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:
- 6f0e3d6eaa0f52928c1f51d679624e42dd9f72e4169716aa49028a2d7568a7ea
- Size of remote file:
- 646 kB
- SHA256:
- 9eb700423b2604e555abf45cf9912bc16015c7f3f9d9d25fcff95c19cd9e15cf
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