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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eff69357f93d439eb8b5d6df87a688a531da4563d802afee44b49b2f5d9fa6a
- Size of remote file:
- 470 kB
- SHA256:
- 85bb276162e1507126640c2c50e2311ecffaf727af6defe6efa0776b6f540b22
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