Instructions to use hf-tiny-model-private/tiny-random-TransfoXLForSequenceClassification 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-TransfoXLForSequenceClassification 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-TransfoXLForSequenceClassification")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-TransfoXLForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f33bd5da4cfd9ce1718c499e069fa9fc5a814bbfa778307299b896359838b427
- Size of remote file:
- 5.38 MB
- SHA256:
- f3db1913a8df72358b97e18008fe9af8a105c98293961aeb13a9450d61a7a91a
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