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