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:
- 5092ab33453a2d1f3e4727a623df2a8e5f348d011578aef40f4d772156dd6d94
- Size of remote file:
- 4.28 MB
- SHA256:
- efe8fc22e9a2937184ef5acf94b8cb3fd53275d4adf508df7a3886516f493519
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