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:
- a3c013f9591e27802b2eed95cc0680cc8b95072b0ee65833c4602f15a610ad6c
- Size of remote file:
- 2.98 MB
- SHA256:
- a51626d09756230a647061831a3a33f0b0d30fdbfde60f04faa4e6b5f336627e
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