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WalkthroughRemoved cell metadata flags in Changes
Sequence Diagram(s)sequenceDiagram
actor User
participant Notebook as "Jupyter Notebook\n(softee220/huggingface_assignment.ipynb)"
participant Datasets as "datasets (IMDB)"
participant Pipeline as "Transformer Pipeline\n(SamLowe/roberta-base-go_emotions)"
participant Results as "DataFrame / CSV"
User ->> Notebook: open & run cells
Notebook ->> Datasets: load IMDB dataset (train split)
Datasets -->> Notebook: dataset object / subset (200)
Notebook ->> Pipeline: load text-classification pipeline
Notebook ->> Pipeline: analyze_emotion(text) [map over subset]
Pipeline -->> Notebook: labels + scores (varied shapes)
Notebook ->> Notebook: normalize outputs into {label, score}
Notebook ->> Results: assemble DataFrame (text, emotion)
Notebook ->> User: display results (optional save)
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Poem
Pre-merge checks and finishing touches❌ Failed checks (1 inconclusive)
✅ Passed checks (2 passed)
✨ Finishing touches🧪 Generate unit tests (beta)
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🧹 Nitpick comments (1)
softee220/huggingface_assignment.ipynb (1)
67-75: Importdisplayexplicitly.
display()isn’t defined in a plain Python runtime, and static analysis already flags it. Import it alongside the other notebook utilities to keep the code portable and lint-clean.-from datasets import load_dataset, DatasetDict -from transformers import pipeline -import torch -import pandas as pd # 데이터 확인용 +from datasets import load_dataset, DatasetDict +from transformers import pipeline +import torch +import pandas as pd # 데이터 확인용 +from IPython.display import display
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📒 Files selected for processing (1)
softee220/huggingface_assignment.ipynb(1 hunks)
🧰 Additional context used
🪛 Ruff (0.13.3)
softee220/huggingface_assignment.ipynb
43-43: Undefined name display
(F821)
93-93: Undefined name display
(F821)
99-99: Undefined name display
(F821)
| "!pip install transformers datasets sentencepiece accelerate torch\n", | ||
| "\n", | ||
| "import datasets\n", | ||
| "from datasets import load_dataset, DatasetDict\n", | ||
| "from transformers import pipeline\n", | ||
| "import torch\n", | ||
| "import pandas as pd # 데이터 확인용\n", | ||
| "\n", | ||
| "# GPU 사용 가능 여부 확인 및 설정 (Colab에서는 보통 GPU 사용 가능)\n", | ||
| "device = 0 if torch.cuda.is_available() else -1\n", | ||
| "print(f\"사용 가능한 디바이스: {'GPU' if device == 0 else 'CPU'}\")" |
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Avoid downgrading Colab’s CUDA-enabled torch.
Running pip install torch in Colab removes the preinstalled GPU build and pulls a CPU-only wheel, so torch.cuda.is_available() flips to False and all inference falls back to CPU. Drop the plain torch install (or replace it with a CUDA-specific wheel) to preserve GPU acceleration.
-!pip install transformers datasets sentencepiece accelerate torch
+!pip install transformers datasets sentencepiece accelerate📝 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.
| "!pip install transformers datasets sentencepiece accelerate torch\n", | |
| "\n", | |
| "import datasets\n", | |
| "from datasets import load_dataset, DatasetDict\n", | |
| "from transformers import pipeline\n", | |
| "import torch\n", | |
| "import pandas as pd # 데이터 확인용\n", | |
| "\n", | |
| "# GPU 사용 가능 여부 확인 및 설정 (Colab에서는 보통 GPU 사용 가능)\n", | |
| "device = 0 if torch.cuda.is_available() else -1\n", | |
| "print(f\"사용 가능한 디바이스: {'GPU' if device == 0 else 'CPU'}\")" | |
| !pip install transformers datasets sentencepiece accelerate | |
| import datasets | |
| from datasets import load_dataset, DatasetDict | |
| from transformers import pipeline | |
| import torch | |
| import pandas as pd # 데이터 확인용 | |
| # GPU 사용 가능 여부 확인 및 설정 (Colab에서는 보통 GPU 사용 가능) | |
| device = 0 if torch.cuda.is_available() else -1 | |
| print(f"사용 가능한 디바이스: {'GPU' if device == 0 else 'CPU'}") |
🤖 Prompt for AI Agents
In softee220/huggingface_assignment.ipynb around lines 65 to 75, the notebook
unconditionally runs a plain "pip install torch" which on Colab replaces the
preinstalled CUDA-enabled torch with a CPU-only wheel and disables GPU; remove
the plain "pip install torch" line (or replace it with a Colab-compatible
CUDA-specific wheel only when needed) and leave the device detection code intact
so torch.cuda.is_available() can correctly detect the GPU, or add a conditional
install that skips torch reinstallation when a CUDA-enabled build is present.
1 participant
Imdb 데이터 + 감정분석모델
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