利用预训练模型skep进行情感分析的方法是_模型训练平台

!pip install --upgrade paddlenlp -i https://pypi.org/simple 

from paddlenlp.datasets import load_dataset

train_ds, dev_ds, test_ds = load_dataset("chnsenticorp", splits=["train", "dev", "test"])

print(train_ds[0])
print(train_ds[1])
print(train_ds[2])

from paddlenlp.transformers import SkepForSequenceClassification, SkepTokenizer

# 指定模型名称，一键加载模型
model = SkepForSequenceClassification.from_pretrained(pretrained_model_name_or_path="skep_ernie_1.0_large_ch", num_classes=len(train_ds.label_list))
# 同样地，通过指定模型名称一键加载对应的Tokenizer，用于处理文本数据，如切分token，转token_id等。
tokenizer = SkepTokenizer.from_pretrained(pretrained_model_name_or_path="skep_ernie_1.0_large_ch")

import os
from functools import partial

import numpy as np
import paddle
import paddle.nn.functional as F
from paddlenlp.data import Stack, Tuple, Pad

from utils import create_dataloader

def convert_example(example,
                    tokenizer,
                    max_seq_length=512,
                    is_test=False):
    """
    Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
    by concatenating and adding special tokens. And creates a mask from the two sequences passed 
    to be used in a sequence-pair classification task.
        
    A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence has the following format:
    ::
        - single sequence: ``[CLS] X [SEP]``
        - pair of sequences: ``[CLS] A [SEP] B [SEP]``

    A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence pair mask has the following format:
    ::

        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |

    If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).


    Args:
        example(obj:`list[str]`): List of input data, containing text and label if it have label.
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` 
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        max_seq_len(obj:`int`): The maximum total input sequence length after tokenization. 
            Sequences longer than this will be truncated, sequences shorter will be padded.
        is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.

    Returns:
        input_ids(obj:`list[int]`): The list of token ids.
        token_type_ids(obj: `list[int]`): List of sequence pair mask.
        label(obj:`int`, optional): The input label if not is_test.
    """
    # 将原数据处理成model可读入的格式，enocded_inputs是一个dict，包含input_ids、token_type_ids等字段
    encoded_inputs = tokenizer(
        text=example["text"], max_seq_len=max_seq_length)

    # input_ids：对文本切分token后，在词汇表中对应的token id
    input_ids = encoded_inputs["input_ids"]
    # token_type_ids：当前token属于句子1还是句子2，即上述图中表达的segment ids
    token_type_ids = encoded_inputs["token_type_ids"]

    if not is_test:
        # label：情感极性类别
        label = np.array([example["label"]], dtype="int64")
        return input_ids, token_type_ids, label
    else:
        # qid：每条数据的编号
        qid = np.array([example["qid"]], dtype="int64")
        return input_ids, token_type_ids, qid

# 批量数据大小
batch_size = 32
# 文本序列最大长度
max_seq_length = 128

# 将数据处理成模型可读入的数据格式
trans_func = partial(
    convert_example,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length)

# 将数据组成批量式数据，如
# 将不同长度的文本序列padding到批量式数据中最大长度
# 将每条数据label堆叠在一起
batchify_fn = lambda samples, fn=Tuple(
    Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
    Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # token_type_ids
    Stack()  # labels
): [data for data in fn(samples)]
train_data_loader = create_dataloader(
    train_ds,
    mode='train',
    batch_size=batch_size,
    batchify_fn=batchify_fn,
    trans_fn=trans_func)
dev_data_loader = create_dataloader(
    dev_ds,
    mode='dev',
    batch_size=batch_size,
    batchify_fn=batchify_fn,
    trans_fn=trans_func)

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle


def create_dataloader(dataset,
                      trans_fn=None,
                      mode='train',
                      batch_size=1,
                      batchify_fn=None):
    """
    Creats dataloader.

    Args:
        dataset(obj:`paddle.io.Dataset`): Dataset instance.
        trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
        mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
        batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
        batchify_fn(obj:`callable`, optional, defaults to `None`): function to generate mini-batch data by merging
            the sample list, None for only stack each fields of sample in axis
            0(same as :attr::`np.stack(..., axis=0)`).

