Instructions to use hf-tiny-model-private/tiny-random-ElectraForTokenClassification 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-ElectraForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-ElectraForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 059325b85039c4299017323c7a4ec5ab670bdcf0caddbe37b577b6ddcbf3a1ce
- Size of remote file:
- 1.01 MB
- SHA256:
- 7f1ec1f23873a9cfa594f886c0db10319b8c1b72368960c825e15598d496b1a8
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