from transformers import ( T5Tokenizer, T5TokenizerFast ) import tritonclient.http as httpclient # Initialize client client = httpclient.InferenceServerClient( URL, concurrency=request_parallelism, verbose=verbose ) # Initialize tokenizers from HuggingFace to do pre and post processings # (convert text...
import torch import numpy as np import pandas as pd from transformers import ( T5ForConditionalGeneration, MT5ForConditionalGeneration, ByT5Tokenizer, PreTrainedTokenizer, RobertaTokenizer, T5TokenizerFast as T5Tokenizer, MT5TokenizerFast as MT5Tokenizer, ) from transformers import AutoTokenizer from torch...
from transformers import ( T5Tokenizer, T5TokenizerFast ) import tritonclient.http as httpclient # Initialize client client = httpclient.InferenceServerClient( URL, concurrency=request_parallelism, verbose=verbose ) # Initialize tokenizers from HuggingFace to do pre and post processings # (con...
这里调用T5Tokenizer的父类PreTrainedTokenizer类别的内容 classPreTrainedTokenizer(PreTrainedTokenizerBase):def__init__(self,**kwargs):super().__init__(**kwargs)# Added tokens - We store this for both slow and fast tokenizers# until the serialization of Fast tokenizers is updatedself.added_tokens...
class T5TokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" T5 tokenizer (backed by HuggingFace's `tokenizers` library). Based on `SentencePiece <https://github.com/google/sentencepiece>`__ . This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFa...
BTW, it's recommend to always use the same model identifier for model and tokenizer, even though in this case it would not have made a difference. So: tokenizer=T5TokenizerFast.from_pretrained('google/t5-v1_1-base' • edited Hi@patrickvonplaten ...
“肉鸡”也称傀儡机，是指可以被黑客远程控制的机器。比如用"灰鸽子"等诱导客户点击或者电脑被黑客攻破或...
The tokenizer is instantiated using the T5Tokenizer module and the name of the model Using the from_pretrained function from T5ForConditionalGeneration, the model is loaded With the DataCollectorForSeq2Seq, a data collector is created to be used for the question-answering task ...
用fastT5输出onnx推理模型model_name_or_path="你的现有模型路径"custom_output_path="你需要生成的onnx模型路径"# 生成并加载模型路径model=export_and_get_onnx_model(model_name_or_path,custom_output_path=custom_output_path,quantized=False)tokenizer=AutoTokenizer.from_pretrained(model_name_or_path)t_...
("nezha", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "nllb", 29 changes: 29 additions & 0 deletions 29 src/transformers/models/myt5/__init__.py Original file line numberDiff line numberDiff line change @@ -0,0 +1,29 @@ # Copyrigh...