    Returns:
        dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
    """
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == 'train' else False
    if mode == "train":
        sampler = paddle.io.DistributedBatchSampler(
            dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    else:
        sampler = paddle.io.BatchSampler(
            dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    dataloader = paddle.io.DataLoader(
        dataset, batch_sampler=sampler, collate_fn=batchify_fn)
    return dataloader


def convert_example(example, tokenizer, is_test=False):
    """
    Builds model inputs from a sequence for sequence classification tasks. 
    It use `jieba.cut` to tokenize text.

    Args:
        example(obj:`list[str]`): List of input data, containing text and label if it have label.
        tokenizer(obj: paddlenlp.data.JiebaTokenizer): It use jieba to cut the chinese string.
        is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.

    Returns:
        input_ids(obj:`list[int]`): The list of token ids.
        valid_length(obj:`int`): The input sequence valid length.
        label(obj:`numpy.array`, data type1 of int64, optional): The input label if not is_test.
    """

    input_ids = tokenizer.encode(example["text"])
    input_ids = np.array(input_ids, dtype='int64')

    if not is_test:
        label = np.array(example["label"], dtype="int64")
        return input_ids, label
    else:
        return input_ids


@paddle.no_grad()
def evaluate(model, criterion, metric, data_loader):
    """
    Given a dataset, it evals model and computes the metric.

    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        criterion(obj:`paddle.nn.Layer`): It can compute the loss.
        metric(obj:`paddle.metric.Metric`): The evaluation metric.
        data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
    """
    model.eval()
    metric.reset()
    losses = []
    for batch in data_loader:
        input_ids, token_type_ids, labels = batch
        logits = model(input_ids, token_type_ids)
        loss = criterion(logits, labels)
        losses.append(loss.numpy())
        correct = metric.compute(logits, labels)
        metric.update(correct)
        accu = metric.accumulate()
    print("eval loss: %.5f, accu: %.5f" % (np.mean(losses), accu))
    model.train()
    metric.reset()

import time

from utils import evaluate

# 训练轮次
epochs = 1
# 训练过程中保存模型参数的文件夹
ckpt_dir = "skep_ckpt"
# len(train_data_loader)一轮训练所需要的step数
num_training_steps = len(train_data_loader) * epochs

# Adam优化器
optimizer = paddle.optimizer.AdamW(
    learning_rate=2e-5,
    parameters=model.parameters())
# 交叉熵损失函数
criterion = paddle.nn.loss.CrossEntropyLoss()
# accuracy评价指标
metric = paddle.metric.Accuracy()

# 开启训练
global_step = 0
tic_train = time.time()
for epoch in range(1, epochs + 1):
    for step, batch in enumerate(train_data_loader, start=1):
        input_ids, token_type_ids, labels = batch
        # 喂数据给model
        logits = model(input_ids, token_type_ids)
        # 计算损失函数值
        loss = criterion(logits, labels)
        # 预测分类概率值
        probs = F.softmax(logits, axis=1)
        # 计算acc
        correct = metric.compute(probs, labels)
        metric.update(correct)
        acc = metric.accumulate()

        global_step += 1
        if global_step % 10 == 0:
            print(
                "global step %d, epoch: %d, batch: %d, loss: %.5f, accu: %.5f, speed: %.2f step/s"
                % (global_step, epoch, step, loss, acc,
                    10 / (time.time() - tic_train)))
            tic_train = time.time()
        
        # 反向梯度回传，更新参数
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()

        if global_step % 100 == 0:
            save_dir = os.path.join(ckpt_dir, "model_%d" % global_step)
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)
            # 评估当前训练的模型
            evaluate(model, criterion, metric, dev_data_loader)
            # 保存当前模型参数等
            model.save_pretrained(save_dir)
            # 保存tokenizer的词表等
            tokenizer.save_pretrained(save_dir)

import numpy as np
import paddle

# 处理测试集数据
trans_func = partial(
    convert_example,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    is_test=True)
batchify_fn = lambda samples, fn=Tuple(
    Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input
    Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # segment
    Stack() # qid
): [data for data in fn(samples)]
test_data_loader = create_dataloader(
    test_ds,
    mode='test',
    batch_size=batch_size,
    batchify_fn=batchify_fn,
    trans_fn=trans_func)

