Roberta Large Ernie2 Skep En

🚀 SKEP-Roberta

SKEP-Roberta is a sentiment analysis model. It leverages sentiment knowledge enhanced pre - training to improve performance in sentiment analysis tasks.

🚀 Quick Start

To start using the SKEP-Roberta model, you can follow these steps. First, load the tokenizer and the model as shown in the basic usage example below.

✨ Features

• Sentiment Knowledge Enhanced: SKEP proposes Sentiment Knowledge Enhanced Pre - training for sentiment analysis. It designs sentiment masking and three sentiment pre - training objectives to incorporate various types of knowledge into the pre - training model.
• Model Conversion: The released PyTorch model is converted from the officially released PaddlePaddle SKEP model, and a series of experiments have been conducted to ensure the accuracy of the conversion.

📦 Installation

The installation process mainly involves loading the model and tokenizer from the pre - trained model repository. You can use the transformers library in Python to achieve this.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Yaxin/roberta-large-ernie2-skep-en")
model = AutoModel.from_pretrained("Yaxin/roberta-large-ernie2-skep-en")

Advanced Usage

#!/usr/bin/env python
#encoding: utf-8
import torch
from transformers import RobertaTokenizer, RobertaForMaskedLM

tokenizer = RobertaTokenizer.from_pretrained('Yaxin/roberta-large-ernie2-skep-en')

input_tx = "<s> He like play with student, so he became a <mask> after graduation </s>"
# input_tx = "<s> He is a <mask> and likes to get along with his students </s>"

tokenized_text = tokenizer.tokenize(input_tx)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)

tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([[0] * len(tokenized_text)])

model = RobertaForMaskedLM.from_pretrained('Yaxin/roberta-large-ernie2-skep-en')
model.eval()

with torch.no_grad():
    outputs = model(tokens_tensor, token_type_ids=segments_tensors)
    predictions = outputs[0]

predicted_index = [torch.argmax(predictions[0, i]).item() for i in range(0, (len(tokenized_text) - 1))]
predicted_token = [tokenizer.convert_ids_to_tokens([predicted_index[x]])[0] for x in
                   range(1, (len(tokenized_text) - 1))]

print('Predicted token is:', predicted_token)

📚 Documentation

Released Model Info

This released PyTorch model is converted from the officially released PaddlePaddle SKEP model, and a series of experiments have been conducted to check the accuracy of the conversion.

• Official PaddlePaddle SKEP repo:
  1. https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/skep
  2. https://github.com/baidu/Senta
• Pytorch Conversion repo: Not released yet

More Detail

For more detailed information about SKEP, please refer to: https://aclanthology.org/2020.acl-main.374.pdf

📄 License

No license information is provided in the original document.

📚 Citation

@article{tian2020skep,
  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},
  journal={arXiv preprint arXiv:2005.05635},
  year={2020}
}

Reference

https://github.com/nghuyong/ERNIE-Pytorch