昇思25天学习打卡营第二十四天｜基于MindSpore通过GPT实现情感分类

时间：2024-07-18 07:00:55

基于MindSpore通过GPT实现情感分类

导入数据集

import os

import mindspore
from mindnlp._legacy.engine import Evaluator, Trainer
from mindnlp._legacy.engine.callbacks import BestModelCallback, CheckpointCallback
from mindnlp._legacy.metrics import Accuracy
from mindnlp.dataset import load_dataset
from mindspore import nn
from mindspore.dataset import GeneratorDataset, text, transforms


imdb_ds = load_dataset("imdb", split=["train", "test"])
imdb_train = imdb_ds["train"]
imdb_test = imdb_ds["test"]

对数据集进行预处理

import numpy as np


def process_dataset(dataset, tokenizer, max_seq_len=512, batch_size=4, shuffle=False):
    is_ascend = mindspore.get_context("device_target") == "Ascend"

    def tokenize(text):
        if is_ascend:
            tokenized = tokenizer(
                text, padding="max_length", truncation=True, max_length=max_seq_len
            )
        else:
            tokenized = tokenizer(text, truncation=True, max_length=max_seq_len)
        return tokenized["input_ids"], tokenized["attention_mask"]

    if shuffle:
        dataset = dataset.shuffle(batch_size)

    # map dataset
    dataset = dataset.map(
        operations=[tokenize],
        input_columns="text",
        output_columns=["input_ids", "attention_mask"],
    )
    dataset = dataset.map(
        operations=transforms.TypeCast(mindspore.int32),
        input_columns="label",
        output_columns="labels",
    )
    # batch dataset
    if is_ascend:
        dataset = dataset.batch(batch_size)
    else:
        dataset = dataset.padded_batch(
            batch_size,
            pad_info={
                "input_ids": (None, tokenizer.pad_token_id),
                "attention_mask": (None, 0),
            },
        )

    return dataset

导入 tokenizer

from mindnlp.transformers import GPTTokenizer

# tokenizer
gpt_tokenizer = GPTTokenizer.from_pretrained("openai-gpt")

# add sepcial token: <PAD>
special_tokens_dict = {
    "bos_token": "<bos>",
    "eos_token": "<eos>",
    "pad_token": "<pad>",
}
num_added_toks = gpt_tokenizer.add_special_tokens(special_tokens_dict)

分割训练数据集

# split train dataset into train and valid datasets
imdb_train, imdb_val = imdb_train.split([0.7, 0.3])

导入训练模型

from mindnlp.transformers import GPTForSequenceClassification
from mindspore.experimental.optim import Adam

# set bert config and define parameters for training
model = GPTForSequenceClassification.from_pretrained('openai-gpt', num_labels=2)
model.config.pad_token_id = gpt_tokenizer.pad_token_id
model.resize_token_embeddings(model.config.vocab_size + 3)

optimizer = nn.Adam(model.trainable_params(), learning_rate=2e-5)

metric = Accuracy()

# define callbacks to save checkpoints
ckpoint_cb = CheckpointCallback(save_path='checkpoint', ckpt_name='gpt_imdb_finetune', epochs=1, keep_checkpoint_max=2)
best_model_cb = BestModelCallback(save_path='checkpoint', ckpt_name='gpt_imdb_finetune_best', auto_load=True)

trainer = Trainer(network=model, train_dataset=dataset_train,
                  eval_dataset=dataset_train, metrics=metric,
                  epochs=1, optimizer=optimizer, callbacks=[ckpoint_cb, best_model_cb],
                  jit=False)

开始训练

trainer.run(tgt_columns="labels")

验证

evaluator = Evaluator(network=model, eval_dataset=dataset_test, metrics=metric)
evaluator.run(tgt_columns="labels")

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