# 根据实际运行情况，更换加载的参数路径
params_path = 'skep_ckp/model_500/model_state.pdparams'
if params_path and os.path.isfile(params_path):
    # 加载模型参数
    state_dict = paddle.load(params_path)
    model.set_dict(state_dict)
    print("Loaded parameters from %s" % params_path)

label_map = {0: '0', 1: '1'}
results = []
# 切换model模型为评估模式，关闭dropout等随机因素
model.eval()
for batch in test_data_loader:
    input_ids, token_type_ids, qids = batch
    # 喂数据给模型
    logits = model(input_ids, token_type_ids)
    # 预测分类
    probs = F.softmax(logits, axis=-1)
    idx = paddle.argmax(probs, axis=1).numpy()
    idx = idx.tolist()
    labels = [label_map[i] for i in idx]
    qids = qids.numpy().tolist()
    results.extend(zip(qids, labels))

res_dir = "./results"
if not os.path.exists(res_dir):
    os.makedirs(res_dir)
# 写入预测结果
with open(os.path.join(res_dir, "ChnSentiCorp.tsv"), 'w', encoding="utf8") as f:
    f.write("index\tprediction\n")
    for qid, label in results:
        f.write(str(qid[0])+"\t"+label+"\n")

train_ds, test_ds = load_dataset("seabsa16", "phns", splits=["train", "test"])

print(train_ds[0])
print(train_ds[1])
print(train_ds[2])

# 指定模型名称一键加载模型
model = SkepForSequenceClassification.from_pretrained(
    'skep_ernie_1.0_large_ch', num_classes=len(train_ds.label_list))
# 指定模型名称一键加载tokenizer
tokenizer = SkepTokenizer.from_pretrained('skep_ernie_1.0_large_ch')

from functools import partial
import os
import time

import numpy as np
import paddle
import paddle.nn.functional as F
from paddlenlp.data import Stack, Tuple, Pad


def convert_example(example,
                    tokenizer,
                    max_seq_length=512,
                    is_test=False,
                    dataset_name="chnsenticorp"):
    """
    Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
    by concatenating and adding special tokens. And creates a mask from the two sequences passed 
    to be used in a sequence-pair classification task.
        
    A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence has the following format:
    ::
        - single sequence: ``[CLS] X [SEP]``
        - pair of sequences: ``[CLS] A [SEP] B [SEP]``

    A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence pair mask has the following format:
    ::

        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |

    If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
    
    note: There is no need token type ids for skep_roberta_large_ch model.


    Args:
        example(obj:`list[str]`): List of input data, containing text and label if it have label.
        tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` 
            which contains most of the methods. Users should refer to the superclass for more information regarding methods.
        max_seq_len(obj:`int`): The maximum total input sequence length after tokenization. 
            Sequences longer than this will be truncated, sequences shorter will be padded.
        is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.
        dataset_name((obj:`str`, defaults to "chnsenticorp"): The dataset name, "chnsenticorp" or "sst-2".

    Returns:
        input_ids(obj:`list[int]`): The list of token ids.
        token_type_ids(obj: `list[int]`): List of sequence pair mask.
        label(obj:`numpy.array`, data type of int64, optional): The input label if not is_test.
    """
    encoded_inputs = tokenizer(
        text=example["text"],
        text_pair=example["text_pair"],
        max_seq_len=max_seq_length)

    input_ids = encoded_inputs["input_ids"]
    token_type_ids = encoded_inputs["token_type_ids"]

    if not is_test:
        label = np.array([example["label"]], dtype="int64")
        return input_ids, token_type_ids, label
    else:
        return input_ids, token_type_ids

