map(slow_tokenize_function, batched=True, num_proc=8) 2.3.3 应用2: tokenize 时进行截断 划重点: tokenizer 中的 return_overflowing_tokens 参数 dataset map 中的 remove_columns 参数 2.3.3.1 直接用会报错 def tokenize_and_split(examples): return tokenizer( examples["review"], truncation=True, ...
map(tokenize_function, batched=True) small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) full_train_dataset = tokenized_datasets["train"] full_eval_dataset = tokenized_...
texts = [tokenizer.apply_chat_template(convo, tokenize=False, add_generation_prompt=False) for convo in convos] return {"text": texts} dataset = dataset.map(formatting_prompts_func, batched=True) To understand how conversations are rendered in Llama-3.1 format, you can print out an item in...
map(tokenize_text, batched=True, batch_size=None) imdb_tokenized.set_format("torch", columns=["input_ids", "attention_mask", "label"]) def custom_iterator(): counter = 0 for item in imdb_tokenized['train']: inputs = {'input_ids': item['input_ids'], 'attention_mask': item['...
= -1 else -1) for l in examples["label"]] return result with training_args.main_process_first(desc="dataset map pre-processing"): raw_datasets = raw_datasets.map( preprocess_function, batched=True, # load_from_cache_file=not data_args.overwrite_cache, # desc="Running tokenizer on ...
test_tokenized = test.map(tokenize_dataset, batched=True) validation_tokenized = validation.map(tokenize_dataset, batched=True) 上面代码的第5行,为罗马尼亚语的标记器设置填充标记是非常必要的。因为它将在第9行使用,标记器使用填充可以使所有输入都具有相同的大小。
dataset.map(_process_data_to_model_inputs, batched=True, batch_size=100, num_proc=32# num of parallel threads.) And when I tried to process with num_proc=32 batch_size=100 The.map()function finishes the processing of 500 million lines in 18 hours of compute time o...
#Tokenize test set dataset_test_encoded = dataset["test"].map(preprocess_function_batch, batched=True) # Use the model to get predictions test_predictions = trainer.predict(dataset_test_encoded) # For each prediction, create the label with argmax ...
eos_token # Preprocess the dataset def preprocess_function(examples): # Tokenize the text and truncate to max length return tokenizer(examples['content'], truncation=True, padding='max_length', max_length=128) train_dataset = train_dataset.map(preprocess_function, batched=True) # Define ...
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