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Pre-merge checks and finishing touches❌ Failed checks (1 inconclusive)
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Actionable comments posted: 2
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (3)
huggingface_basics.ipynb(32 hunks)jimin/news.py(1 hunks)transformer.ipynb(14 hunks)
🧰 Additional context used
🪛 Ruff (0.13.3)
jimin/news.py
116-116: Undefined name display
(F821)
transformer.ipynb
116-116: Undefined name display
(F821)
186-186: Undefined name display
(F821)
248-248: Undefined name display
(F821)
huggingface_basics.ipynb
30-30: Found useless expression. Either assign it to a variable or remove it.
(B018)
122-122: Found useless expression. Either assign it to a variable or remove it.
(B018)
125-125: Found useless expression. Either assign it to a variable or remove it.
(B018)
161-161: Redefinition of unused pd from line 136
Remove definition: pd
(F811)
| "generator = pipeline('text-generation', model='gpt2', device=0) # GPU 사용 시\n", | ||
| "#generator = pipeline('text-generation', model='gpt2') # CPU 사용 시 (기본값)\n", | ||
| "\n", | ||
| "# 텍스트 생성을 시작할 프롬프트(prompt) 또는 시작 문맥(context)\n", | ||
| "prompt = \"Once upon a time, in a land far, far away,\"\n", | ||
| "\n", | ||
| "# GPT-2는 기본 pad 토큰이 없으므로, eos 토큰을 pad 토큰으로 설정 (일반적인 관행)\n", | ||
| "if gpt_tokenizer.pad_token is None:\n", | ||
| " gpt_tokenizer.pad_token = gpt_tokenizer.eos_token\n", | ||
| "# 파이프라인을 사용하여 텍스트 생성 실행\n", | ||
| "# max_length: 생성될 텍스트의 최대 길이 (프롬프트 포함)\n", | ||
| "# num_return_sequences: 몇 개의 다른 생성 결과를 반환할지\n", | ||
| "# 결과는 리스트 형태로 반환되며, 각 요소는 딕셔너리 형태입니다.\n", | ||
| "results = generator(prompt, max_length=50, num_return_sequences=1)\n", | ||
| "\n", | ||
| "# 2. 입력 텍스트 준비\n", | ||
| "text = \"What is Huggingface Transformer?\"\n", | ||
| "encoded_input = gpt_tokenizer(text, return_tensors='pt')\n", | ||
| "input_ids = encoded_input['input_ids']\n", | ||
| "# 생성된 텍스트 출력\n", | ||
| "print(f\"\\n프롬프트: {prompt}\")\n", | ||
| "print(\"--- 생성된 텍스트 ---\")\n", | ||
| "for result in results:\n", | ||
| " print(result['generated_text'])\n", | ||
| "\n", |
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Don’t hard-code GPU usage for generator
Setting device=0 forces GPU even when Colab/locals lack one, leading to AssertionError: Torch not compiled with CUDA enabled. Reuse the earlier device guard (0 or -1) so the cell runs everywhere.
-generator = pipeline('text-generation', model='gpt2', device=0) # GPU 사용 시
-#generator = pipeline('text-generation', model='gpt2') # CPU 사용 시 (기본값)
+device = 0 if torch.cuda.is_available() else -1
+generator = pipeline('text-generation', model='gpt2', device=device)📝 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.
