Quickstart

Installation

sgnlp is tested on Python 3.8+ and Pytorch 1.8+.

Install sgnlp with Python’s pip package manager.

pip install sgnlp

Basics

The package is built around the following classes for each model, similar to how the transformers package is built.

1. Preprocessor: Preprocesses input data into a format that can be fed into the model

2. Config: Stores the configuration of the model.

3. Model: Pytorch models which can work with pretrained weights.

4. Tokenizer: Stores the vocabulary for each model and encodes text into a format that can be fed into Model.

5. Postprocessor (available for some models): Postprocesses the raw output of the model into a format that can be easily interpreted.

Example

from sgnlp.models.emotion_entailment import (
    RecconEmotionEntailmentConfig,
    RecconEmotionEntailmentModel,
    RecconEmotionEntailmentTokenizer,
    RecconEmotionEntailmentPreprocessor,
    RecconEmotionEntailmentPostprocessor,
)

config = RecconEmotionEntailmentConfig.from_pretrained(
    "https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/config.json"
)
model = RecconEmotionEntailmentModel.from_pretrained(
    "https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/pytorch_model.bin",
    config=config,
)
tokenizer = RecconEmotionEntailmentTokenizer.from_pretrained("roberta-base")
preprocessor = RecconEmotionEntailmentPreprocessor(tokenizer)
postprocess = RecconEmotionEntailmentPostprocessor()

input_batch = {
    "emotion": ["happiness", "happiness"],
    "target_utterance": ["Thank you very much .", "Thank you very much ."],
    "evidence_utterance": [
        "How can I forget my old friend ?",
        "My best wishes to you and the bride !",
    ],
    "conversation_history": [
        "It's very thoughtful of you to invite me to your wedding . How can I forget my old friend ? My best wishes to you and the bride ! Thank you very much .",
        "It's very thoughtful of you to invite me to your wedding . How can I forget my old friend ? My best wishes to you and the bride ! Thank you very much .",
    ],
}
input_dict = preprocessor(input_batch)
raw_output = model(**input_dict)
output = postprocess(raw_output)
print(output)
# [0, 1]