metadata

tags:
  - autotrain
  - summarization
language:
  - en
widget:
  - text: |
      class Solution(object):
        def isValid(self, s):
          stack = []
          mapping = {")": "(", "}": "{", "]": "["}
          for char in s:
              if char in mapping:
                  top_element = stack.pop() if stack else '#'
                  if mapping[char] != top_element:
                      return False
              else:
                  stack.append(char)
          return not stack
datasets:
  - sagard21/autotrain-data-code-explainer
co2_eq_emissions:
  emissions: 5.393079045128973
license: mit
pipeline_tag: summarization

Model Trained Using AutoTrain

Problem type: Summarization
Model ID: 2745581349
CO2 Emissions (in grams): 5.3931

Model Description

This model is an attempt to simplify code understanding by generating line by line explanation of a source code. This model was fine-tuned using the Salesforce/codet5-large model. Currently it is trained on a small subset of Python snippets.

Model Usage

from transformers import (
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    AutoConfig,
    pipeline,
)

model_name = "ashwinR/CodeExplainer"

tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True)

model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

config = AutoConfig.from_pretrained(model_name)

model.eval()

pipe = pipeline("summarization", model=model_name, config=config, tokenizer=tokenizer)

raw_code = """
def preprocess(text: str) -> str:
    text = str(text)
    text = text.replace("\n", " ")
    tokenized_text = text.split(" ")
    preprocessed_text = " ".join([token for token in tokenized_text if token])

    return preprocessed_text
"""

print(pipe(raw_code)[0]["summary_text"])

Validation Metrics

Loss: 2.156
Rouge1: 29.375
Rouge2: 18.128
RougeL: 25.445
RougeLsum: 28.084
Gen Len: 19.000