| "generator = pipeline('text-generation', model='gpt2', device=0) # GPU 사용 시\n", | |
| "#generator = pipeline('text-generation', model='gpt2') # CPU 사용 시 (기본값)\n", | |
| "\n", | |
| "# 텍스트 생성을 시작할 프롬프트(prompt) 또는 시작 문맥(context)\n", | |
| "prompt = \"Once upon a time, in a land far, far away,\"\n", | |
| "\n", | |
| "# GPT-2는 기본 pad 토큰이 없으므로, eos 토큰을 pad 토큰으로 설정 (일반적인 관행)\n", | |
| "if gpt_tokenizer.pad_token is None:\n", | |
| " gpt_tokenizer.pad_token = gpt_tokenizer.eos_token\n", | |
| "# 파이프라인을 사용하여 텍스트 생성 실행\n", | |
| "# max_length: 생성될 텍스트의 최대 길이 (프롬프트 포함)\n", | |
| "# num_return_sequences: 몇 개의 다른 생성 결과를 반환할지\n", | |
| "# 결과는 리스트 형태로 반환되며, 각 요소는 딕셔너리 형태입니다.\n", | |
| "results = generator(prompt, max_length=50, num_return_sequences=1)\n", | |
| "\n", | |
| "# 2. 입력 텍스트 준비\n", | |
| "text = \"What is Huggingface Transformer?\"\n", | |
| "encoded_input = gpt_tokenizer(text, return_tensors='pt')\n", | |
| "input_ids = encoded_input['input_ids']\n", | |
| "# 생성된 텍스트 출력\n", | |
| "print(f\"\\n프롬프트: {prompt}\")\n", | |
| "print(\"--- 생성된 텍스트 ---\")\n", | |
| "for result in results:\n", | |
| " print(result['generated_text'])\n", | |
| "\n", | |
| # Choose device at runtime: GPU if available else CPU | |
| device = 0 if torch.cuda.is_available() else -1 | |
| generator = pipeline('text-generation', model='gpt2', device=device) | |
| # 텍스트 생성을 시작할 프롬프트(prompt) 또는 시작 문맥(context) | |
| prompt = "Once upon a time, in a land far, far away," | |
| # 파이프라인을 사용하여 텍스트 생성 실행 | |
| # max_length: 생성될 텍스트의 최대 길이 (프롬프트 포함) | |
| # num_return_sequences: 몇 개의 다른 생성 결과를 반환할지 | |
| # 결과는 리스트 형태로 반환되며, 각 요소는 딕셔너리 형태입니다. | |
| results = generator(prompt, max_length=50, num_return_sequences=1) | |
| # 생성된 텍스트 출력 | |
| print(f"\n프롬프트: {prompt}") | |
| print("--- 생성된 텍스트 ---") | |
| for result in results: | |
| print(result['generated_text']) |
🤖 Prompt for AI Agents
In huggingface_basics.ipynb around lines 1689 to 1706, the generator pipeline is
hard-coded with device=0 which forces GPU and causes an assertion on machines
without CUDA; change the pipeline call to use the previously computed device
guard (e.g., device variable that is 0 when CUDA is available and -1 otherwise)
so the pipeline becomes device-aware and runs on CPU when no GPU is present;
update the commented GPU/CPU examples to reference this device variable instead
of hard-coding 0.
| import datasets | ||
| from datasets import load_dataset, DatasetDict | ||
| from transformers import pipeline | ||
| import torch | ||
| import pandas as pd | ||
| import matplotlib.pyplot as plt | ||
| import seaborn as sns |
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Fix the display NameError in script execution
Running this .py file outside a notebook will raise NameError: name 'display' is not defined, halting the pipeline before results print. Add a safe import (with fallback) near the top so the script works in both notebook and CLI runs.
+try:
+ from IPython.display import display
+except ImportError:
+ def display(value):
+ print(value)Also applies to: 109-117
🤖 Prompt for AI Agents
In jimin/news.py at lines 1-7 (and similarly around lines 109-117) the script
uses display which exists in notebooks but raises NameError when run as a CLI
script; add a safe import/fallback near the top: attempt to import display from
IPython.display inside a try/except and on ImportError define a no-op fallback
function (or a simple wrapper that prints the object) so calls to display work
both in notebooks and in non-interactive CLI runs; update any usage sites if
necessary to rely on this fallback.
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Actionable comments posted: 2
🧹 Nitpick comments (3)
jimin/news.py (3)
58-60: Refactor conditional statements for better readability.The if-elif-else chain uses multiple statements on one line, which reduces readability and violates PEP 8 style guidelines.