# 处理的最大文本序列长度
max_seq_length=256
# 批量数据大小
batch_size=16

# 将数据处理成model可读入的数据格式
trans_func = partial(
    convert_example,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length)
# 将数据组成批量式数据，如
# 将不同长度的文本序列padding到批量式数据中最大长度
# 将每条数据label堆叠在一起
batchify_fn = lambda samples, fn=Tuple(
    Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
    Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # token_type_ids
    Stack(dtype="int64")  # labels
): [data for data in fn(samples)]
train_data_loader = create_dataloader(
    train_ds,
    mode='train',
    batch_size=batch_size,
    batchify_fn=batchify_fn,
    trans_fn=trans_func)

# 训练轮次
epochs = 3
# 总共需要训练的step数
num_training_steps = len(train_data_loader) * epochs
# 优化器
optimizer = paddle.optimizer.AdamW(
    learning_rate=5e-5,
    parameters=model.parameters())
# 交叉熵损失
criterion = paddle.nn.loss.CrossEntropyLoss()
# Accuracy评价指标
metric = paddle.metric.Accuracy()

# 开启训练
ckpt_dir = "skep_aspect"
global_step = 0
tic_train = time.time()
for epoch in range(1, epochs + 1):
    for step, batch in enumerate(train_data_loader, start=1):
        input_ids, token_type_ids, labels = batch
        # 喂数据给model
        logits = model(input_ids, token_type_ids)
        # 计算损失函数值
        loss = criterion(logits, labels)
        # 预测分类概率
        probs = F.softmax(logits, axis=1)
        # 计算acc
        correct = metric.compute(probs, labels)
        metric.update(correct)
        acc = metric.accumulate()

        global_step += 1
        if global_step % 10 == 0:
            print(
                "global step %d, epoch: %d, batch: %d, loss: %.5f, acc: %.5f, speed: %.2f step/s"
                % (global_step, epoch, step, loss, acc,
                    10 / (time.time() - tic_train)))
            tic_train = time.time()
        
        # 反向梯度回传，更新参数
        loss.backward()
        optimizer.step()
        optimizer.clear_grad()

        if global_step % 100 == 0:
            
            save_dir = os.path.join(ckpt_dir, "model_%d" % global_step)
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)
            # 保存模型参数
            model.save_pretrained(save_dir)
            # 保存tokenizer的词表等
            tokenizer.save_pretrained(save_dir)

@paddle.no_grad()
def predict(model, data_loader, label_map):
    """
    Given a prediction dataset, it gives the prediction results.

    Args:
        model(obj:`paddle.nn.Layer`): A model to classify texts.
        data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
        label_map(obj:`dict`): The label id (key) to label str (value) map.
    """
    model.eval()
    results = []
    for batch in data_loader:
        input_ids, token_type_ids = batch
        logits = model(input_ids, token_type_ids)
        probs = F.softmax(logits, axis=1)
        idx = paddle.argmax(probs, axis=1).numpy()
        idx = idx.tolist()
        labels = [label_map[i] for i in idx]
        results.extend(labels)
    return results

# 处理测试集数据
label_map = {0: '0', 1: '1'}
trans_func = partial(
    convert_example,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    is_test=True)
batchify_fn = lambda samples, fn=Tuple(
    Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
    Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # token_type_ids
): [data for data in fn(samples)]
test_data_loader = create_dataloader(
    test_ds,
    mode='test',
    batch_size=batch_size,
    batchify_fn=batchify_fn,
    trans_fn=trans_func)

# 根据实际运行情况，更换加载的参数路径
params_path = 'skep_ckpt/model_900/model_state.pdparams'
if params_path and os.path.isfile(params_path):
    # 加载模型参数
    state_dict = paddle.load(params_path)
    model.set_dict(state_dict)
    print("Loaded parameters from %s" % params_path)

results = predict(model, test_data_loader, label_map)

# 写入预测结果
with open(os.path.join("results", "SE-ABSA16_PHNS.tsv"), 'w', encoding="utf8") as f:
    f.write("index\tprediction\n")
    for idx, label in enumerate(results):
        f.write(str(idx)+"\t"+label+"\n")

#将预测文件结果压缩至zip文件，提交
!zip -r results.zip results