遮蔽语言模型¶

pip install transformers datasets evaluate

from huggingface_hub import notebook_login

notebook_login()

from datasets import load_dataset

eli5 = load_dataset("eli5_category", split="train[:5000]")

eli5 = eli5.train_test_split(test_size=0.2)

eli5["train"][0]

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base")

eli5 = eli5.flatten()
eli5["train"][0]

def preprocess_function(examples):
    return tokenizer([" ".join(x) for x in examples["answers.text"]])

tokenized_eli5 = eli5.map(
    preprocess_function,
    batched=True,
    num_proc=4,
    remove_columns=eli5["train"].column_names,
)

block_size = 128

def group_texts(examples):
    # 连接所有文本。
    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
    total_length = len(concatenated_examples[list(examples.keys())[0]])
    # 我们可以添加填充而不是这种丢弃，如果模型支持的话，你可以根据自己的需要定制这部分。
    if total_length >= block_size:
        total_length = (total_length // block_size) * block_size
    # 按 block_size 分割块。
    result = {
        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
        for k, t in concatenated_examples.items()
    }
    return result

lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)

from transformers import DataCollatorForLanguageModeling

tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)

from transformers import DataCollatorForLanguageModeling

data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15, return_tensors="tf")

from transformers import AutoModelForMaskedLM

model = AutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")

training_args = TrainingArguments(
    output_dir="my_awesome_eli5_mlm_model",
    eval_strategy="epoch",
    learning_rate=2e-5,
    num_train_epochs=3,
    weight_decay=0.01,
    push_to_hub=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=lm_dataset["train"],
    eval_dataset=lm_dataset["test"],
    data_collator=data_collator,
    tokenizer=tokenizer,
)

trainer.train()

import math

eval_results = trainer.evaluate()
print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")

trainer.push_to_hub()

from transformers import create_optimizer, AdamWeightDecay

optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

from transformers import TFAutoModelForMaskedLM

model = TFAutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")

tf_train_set = model.prepare_tf_dataset(
    lm_dataset["train"],
    shuffle=True,
    batch_size=16,
    collate_fn=data_collator,
)

tf_test_set = model.prepare_tf_dataset(
    lm_dataset["test"],
    shuffle=False,
    batch_size=16,
    collate_fn=data_collator,
)

import tensorflow as tf

model.compile(optimizer=optimizer)  # No loss argument!

from transformers.keras_callbacks import PushToHubCallback

callback = PushToHubCallback(
    output_dir="my_awesome_eli5_mlm_model",
    tokenizer=tokenizer,
)

model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])

text = "The Milky Way is a <mask> galaxy."

from transformers import pipeline

mask_filler = pipeline("fill-mask", "username/my_awesome_eli5_mlm_model")
mask_filler(text, top_k=3)

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_eli5_mlm_model")
inputs = tokenizer(text, return_tensors="pt")
mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]

from transformers import AutoModelForMaskedLM

model = AutoModelForMaskedLM.from_pretrained("username/my_awesome_eli5_mlm_model")
logits = model(**inputs).logits
mask_token_logits = logits[0, mask_token_index, :]

top_3_tokens = torch.topk(mask_token_logits, 3, dim=1).indices[0].tolist()

for token in top_3_tokens:
    print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_eli5_mlm_model")
inputs = tokenizer(text, return_tensors="tf")
mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]

from transformers import TFAutoModelForMaskedLM

model = TFAutoModelForMaskedLM.from_pretrained("username/my_awesome_eli5_mlm_model")
logits = model(**inputs).logits
mask_token_logits = logits[0, mask_token_index, :]

top_3_tokens = tf.math.top_k(mask_token_logits, 3).indices.numpy()

for token in top_3_tokens:
    print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))