Instructions to use cjwilliams/codet5-base-python-sum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cjwilliams/codet5-base-python-sum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cjwilliams/codet5-base-python-sum") model = AutoModelForSeq2SeqLM.from_pretrained("cjwilliams/codet5-base-python-sum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
CodeT5 Base Python Summarization
Fine-tuned from codet5-base-multi-sum using the Python split of CodeXGlue code-to-text dataset.
How to use
(Modified from example here)
from transformers import RobertaTokenizer, T5ForConditionalGeneration
if __name__ == '__main__':
tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base')
model = T5ForConditionalGeneration.from_pretrained('cjwilliams/codet5-base-python-sum')
text = """def svg_to_image(string, size=None):
if isinstance(string, unicode):
string = string.encode('utf-8')
renderer = QtSvg.QSvgRenderer(QtCore.QByteArray(string))
if not renderer.isValid():
raise ValueError('Invalid SVG data.')
if size is None:
size = renderer.defaultSize()
image = QtGui.QImage(size, QtGui.QImage.Format_ARGB32)
painter = QtGui.QPainter(image)
renderer.render(painter)
return image"""
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=20)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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