基于MindSpore通过GPT实现情感分类

导入数据集

import osimport 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, transformsimdb_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 npdef 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 datasetdataset = 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 datasetif 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")