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--- |
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tags: |
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- autotrain |
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- summarization |
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language: |
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- en |
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widget: |
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- text: > |
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class Solution(object): |
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def isValid(self, s): |
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stack = [] |
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mapping = {")": "(", "}": "{", "]": "["} |
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for char in s: |
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if char in mapping: |
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top_element = stack.pop() if stack else '#' |
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if mapping[char] != top_element: |
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return False |
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else: |
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stack.append(char) |
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return not stack |
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datasets: |
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- sagard21/autotrain-data-code-explainer |
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co2_eq_emissions: |
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emissions: 5.393079045128973 |
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license: mit |
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pipeline_tag: summarization |
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--- |
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|
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# Model Trained Using AutoTrain |
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|
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- Problem type: Summarization |
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- Model ID: 2745581349 |
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- CO2 Emissions (in grams): 5.3931 |
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|
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# Model Description |
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|
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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. |
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# Model Usage |
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|
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```py |
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from transformers import ( |
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AutoModelForSeq2SeqLM, |
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AutoTokenizer, |
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AutoConfig, |
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pipeline, |
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) |
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|
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model_name = "ashwinR/CodeExplainer" |
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|
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tokenizer = AutoTokenizer.from_pretrained(model_name, padding=True) |
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|
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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config = AutoConfig.from_pretrained(model_name) |
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model.eval() |
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pipe = pipeline("summarization", model=model_name, config=config, tokenizer=tokenizer) |
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|
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raw_code = """ |
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def preprocess(text: str) -> str: |
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text = str(text) |
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text = text.replace("\n", " ") |
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tokenized_text = text.split(" ") |
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preprocessed_text = " ".join([token for token in tokenized_text if token]) |
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return preprocessed_text |
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""" |
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|
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print(pipe(raw_code)[0]["summary_text"]) |
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``` |
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|
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## Validation Metrics |
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|
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- Loss: 2.156 |
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- Rouge1: 29.375 |
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- Rouge2: 18.128 |
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- RougeL: 25.445 |
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- RougeLsum: 28.084 |
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- Gen Len: 19.000 |
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