Instructions to use hf-tiny-model-private/tiny-random-GPTJForSequenceClassification 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-GPTJForSequenceClassification 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-GPTJForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPTJForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-GPTJForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06ecb69f67243abcdcfc6e4c0c123913855a206a441571e993ebb8afc5a5a7c2
- Size of remote file:
- 1.71 MB
- SHA256:
- b548e0f8d34a866777c3cd3b2e912e6dc03e423167c765312f9e940dc29f0799
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