spacy-wrap 0.0.2

Wrappers for including pre-trained transformers in spaCy pipelines

spaCy-wrap: For Wrapping fine-tuned transformers in spaCy pipelines

Installation

Installing spacy-wrap is simple using pip:

pip install spacy_wrap

There is no reason to update from GitHub as the version on PyPI should always be the same as on GitHub.

Simple Example

The following shows a simple example of how you can quickly add a finetuned transformer model from the Huggingface model hub. In this case we will add the Hate speech model for Danish to a blank Danish pipeline.

import spacy
import spacy_wrap

nlp = spacy.blank("da")

config = {
    "doc_extension_trf_data": "clf_trf_data",  # document extention for the forward pass
    "doc_extension_prediction": "hate_speech",  # document extention for the prediction
    "labels": ["Not hate Speech", "Hate speech"],
    "model": {
        "name": "DaNLP/da-bert-hatespeech-detection",  # the model name or path of huggingface model
    },
}

transformer = nlp.add_pipe("classification_transformer", config=config)
transformer.model.initialize()

doc = nlp("Senile gamle idiot") # old senile idiot

doc._.clf_trf_data
# TransformerData(wordpieces=...
doc._.hate_speech
# "Hate speech"
doc._.hate_speech_prob
# {'prob': array([0.013, 0.987], dtype=float32), 'labels': ['Not hate Speech', 'Hate speech']}

📖 Documentation

Documentation
🔧 Installation	Installation instructions for spacy-wrap
📚 Usage Guides	Guides and instructions on how to use spacy-wrap and its features.
📰 News and changelog	New additions, changes and version history.
🎛 Documentation	The detailed reference for spacy-wrap's API. Including function documentation

💬 Where to ask questions

Type
🚨 FAQ	FAQ
🚨 Bug Reports	GitHub Issue Tracker
🎁 Feature Requests & Ideas	GitHub Issue Tracker
👩‍💻 Usage Questions	GitHub Discussions
🗯 General Discussion	GitHub Discussions