import torch from torch.utils.data import Dataset class PreferenceDataset(Dataset): def __init__(self, data, tokenizer): self.data = data # Pre-tokenize texts self.encoded_texts = [] for entry in data: prompt = format_input(entry) rejected_response = entry["rejected"] chosen_response =...
fromdatasetsimportload_datasetfromtransformersimportAutoTokenizer# This can take a few minutes to load, so grab a coffee or tea while you wait!raw_datasets=load_dataset("code_search_net","python")print(raw_datasets["train"])#输出训练的数据列名、总行数print(raw_datasets["train"][123456]["whol...
hf_dataset: if dist.get_local_rank() == 0: dataset = load_dataset(config.dataset.name, split=config.dataset.split) dist.barrier() dataset = load_dataset(config.dataset.name, split=config.dataset.split) dist.barrier() elif tokenizer: dataset = hydra.utils.instantiate(config.dataset) else: ...
My own task or dataset (give details below) Reproduction Run a Python script with the following code: tokenizer = AutoTokenizer.from_pretrained('distilroberta-base') when the program runs at that line, it will report an OS ERR: OSError ...
from datasets import load_dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, pipeline, logging, ) from peft import LoraConfig, PeftModel from trl import SFTTrainer 我们继续分析导入 torch是我们很熟悉的深度学习库,这里我们不需要torch的那些低级功...
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...
# 需要導入模塊: from allennlp.data.tokenizers import Tokenizer [as 別名]# 或者: from allennlp.data.tokenizers.Tokenizer importfrom_params[as 別名]deffrom_params(cls, params: Params)-> 'SciciteDatasetReader':lazy = params.pop('lazy',False) ...
Dataset API: 丰富的中文数据集 Dataset API提供便捷、高效的数据集加载功能;内置千言数据集,提供丰富的面向自然语言理解与生成场景的中文数据集,为NLP研究人员提供一站式的科研体验。 frompaddlenlp.datasetsimportload_dataset train_ds, dev_ds, test_ds = load_dataset("chnsenticorp", splits=["train","dev...
self.tokenizer = SpacyWordSplitter(language="en_core_web_sm") dataset_reader_params = archive.config["dataset_reader"] self.dataset_reader = DatasetReader.from_params(dataset_reader_params) self.model = archive.model self.model.eval()
from datasets import load_dataset imdb = load_dataset('imdb') print('starting collecting sentences with tokens >= 512') sentences = [sentence for sentence in imdb['train']['text'] if tokenizer(sentence, truncation=True, return_tensors='...