当训练数据规模在0-23w以内,load_dataset加载本地jsonl文件不会出现问题,速度还能接受。但如果数据规模超过百万,会出现以下问题: Generating train split: 234665 examples [00:01, 172075.77 examples/s] datasets.exceptions.DatasetGenerationError: An erro
data_files=["s3://<bucket name>/<data folder>/data-parquet"],storage_options=fs.storage_options,streaming=True)File~/.../datasets/src/datasets/load.py:1790,inload_dataset(path,name,data_dir,data_files,split,cache_dir,features,download_config,download_mode,verification_mode,ignore_verification...
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split ...
For this dataset, the data is already split into train and test. We just load them separately. print(data_dir)train_data=ak.text_dataset_from_directory(os.path.join(data_dir,"train"),batch_size=batch_size)test_data=ak.text_dataset_from_directory(os.path.join(data_dir,"test"),shuffle=...
>>> dataset.train_test_split(test_size=0.1) {'train': Dataset(schema: {'sentence1': 'string', 'sentence2': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 3301), 'test': Dataset(schema: {'sentence1': 'string', 'sentence2': 'string', 'label': 'int64', 'idx':...
load text split text Create embedding using OpenAI Embedding API Load the embedding into Chroma vector DB Save Chroma DB to disk I am able to follow the above sequence. Now I want to start fromretrieving the saved embeddings from diskand then start with the question stuff, rather than process...
split the README into the wiki The backdoor factory ? Impacket ? support for https proxy HTTP transport UDP transport DNS transport ICMP transport bypass UAC module privilege elevation module ... any cool idea ? FAQ Does the server works on windows ?