Instructions to use vijayjadhavaug/vj-distilbert-imdb-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vijayjadhavaug/vj-distilbert-imdb-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vijayjadhavaug/vj-distilbert-imdb-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vijayjadhavaug/vj-distilbert-imdb-sentiment") model = AutoModelForSequenceClassification.from_pretrained("vijayjadhavaug/vj-distilbert-imdb-sentiment") - Notebooks
- Google Colab
- Kaggle
π DistilBERT IMDB Sentiment Classifier
This is a fine-tuned DistilBERT model for sentiment analysis on the IMDB dataset.
It classifies reviews into Positive or Negative sentiments.
π Model Details
- Base Model:
distilbert-base-uncased - Dataset: IMDB Reviews
- Fine-Tuning Epochs: 3
- Batch Size: 16
- Evaluation Loss: 0.291
π Usage
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="vj/distilbert-imdb-sentiment")
result = classifier("The movie was absolutely fantastic!")
print(result) # [{'label': 'POSITIVE', 'score': 0.98}]
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