Instructions to use hf-tiny-model-private/tiny-random-GPTJForSequenceClassification 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-GPTJForSequenceClassification 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-GPTJForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPTJForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-GPTJForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d72c7e9bdf42e497251ccc73dd4bb019e629d888f5263ed02c3e728c7ba1a9b
- Size of remote file:
- 1.7 MB
- SHA256:
- 341a7d4fc1e74c96b9842e49b65ebce3f1fba9aae266dabcc6aee2d8683a4efd
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