```python
from senta import Senta
my_senta = Senta()

# get pre-trained model, we provide three pre-trained models, all of which are based on the SKEP
print(my_senta.get_support_model()) # ["ernie_1.0_skep_large_ch", "ernie_2.0_skep_large_en", "roberta_skep_large_en"]
                                    # ernie_1.0_skep_large_ch, skep Chinese pre-trained model based on ERNIE 1.0 large.
                                    # ernie_2.0_skep_large_en, skep English pre-trained model based on ERNIE 2.0 large.
                                    # roberta_skep_large_en, skep English pre-trained model based on RoBERTa large, which is used in our paper.

# get supported task
print(my_senta.get_support_task()) # ["sentiment_classify", "aspect_sentiment_classify", "extraction"]

use_cuda = True # set True or False

# predict different tasks
my_senta.init_model(model_class="roberta_skep_large_en", task="sentiment_classify", use_cuda=use_cuda)
texts = ["a sometimes tedious film ."]
result = my_senta.predict(texts)
print(result)

my_senta.init_model(model_class="roberta_skep_large_en", task="aspect_sentiment_classify", use_cuda=use_cuda)
texts = ["I love the operating system and the preloaded software."]
aspects = ["operating system"]
result = my_senta.predict(texts, aspects)
print(result)

my_senta.init_model(model_class="roberta_skep_large_en", task="extraction", use_cuda=use_cuda)
texts = ["The JCC would be very pleased to welcome your organization as a corporate sponsor ."]
result = my_senta.predict(texts)
print(result)
```

```shell
# download code
git clone https://github.com/baidu/Senta.git

# download a pre-trained skep model
cd ./Senta/model_files
sh download_roberta_skep_large_en.sh # download roberta_skep_large_en model. For other pre-trained skep models, you can find them in this dir.
cd -

# download task dataset
cd ./Senta/data/
sh download_en_data.sh # download English dataset used in our paper. For Chinese dataset, you can find its download script in this dir.
cd - 
```

@inproceedings{tian-etal-2020-skep,
    title = "{SKEP}: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis",
    author = "Tian, Hao  and
      Gao, Can  and
      Xiao, Xinyan  and
      Liu, Hao  and
      He, Bolei  and
      Wu, Hua  and
      Wang, Haifeng  and
      wu, feng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.374",
    pages = "4067--4076",
    abstract = "Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-training, despite the fact that they are widely used in traditional sentiment analysis approaches. In this paper, we introduce Sentiment Knowledge Enhanced Pre-training (SKEP) in order to learn a unified sentiment representation for multiple sentiment analysis tasks. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. Experiments on three kinds of sentiment tasks show that SKEP significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets. We release our code at https://github.com/baidu/Senta.",
}