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WalkthroughIntroduces an end-to-end NLP pipeline that loads a Yelp Polarity subset, translates English reviews to Korean with NLLB, classifies sentiment on the Korean text with a multilingual model, and stores results. Adds helper functions, applies map-based processing, previews via DataFrames, and includes a parallel script version. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor User
participant Notebook/Script
participant Datasets as HuggingFace Datasets
participant NLLB as Translation Model (NLLB 600M)
participant Sentiment as Multilingual Sentiment Model
participant Storage as Dataset w/ new fields
User->>Notebook/Script: Run pipeline
Notebook/Script->>Datasets: load_dataset("yelp_polarity", split="train")
Note right of Notebook/Script: Select first 20 samples, drop label
loop Map: translate_english_to_korean
Notebook/Script->>NLLB: translate(example["text"], en->ko)
NLLB-->>Notebook/Script: korean text
Notebook/Script->>Storage: add field "korean_translate"
end
loop Map: analyze_emotion
Notebook/Script->>Sentiment: classify("korean_translate")
Sentiment-->>Notebook/Script: top label
Notebook/Script->>Storage: add field "emotion"
end
Notebook/Script-->>User: Preview DataFrames and sample summaries
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Poem
Pre-merge checks and finishing touches❌ Failed checks (1 warning, 1 inconclusive)
✅ Passed checks (1 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
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Actionable comments posted: 1
🧹 Nitpick comments (1)
transformer.py (1)
12-112: Avoid executing the whole pipeline at import time.
Importing this module immediately downloads models, loads datasets, and runs translation/emotion inference. That makes the module unusable for reuse or testing and wastes resources whenever it’s imported. Wrap the workflow in amain()(or similar) guarded byif __name__ == "__main__":so callers can choose when to run it.Suggested structure:
-full_dataset = load_dataset("yelp_polarity", split="train") -# … -for i in range(min(5, len(final_dataset))): - ... +def main(): + full_dataset = load_dataset("yelp_polarity", split="train") + # … + for i in range(min(5, len(final_dataset))): + ... + + +if __name__ == "__main__": + main()
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transformer.ipynb(1 hunks)transformer.py(1 hunks)
🧰 Additional context used
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transformer.py
28-28: Undefined name display
(F821)
67-67: Undefined name display
(F821)
103-103: Undefined name display
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| # 데이터셋 확인을 위해 Pandas DataFrame으로 변환 (선택 사항) | ||
| df_check = pd.DataFrame(full_dataset_subset) | ||
| print("\n데이터셋 일부 미리보기 (DataFrame):") | ||
| display(df_check.head(3)) # Colab 환경에서는 display()가 표 형태로 보여줍니다 |
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Fix display NameError.
Running this module outside a notebook will crash because display is undefined. Import it (or replace with print) before use.
Apply this diff:
-from transformers import pipeline
-import torch
-import pandas as pd # 데이터 확인용
+from transformers import pipeline
+import torch
+import pandas as pd # 데이터 확인용
+from IPython.display import displayAlso applies to: 67-67, 103-103
🧰 Tools
🪛 Ruff (0.13.2)
28-28: Undefined name display
(F821)
🤖 Prompt for AI Agents
In transformer.py around lines 28, 67, and 103, calls to display(...) will raise
NameError when run outside a notebook; replace or import it so the module runs
in non-interactive environments. Either import display from IPython.display at
the top of the file (from IPython.display import display) or change those three
display(...) calls to print(...) (or use df.head(n).to_string() for nicer
formatting); update imports or calls consistently for all three locations.
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Actionable comments posted: 1
♻️ Duplicate comments (1)
dhleesep9/transformer.py (1)
1-5: Thedisplayissue has already been flagged.The missing
from IPython.display import displayimport has been identified in a previous review. Please address that comment to resolve theNameErrorat lines 28, 67, and 103.
