PyTorch Book - Sentiment Analysis Model
π λͺ¨λΈ μ€λͺ (Model Description)
μ΄ λͺ¨λΈμ μν 리뷰μ λν κ°μ± λΆμ(Sentiment Analysis)μ μνν©λλ€. HuggingFace Transformers λΌμ΄λΈλ¬λ¦¬μ DistilBERT λͺ¨λΈμ κΈ°λ°μΌλ‘ IMDb λ°μ΄ν°μ μμ νμ΅λμμ΅λλ€.
This model performs sentiment analysis on movie reviews. Based on DistilBERT from HuggingFace Transformers, fine-tuned on the IMDb dataset.
π― νμ΅ λ°μ΄ν° (Training Data)
- Dataset: IMDb Movie Reviews
- Size: 25,000 training samples
- Classes: 2 (Positive / Negative)
- Language: English
π μ¬μ© λ°©λ² (Usage)
Python
from transformers import pipeline
# νμ΄νλΌμΈ μμ±
classifier = pipeline("sentiment-analysis", model="aiegoo/pytorch-book")
# κ°μ± λΆμ μν
result = classifier("This movie is amazing!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998}]
μ§μ λͺ¨λΈ λ‘λ
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aiegoo/pytorch-book")
model = AutoModelForSequenceClassification.from_pretrained("aiegoo/pytorch-book")
# ν ν°ν λ° μμΈ‘
inputs = tokenizer("I love this movie!", return_tensors="pt")
outputs = model(**inputs)
π μ±λ₯ (Performance)
- Accuracy: ~92% (on test set)
- F1 Score: ~0.91
- Model Size: 67M parameters (DistilBERT)
ποΈ λͺ¨λΈ μν€ν μ² (Model Architecture)
- Base Model:
distilbert-base-uncased-finetuned-sst-2-english - Type: Sequence Classification
- Framework: PyTorch + Transformers
π νμ΅ κ³Όμ (Training Process)
- ν ν¬λμ΄μ : BERT WordPiece tokenizer
- μ μ²λ¦¬: μλ¬Έμ λ³ν, μ΅λ 512 ν ν°
- λ°μ΄ν°μ : IMDb 25,000 samples
- λ°°μΉ ν¬κΈ°: 16
- μ΅μ ν: AdamW
π κ΅μ‘ λͺ©μ (Educational Purpose)
μ΄ λͺ¨λΈμ PyTorch Book νμ΅ κ³Όμ μ μΌλΆλ‘ μμ±λμμ΅λλ€:
- Week 2, Day 6: HuggingFace Transformers
- Topic: Tokenizer, Dataset, Pre-trained Models
- Environment: Local Jupyter + Google Colab
This model was created as part of the PyTorch Book learning curriculum.
β οΈ μ νμ¬ν (Limitations)
- μμ΄ ν μ€νΈμ μ΅μ νλ¨ (Optimized for English text)
- μν 리뷰 λλ©μΈμ νΉνλ¨ (Specialized for movie review domain)
- κΈ΄ ν μ€νΈλ 512 ν ν°μΌλ‘ μλ¦Ό (Long text truncated to 512 tokens)
π λΌμ΄μ μ€ (License)
MIT License - μμ λ‘κ² μ¬μ© κ°λ₯ν©λλ€.
π κ΄λ ¨ λ§ν¬ (Related Links)
π€ μ μμ (Created by)
- Author: aiegoo
- Course: AI Track - Week 02, Day 6
- Date: November 2025
Created with β€οΈ for learning PyTorch and Transformers
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