austin_sailor_dataset Mirror of https://huggingface.co/datasets/lerobot/austin_sailor_dataset 1 0 0 xarm_push_medium Mirror of https://huggingface.co/datasets/lerobot/xarm_push_medium 1 0 0 cmu_franka_exploration_dataset Mirror of https://huggingface.co/datasets/lerobot/cmu_franka_exploration_...
austin_sailor_dataset Mirror of https://huggingface.co/datasets/lerobot/austin_sailor_dataset 1 0 0 xarm_push_medium Mirror of https://huggingface.co/datasets/lerobot/xarm_push_medium 1 0 0 cmu_franka_exploration_dataset Mirror of https://huggingface.co/datasets/lerobot/cmu_franka_exploration_...
[i1]=j1 all2.append(tmp) print(111) from datasets import Dataset ds = Dataset.from_list(all2) #===tag进行编码 #'O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC' ds[0] ds2=ds.train_test_split(test_size=0.3)#下面我们使用ds2即可....
ds = load_dataset("openai/gsm8k", "main") for split, split_dataset in ds.items(): split_dataset.to_json(f"gsm8k-{split}.jsonl") 1. 2. 3. 4. 你会发现数据集的下载速度变快了: AI检测代码解析 Downloading readme: 7.94kB [00:00, 7.75MB/s] ...
With a simple command like squad_dataset = load_dataset("squad"), get any of these datasets ready to use in a dataloader for training/evaluating a ML model (Numpy/Pandas/PyTorch/TensorFlow/JAX), efficient data pre-processing: simple, fast and reproducible data pre-processing for the public ...
使用Dataset 和DataCollator 处理数据。 动态填充(padding=True)与截断(truncation=True)。 3. 模型微调(Fine-tuning) 训练循环 使用Trainer 类简化训练(封装训练、评估、保存)。 from transformers import Trainer, TrainingArguments training_args = TrainingArguments(output_dir="my_model", per_device_train_batch...
ValueError: Config name is missing. Please pick one among the available configs: ['3.0.0','1.0.0','2.0.0'] Example of usage:`load_dataset('cnn_dailymail','3.0.0')` 大概意思是它有三个配置(版本),需要指定版本号。 我们补齐版本号再试一次 ...
return torch.tensor(self.examples[i]) # Create the train and evaluation dataset train_dataset = CustomDataset(train_df['description'], tokenizer) eval_dataset = CustomDataset(test_df['description'], tokenizer) 一旦我们有了数据集,数据整理器(Data Collator)将帮助我们屏蔽训练文本。这只是一个小助手...
# Create the train and evaluation dataset train_dataset = CustomDataset(train_df['description'], tokenizer) eval_dataset = CustomDataset(test_df['description'], tokenizer) 一旦我们有了数据集,数据整理器(Data Collator)将帮助我们屏蔽训练文本。这只是一个小助手,它将帮助我们将数据集的不同样本批量处理...
austin_sailor_dataset Mirror of https://huggingface.co/datasets/lerobot/austin_sailor_dataset 1 0 0 xarm_push_medium Mirror of https://huggingface.co/datasets/lerobot/xarm_push_medium 1 0 0 cmu_franka_exploration_dataset Mirror of https://huggingface.co/datasets/lerobot/cmu_franka_exploration_...