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:
- d03ee83f17d04a1d9cdfec7d70bc4badcdac37546c383a6cf3f2f4c74e1354df
- Size of remote file:
- 4.19 MB
- SHA256:
- 0234a7207d48f682694472012525201a897517b60cc9f6a7dc3bed63f4f7b5f4
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