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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df21801c90457736b379deea6cda6c968e4fca1f8bb05aa1d3fb1b9850aff566
- Size of remote file:
- 1.13 MB
- SHA256:
- 2dc38ef8843ea302a28146f98345b392f6c30858e51391a951cabe5c13c7a4cf
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