Instructions to use hf-tiny-model-private/tiny-random-XLNetForSequenceClassification 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-XLNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-XLNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93059aef54f98bb2d8b84445c2b203ab18d88b09909fb4d875fbad2ef651b79f
- Size of remote file:
- 4.38 MB
- SHA256:
- 194e6a9a197e5e18b82009947d6f60845ee3003691f78cc039d64f0541ad1199
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