Instructions to use hf-tiny-model-private/tiny-random-IBertForSequenceClassification 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-IBertForSequenceClassification 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-IBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-IBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-IBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67ff9a43de03b4f5a4aad38cf31d071149a776d3d7ccdb8665df1f0ab53e5d79
- Size of remote file:
- 729 kB
- SHA256:
- 8226d7a84cd36fbac803a75d34bcc87bb35353e293e427abc6793d5b580ebd55
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