Instructions to use hf-tiny-model-private/tiny-random-XLMRobertaXLForTokenClassification 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-XLMRobertaXLForTokenClassification 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-XLMRobertaXLForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMRobertaXLForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLMRobertaXLForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 246c676f7fe13bea0a3ec23f650741db6c747810d66f3ff16b5df6dc9f6fc467
- Size of remote file:
- 17.1 MB
- SHA256:
- 592ccc69de22c051c7971596a69df9932829344473990c7bef3b3faf5705d488
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