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:
- 1d7da4a48e6d773e866a6d80cb96e79496df43bc121f71e41ed502eb16e60003
- Size of remote file:
- 2.96 MB
- SHA256:
- ad54bac12e867b5b817ab7c9b1736f50a3ceb6f222375a9d78ce96f291d34b20
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