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:
- 4113c1e6954dfc364433ddbbd1a80f90fbb28a57451cb458300b6cad2b7107f4
- Size of remote file:
- 32.2 MB
- SHA256:
- 9a071864ac3d2c9d9dd54f245fcfeb30d6c66de019ab1b4d7535430e72f7427b
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