Instructions to use hf-internal-testing/tiny-random-RoCBertForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RoCBertForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-RoCBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RoCBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-RoCBertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 385c734513ca8eca799b7975f6082d850f2c97c0083e5eb81865717f1127fb42
- Size of remote file:
- 2.96 MB
- SHA256:
- fd23ed3829bdedd1a929270b404f1f7af130d813a2d24d1aa35c3ddda55ca024
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