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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42d428bfb498119cd9fc094f25d09f58e1fe6136e816f3cb8f5a6a0e834e82fe
- Size of remote file:
- 1.13 MB
- SHA256:
- 040920074a8fa914e326b149a9708504549a77cb60dfdaea57ab3427ec1d3ec5
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