Apply this diff to fix the formatting:
- if label == 'LABEL_0': label_name = 'Negative' - elif label == 'LABEL_1': label_name = 'Neutral' - else: label_name = 'Positive' + if label == 'LABEL_0': + label_name = 'Negative' + elif label == 'LABEL_1': + label_name = 'Neutral' + else: + label_name = 'Positive'Alternatively, use a dictionary for cleaner mapping:
- if label == 'LABEL_0': label_name = 'Negative' - elif label == 'LABEL_1': label_name = 'Neutral' - else: label_name = 'Positive' + label_map = {'LABEL_0': 'Negative', 'LABEL_1': 'Neutral', 'LABEL_2': 'Positive'} + label_name = label_map.get(label, 'Positive')
90-91: Refactor conditional statements for better readability.Similar to the sentiment analysis function, these conditional statements should be split across multiple lines for better readability.
Apply this diff:
- if label == 'LABEL_1': label_name = 'Offensive' - else: label_name = 'Not Offensive' + if label == 'LABEL_1': + label_name = 'Offensive' + else: + label_name = 'Not Offensive'Or use a dictionary:
- if label == 'LABEL_1': label_name = 'Offensive' - else: label_name = 'Not Offensive' + label_map = {'LABEL_1': 'Offensive', 'LABEL_0': 'Not Offensive'} + label_name = label_map.get(label, 'Not Offensive')
127-128: Add emotion analysis columns to the display once the emotion analysis is fixed.After fixing the
analyze_emotionfunction, remember to include the emotion analysis results in the preview columns.Apply this diff:
# 최종 확인을 위해 필요한 컬럼만 선택 -columns_to_show = ['text', 'predicted_sentiment', 'sentiment_score', 'is_offensive', 'offensive_score'] +columns_to_show = ['text', 'predicted_sentiment', 'sentiment_score', 'is_offensive', 'offensive_score', 'emotion', 'emotion_score'] df_final_preview = df_final[columns_to_show]Similarly, update the sample output loop at lines 134-138 to display emotion results:
for i in range(min(3, len(final_dataset))): print(f"\n--- 샘플 {i+1} ---") print(f"원문: {final_dataset[i]['text'][:70].replace('\n', ' ')}...") print(f"예측 감성: {final_dataset[i]['predicted_sentiment']} (Score: {final_dataset[i]['sentiment_score']:.3f})") print(f"공격성 여부: {final_dataset[i]['is_offensive']} (Score: {final_dataset[i]['offensive_score']:.3f})") print(f"감정: {final_dataset[i]['emotion']} (Score: {final_dataset[i]['emotion_score']:.3f})")
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
jimin/news.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.13.3)
jimin/news.py
58-58: Multiple statements on one line (colon)
(E701)
59-59: Multiple statements on one line (colon)
(E701)
60-60: Multiple statements on one line (colon)
(E701)
90-90: Multiple statements on one line (colon)
(E701)
91-91: Multiple statements on one line (colon)
(E701)
🔇 Additional comments (3)
jimin/news.py (3)
1-16: LGTM!The imports and device configuration are properly set up. The device selection logic correctly falls back to CPU when CUDA is not available.
17-35: LGTM!Dataset loading and initial setup look correct. The subset selection and label information display are properly implemented.
1-10: Past review comment aboutdisplayNameError is no longer applicable.The previous review flagged a potential
NameErrorfor thedisplayfunction. However, the current code does not usedisplay()anywhere—it only usesprint()statements. This past concern has been resolved.
| return example | ||
|
|
||
| # 데이터셋에 적용 | ||
| final_dataset = processing_subset.map(analyze_offensive) |
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Fix dataset processing chain to preserve previous results.
The offensive classification results are assigned to final_dataset, but Line 120 overwrites this by mapping analyze_emotion on processing_subset (which only has sentiment results). This means the final dataset will be missing offensive classification data.
Apply this diff to fix the processing chain:
# 데이터셋에 적용
-final_dataset = processing_subset.map(analyze_offensive)
+processing_subset = processing_subset.map(analyze_offensive)
print("\n[작업 2] 혐오/공격성 분류 완료. 'is_offensive' 컬럼 추가.")This ensures that emotion analysis (line 120) is applied on top of both sentiment and offensive classifications.
