Instructions to use hf-tiny-model-private/tiny-random-XLMForTokenClassification 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-XLMForTokenClassification 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-XLMForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLMForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 735f261588dca73b94fb8213f1041c9e772b204ddabd00acfb93e21f369dec3d
- Size of remote file:
- 4.21 MB
- SHA256:
- 36d56e53c034fc2e9c541146eb835968fd73d7dd1478282938051f6e87c9512e
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