Instructions to use hf-tiny-model-private/tiny-random-ElectraForSequenceClassification 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-ElectraForSequenceClassification 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-ElectraForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 077d9b657eb911677ce541e802b65b1d85896450573fa994481fff6401ea1a3e
- Size of remote file:
- 1.02 MB
- SHA256:
- 2a9e254704bd1a18e38d6de507c481d4ed88d2eabcb219eae46cb5b45a570510
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