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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78772a9c960e407d05e21a6601e23822f1375188e3a9e6f3785396879f7741d7
- Size of remote file:
- 4.58 MB
- SHA256:
- a85f215ad2c897e853fca27fdb7033533b0268be791bf59ba333f33431abb5da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.