Instructions to use hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d508fec1d69480398fcc96953ac259e816c83e97dbd2dfacd812838bc3dfb804
- Size of remote file:
- 1.59 MB
- SHA256:
- 3b8bbdbf4f4c8e9eb9ff0cea8130f0e2573c0fa566685e350d1826a27aa996f4
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