Instructions to use hf-tiny-model-private/tiny-random-GPT2ForSequenceClassification 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-GPT2ForSequenceClassification 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-GPT2ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPT2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-GPT2ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6193797384aa7f28a62790e3c2a0b8c80e1e9f3f07294070a03c2d6c6bf8b5a
- Size of remote file:
- 420 kB
- SHA256:
- 65e5f754e478e3bb49f31f443b5a72658ea134f1c57eb34a52ba0d160bc1f7f1
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