Instructions to use hf-tiny-model-private/tiny-random-AlbertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-AlbertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-AlbertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f1cddec721caf0dfd4fce7e82a3ab81eb33d56ec745df356c35e74f070a4ce4
- Size of remote file:
- 15.9 MB
- SHA256:
- 68993de4c00e8a7ecdbe6397786b31b10bfd9297d0b7f43867745a070ae30873
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