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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2089bbf8a491c9471e1cb8a04a9fe5350d91d2beccb3f44f167d290a3372ca6f
- Size of remote file:
- 1.65 MB
- SHA256:
- bfdd09e29c9a82b323aee41c4ec0de983c1eec895c4777f7859b46e40a929353
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