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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e0d2305a08b0fd6b606ee1fe1e6ad057d4ebe404db35b4bb057cc2cc9f2e265
- Size of remote file:
- 1.6 MB
- SHA256:
- d0290dad0a3d6a0bd9c5c6c2e8f606f7e1fdf88d88da0ee3d4402d899d607ba7
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