Instructions to use hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa8016d00635474138e34c25c5b9fe07b6fabf1487cb6b1fc703e166411ddaa4
- Size of remote file:
- 32.2 MB
- SHA256:
- 43fd45e6fcbeb68ae80aeda4c0a371903f0acd452515bc794f82076671da1533
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