Instructions to use hf-internal-testing/tiny-random-XLNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-XLNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-XLNetForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70bec42fa8cd2a18f66ed3ea46d80ebf603b80138e293a6d27db81de36aafef6
- Size of remote file:
- 4.38 MB
- SHA256:
- f5cb73547007296d32f85d3675f822e3a5e07ed2b224201eb7980b3c1fcd8ff0
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