# Description CodeT5-small model, fine-tuned on the code summarization subtask of CodeXGLUE (Ruby programming language). This model can generate a docstring of a given function written in Ruby. # Usage Here's how to use this model: ```python from transformers import RobertaTokenizer, T5ForConditionalGeneration model_name = "nielsr/codet5-small-code-summarization-ruby" tokenizer = RobertaTokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) code = """ def update_with_file_contents(digest, filename) File.open(filename) do |io| while (chunk = io.read(1024 * 8)) digest.update(chunk) end end end """ input_ids = tokenizer(code, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # Update the digest with the contents of the given file ```