🧹 Nitpick comments (2)
dhleesep9/transformer.py (2)
55-56: Consider adding error handling for robustness.The
.map()call assumes all translations will succeed. If the translation pipeline encounters an error (e.g., network issues, model failures, or problematic input), the entire process will crash without helpful diagnostics.Consider wrapping the translation logic with try-except:
def translate_english_to_korean(example): """데이터셋의 'text'를 받아 영어 번역 결과를 반환하는 함수""" - translation_result = translator( - example['text'], - tgt_lang="kor_Hang", - src_lang="eng_Latn", - max_length=400, - min_length=30, - do_sample=False # deterministic 출력을 원하면 False, 다양성을 원하면 True - ) - # 결과에서 번역 텍스트 추출 - example['korean_translate'] = translation_result[0]['translation_text'] + try: + translation_result = translator( + example['text'], + tgt_lang="kor_Hang", + src_lang="eng_Latn", + max_length=400, + min_length=30, + do_sample=False # deterministic 출력을 원하면 False, 다양성을 원하면 True + ) + # 결과에서 번역 텍스트 추출 + example['korean_translate'] = translation_result[0]['translation_text'] + except Exception as e: + print(f"Translation failed for text: {example['text'][:50]}... Error: {e}") + example['korean_translate'] = "" # Fallback to empty string return example
84-87: Consider adding error handling and clarifying nested access.Similar to the translation function, this lacks error handling. Additionally, the
[0][0]indexing pattern (due totop_k=1returning a nested structure) could be clearer.Apply this diff to improve robustness and readability:
def analyze_emotion(example): - emotion_result = emotion_classifier(example['korean_translate']) - example['emotion'] = emotion_result[0][0]['label'] + try: + emotion_result = emotion_classifier(example['korean_translate']) + # top_k=1 returns [[{label, score}]] + example['emotion'] = emotion_result[0][0]['label'] + except Exception as e: + print(f"Emotion analysis failed for text: {example['korean_translate'][:50]}... Error: {e}") + example['emotion'] = "unknown" # Fallback return example
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📒 Files selected for processing (1)
dhleesep9/transformer.py(1 hunks)
🧰 Additional context used
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dhleesep9/transformer.py
28-28: Undefined name display
(F821)
67-67: Undefined name display
(F821)
103-103: Undefined name display
(F821)
🔇 Additional comments (5)
dhleesep9/transformer.py (5)
12-23: LGTM!The dataset loading and sampling logic is clean and appropriate for an experimental pipeline. Removing the label column and selecting 20 samples makes sense for testing the translation and sentiment analysis workflow.
31-37: LGTM!The translation pipeline is correctly configured with the NLLB model and proper device handling for GPU acceleration.
70-80: LGTM!The emotion classification pipeline is properly configured with an appropriate multilingual model. The test with Korean text confirms the pipeline works as expected.
107-111: LGTM!The final results display is well-structured. Truncating the original text to 100 characters prevents excessive output while still providing context for each sample.
47-48: Verify Yelp review length distribution before setting min_lengthThe script failed due to the missing
datasetslibrary; manually verify the word-count distribution of your Yelp reviews (e.g., installdatasetsand rerun or sample your dataset) to determine how many fall below 30 words. Depending on the result, either removemin_lengthor lower it (e.g., to 5).
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| # 번역을 수행하는 함수 정의 | ||
| def translate_english_to_korean(example): | ||
| """데이터셋의 'summary'를 받아 영어 번역 결과를 반환하는 함수""" |
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Fix the misleading docstring.
The docstring mentions receiving 'summary', but the function actually operates on the 'text' field at line 44. This inconsistency could confuse future maintainers.
Apply this diff:
- """데이터셋의 'summary'를 받아 영어 번역 결과를 반환하는 함수"""
+ """데이터셋의 'text'를 받아 영어 번역 결과를 반환하는 함수"""📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| """데이터셋의 'summary'를 받아 영어 번역 결과를 반환하는 함수""" | |
| """데이터셋의 'text'를 받아 영어 번역 결과를 반환하는 함수""" |
🤖 Prompt for AI Agents
In dhleesep9/transformer.py around line 42, the docstring incorrectly states the
function receives the dataset's 'summary' field while the function actually
operates on the 'text' field at line 44; update the docstring to accurately
describe that the function accepts the dataset's 'text' field (or use a more
general phrase like "input text") and returns the English translation, keeping
the docstring language consistent and concise.
| display(df_check.head(3)) # Colab 환경에서는 display()가 표 형태로 보여줍니다 | ||
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| translator = pipeline( |
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긴 context가 model의 입력 한도를 넘을 수 있으니, 입력 토큰을 일정한 사이즈로 chunking -> 번역 -> 병합 하면 잘림도 방지하고 품질도 더 높아질 것 같습니다!
2 participants
영어 리뷰를 받고 -> 한국어로 번역한 후 -> 다국어 감정분석 기능을 지원하는 모델을 통해 평점을 추론하는 코드를 짰습니다.
Summary by CodeRabbit
New Features
Refactor
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