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:
- f307670f895d8d726bf31bfa8d597c409fa1842f3d44d1407a7a7ccc59cb857f
- Size of remote file:
- 32.2 MB
- SHA256:
- 0fa6ad58e640fc70875f861c14b8832dff85adf138677020ede703733b057d18
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