📝 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.
| final_dataset = processing_subset.map(analyze_offensive) | |
| # 데이터셋에 적용 | |
| processing_subset = processing_subset.map(analyze_offensive) | |
| print("\n[작업 2] 혐오/공격성 분류 완료. 'is_offensive' 컬럼 추가.") |
🤖 Prompt for AI Agents
In jimin/news.py around lines 98 and 120, the offensive classification results
are assigned to final_dataset at line 98 but later line 120 maps analyze_emotion
over processing_subset, which drops the offensive labels; change the emotion
mapping to run on the accumulated final_dataset (i.e., map analyze_emotion on
final_dataset instead of processing_subset) so the pipeline composes sentiment
-> offensive -> emotion and preserves previous results.
| def analyze_emotion(example): | ||
| """'english_summary'의 감정을 분석하고 'emotion' 컬럼에 추가""" | ||
| # 감정 분석 파이프라인은 텍스트를 리스트 형태로 받으므로, 입력 텍스트를 리스트로 감싸야 합니다. | ||
| emotion_result = emotion_classifier(example['english_summary']) | ||
| # top_k=1 옵션으로 인해 결과는 [[{'label': '...', 'score': ...}]] 형태 | ||
| example['emotion'] = emotion_result[0][0]['label'] | ||
| example['emotion_score'] = emotion_result[0][0]['score'] # 점수도 함께 저장 | ||
| return example |
There was a problem hiding this comment.
Fix KeyError: 'english_summary' field does not exist in the dataset.
The analyze_emotion function attempts to access example['english_summary'], but this field is never created in the dataset. The dataset only contains the 'text' field from the tweet_eval dataset. This will cause a KeyError when the function runs.
Apply this diff to use the correct field:
def analyze_emotion(example):
- """'english_summary'의 감정을 분석하고 'emotion' 컬럼에 추가"""
+ """트윗 텍스트의 감정을 분석하고 'emotion' 컬럼에 추가"""
# 감정 분석 파이프라인은 텍스트를 리스트 형태로 받으므로, 입력 텍스트를 리스트로 감싸야 합니다.
- emotion_result = emotion_classifier(example['english_summary'])
+ emotion_result = emotion_classifier(example['text'])
# top_k=1 옵션으로 인해 결과는 [[{'label': '...', 'score': ...}]] 형태
example['emotion'] = emotion_result[0][0]['label']
example['emotion_score'] = emotion_result[0][0]['score'] # 점수도 함께 저장
return example📝 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.
| def analyze_emotion(example): | |
| """'english_summary'의 감정을 분석하고 'emotion' 컬럼에 추가""" | |
| # 감정 분석 파이프라인은 텍스트를 리스트 형태로 받으므로, 입력 텍스트를 리스트로 감싸야 합니다. | |
| emotion_result = emotion_classifier(example['english_summary']) | |
| # top_k=1 옵션으로 인해 결과는 [[{'label': '...', 'score': ...}]] 형태 | |
| example['emotion'] = emotion_result[0][0]['label'] | |
| example['emotion_score'] = emotion_result[0][0]['score'] # 점수도 함께 저장 | |
| return example | |
| def analyze_emotion(example): | |
| """트윗 텍스트의 감정을 분석하고 'emotion' 컬럼에 추가""" | |
| # 감정 분석 파이프라인은 텍스트를 리스트 형태로 받으므로, 입력 텍스트를 리스트로 감싸야 합니다. | |
| emotion_result = emotion_classifier(example['text']) | |
| # top_k=1 옵션으로 인해 결과는 [[{'label': '...', 'score': ...}]] 형태 | |
| example['emotion'] = emotion_result[0][0]['label'] | |
| example['emotion_score'] = emotion_result[0][0]['score'] # 점수도 함께 저장 | |
| return example |
🤖 Prompt for AI Agents
In jimin/news.py around lines 110-117, the function accesses a non-existent key
'english_summary' causing KeyError; change it to use the dataset's actual field
'text', pass that text as a single-item list into the emotion classifier, and
assign the returned label and score into example['emotion'] and
example['emotion_score']; also guard against missing text by using
example.get('text', '') or returning the example unchanged if no text is
present.
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tweet_eval 데이터셋에 대한 감성 및 공격성 분류
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