Instructions to use hf-tiny-model-private/tiny-random-RoCBertForTokenClassification 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-RoCBertForTokenClassification 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-RoCBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25b7a5f6b2bdfb5d011ffbeed4b6dd8f7b710cecbdf64f99051895d225a27f28
- Size of remote file:
- 2.98 MB
- SHA256:
- 3ea66403e250e93ab4ac3c0e41c93ed5065747375822411313fcfb2dc2f75f9b
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