If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step. How this can be done any suggestions? As of now , using the method given in the notebook: from transformers import TextDataset dataset = TextDataset( tokenizer=tokeni...
BERT是google最新提出的NLP预训练方法，在大型文本语料库（如维基百科）上训练通用的“语言理解”模型，...
from datasets import load_dataset , Dataset,list_datasets # 设置一下下载的临时路径,要不然每次下载在C盘,空间受不了 datasets = load_dataset("code_search_net", "python", cache_dir='D:\\temp\\huggingface\\chen\\datasets') # 下面是读取数据的过程 # huggingface推荐建立一个迭代器函数,迭代器的好处...
datasets import wandb import os import numpy as np from datasets import load_dataset from transformers import TrainingArguments, Trainer from transformers import AutoTokenizer, AutoModelForSequenceClassification def tokenize_function(examples): return tokenizer(examples["text"], padding="max_length", trunca...
from datasets import load_dataset from transformers import ( AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification, TrainingArguments, Trainer, pipeline ) import evaluate import numpy as np There’s no need to delve deeply into each package at the moment. We’ll certainly expl...
fromtransformersimportAutoModel,AutoModelForCausalLMraw_model=AutoModel.from_config(config)# 没带因果头# raw_model = AutoModelForCausalLM.from_config(config) # 带了因果头print(raw_model)"""LlamaModel((embed_tokens): Embedding(128, 24)(layers): ModuleList((0-3): 4 x LlamaDecoderLayer((self...
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...
开发者ID:Alexander-H-Liu,项目名称:End-to-end-ASR-Pytorch,代码行数:5,代码来源:text.py 示例6: __init__ ▲点赞 5▼ # 需要导入模块: from pytorch_transformers import BertTokenizer [as 别名]# 或者: from pytorch_transformers.BertTokenizer importfrom_pretrained[as 别名]def__init__(self, mode...
fromPIL import Image import requestsfromtransformers import CLIPProcessor, CLIPModel model= CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor= CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") url="http://images.cocodataset.org/val2017/000000039769.jpg"image= Image.ope...
It uses the MODIS fire dataset to adapt a pretrained ResNet-18 model. Read Use PyTorch for Monocular Depth Estimation Learn how to use a model based on Hugging Face Transformers to produce a clipped image with background clutter removed, ultimately creating a depth estimate from a single image...