Description:
rasapaddlenlp 0.3.1
Rasa NLU Components using PaddleNLP
Features
Tokenizer and Dense featurizer using pre-trained models supported by PaddleNLP.
Usage
pip install rasa-paddlenlp
In your config.yml, use the following configuration:
language: zh
pipeline:
- name: "rasa_paddlenlp.nlu.paddlenlp_tokenizer.PaddleNLPTokenizer"
model_name: bert
model_weights: bert-wwm-ext-chinese
# Flag to check whether to split intents
intent_tokenization_flag: false
# Symbol on which intent should be split
intent_split_symbol: "_"
- name: "rasa_paddlenlp.nlu.paddlenlp_featurizer.PaddleNLPFeaturizer"
model_name: bert
model_weights: bert-wwm-ext-chinese
# rest of your configurations
Currently there is code to support BERT pre-trained models, we just need to add the model definitions and default weights in order for other PaddleNLP-supported models.
Credits
This package took inspiration from the following projects:
Rasa
PaddleNLP
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
Cookiecutter
audreyr/cookiecutter-pypackage
License
MIT
History
0.3.1 (2022-02-10)
Made PaddleNLPTokenizer match V3 specification.
0.3.0 (2022-02-10)
From now on the library supports Rasa V3
Added XLNet and Roberta support (ERNIE will come soon)
0.2.0 (2022-01-13)
First release on PyPI.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
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