Instructions to use hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification 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-GPTNeoForSequenceClassification 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-GPTNeoForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3e0aeb9c3c4108793eac3d72e72f1dac80562cc376049e3a713b77f7e4972a5
- Size of remote file:
- 1.45 MB
- SHA256:
- 2f213e82ac5f10908203773e90ec1bf5d1478d731f650a687e11078b1bde0fc5
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