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:
- 5f26228e7ee3edbe3f54f8b1462a3d699904e6ed1f62b2c76fb36c1722500e98
- Size of remote file:
- 1.04 MB
- SHA256:
- b03889e7df08b139420f7ec93c2036602e13d3537751d74123db5e0e1e1d1555
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