine_similarity from transformers import T5Tokenizer, T5Model import torch tokenizer = T5Tokenizer.from_pretrained('t5-base...
请注意,根据我们的模型扩展策略,后一组CodeT5+模型相对于原始的CodeGen模型引入了微不足道的可训练参数(350M编码器加上2B、6B、16B模型分别为36M、67M、151M的交叉注意力层)。我们分别使用CodeT5 tokenizer和CodeGen tokenizer对这两组模型进行标记化处理。在预训练中,我们采用分阶段策略,首先在大规模单模态数据集...
name=dataset_config)# Load tokenizer of FLAN-t5-basetokenizer = AutoTokenizer.from_pretrained(model_id)print(f"Train dataset size: {len(dataset['train'])}")print(f"Test dataset size: {len(dataset['test'])}")# Train dataset size
tokenizer = T5Tokenizer.from_pretrained('t5-base') model = T5ForConditionalGeneration.from_pretrained('t5-base') 输入文本 input_text = "translate English to French: The quick brown fox jumpsover the lazy dog." 使用T5 模型进行文本扩充 output = model.generate(input_text, max_length=200, num...
fromtransformersimportT5Tokenizer,T5ForConditionalGeneration# 选择模型大小,常见的有 "t5-small", "t5-base", "t5-large", "t5-3b" 和 "t5-11b"model_name = "t5-small"# 加载分词器tokenizer = T5Tokenizer.from_pretrained(model_name)# 加载预训练模型model = T5ForConditionalGeneration.from_pretrained(...
除了用这个新 Tokenizer 来训练 T5 PEGASUS 外,我们还用它来重新训练了一版 WoBERT 模型(WoBERT+),也欢迎读者尝试。 预训练任务 对于预训练任务,我们希望更加接近自然语言生成(而不是像 T5 那样的只预测挖空部分),并且尽可能具有实用价值。为此,我们关注到了 PEGASUS,来自论文《PEGASUS: Pre-training with Extracted...
checkpoint_path = '/root/kg/bert/mt5/mt5_base/model.ckpt-1000000' spm_path = '/root/kg/bert/mt5/sentencepiece_cn.model' keep_tokens_path = '/root/kg/bert/mt5/sentencepiece_cn_keep_tokens.json' # 加载分词器 tokenizer = SpTokenizer(spm_path, token_start=None, token_end='</s>') ...
checkpoint_path ='/root/kg/bert/mt5/mt5_base/model.ckpt-1000000' spm_path ='/root/kg/bert/mt5/sentencepiece_cn.model' keep_tokens_path ='/root/kg/bert/mt5/sentencepiece_cn_keep_tokens.json' # 加载分词器 tokenizer = SpTokenizer(spm_path, token_start=None, token_end='</s>') ...
# t5-base tokenizer >>> tokenizer.encode("<extra_id_0>. Hello", add_special_tokens = False) [32099, 3, 5, 8774] # ['<extra_id_0>', ' ▁', '.', '▁Hello'] # seqio.SentencePieceVocabulary(vocab_path, extra_ids = 300) >>> processor.encode("<extra_id_0>. Hello") [32099...