Instructions to use textattack/albert-base-v2-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-imdb") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c18c16be6130edac41daa6383814645a8d9c0cb923cff620e7a225d34199528
- Size of remote file:
- 46.7 MB
- SHA256:
- f2039fee1ab2259f1c4d5c254a89a2cb7f1a6e0a66ddcd467c463b641e5d3e6e
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