select(range(30000)) small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(5000)) 微调训练配置 加载BERT 模型 警告通知我们正在丢弃一些权重(vocab_transform 和vocab_layer_norm 层),并随机初始化其他一些权重(pre_classifier 和classifier 层)。在微调模型情况下是绝对正常的,因为...
Returns:- dataset_splits (datasets.dataset_dict.DatasetDict): Preprocessed dataset containing train and test parts.“”” # load dataset (only “train” part will be enough for this lab).dataset = load_dataset(dataset_name, split=”train”) # Filter the dialogues of l...
Pulled using PRAW and Reddit’s APIDataset Creator SpacePulls the new Reddit data into a datasetreddit-tools-HF/dataset-creator-reddit-bestofredditorupdates(space)- Scheduled dataset pull job- Monitoring of Process 1 via Log VisualizationRaw DatasetThe latest aggregation of raw data fromr/bestofr...
构建Dataset 构建DataLoader 构建普通collate_fn packing_collate_fn fromtypingimportCallableimporttorchfromtorch.utils.dataimportDatasetfromtqdmimporttqdmdefpreprocess_data(data,input_template=None,input_key="input",output_key=None,apply_chat_template=None):ifapply_chat_template:# 使用模型自带的对话模板ifoutpu...
正如我们在 第五章 学到的, Dataset.filter() 函数可以让我们非常有效地对数据集进行切片,所以我们可以定义一个简单的函数来进行此操作:def filter_books(example): return ( example["product_category"] == "book" or example["product_category"] == "digital_ebook_purchase" )...
eval_dataset=eval_dataset.map( preprocess_function, batched=True, num_proc=num_proc, remove_columns=original_columns, ) eval_dataset=eval_dataset.filter(lambdax: len(x["input_ids_j"]) <= script_args.max_lengthandlen(x["input_ids_k"]) <=script_args.max_length ...
dataset = dataset.filter(lambda x: len(x[“dialogue”]) > input_min_text_length and len(x[“dialogue”]) <= input_max_text_length, batched=False) # Prepare tokenizer. Setting device_map=”auto” allows to switch between GPU and CPU automatically. ...
The datasets viewer also allows you to search and filter datasets, which can be valuable to potential dataset users, understanding the nature of a dataset more quickly. The dataset viewer for the multiconer_v2 Named Entity Recognition dataset. Community tools Alongside the datase...
ds=Dataset.from_dict({"text":texts, })deftokenize(sample): sample["input_ids"]=tokenizer.encode(sample["text"])[:] sample["query"]=tokenizer.decode(sample["input_ids"])returnsample ds=ds.map(tokenize, batched=False) ds=ds.filter(lambdax:len(x["input_ids"])<=256) ...
DataSet 获取此表所属的DataSet。 DefaultView 获取可能包括筛选视图或游标位置的表的自定义视图。 HasErrors 获取一个值,该值指示该表所属的DataSet 的任何表的任何行中是否有错误。 MinimumCapacity 获取或设置该表最初的起始大小。该表中行的最初起始大小。默认值为 50。