Instructions to use hf-tiny-model-private/tiny-random-PerceiverForSequenceClassification 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-PerceiverForSequenceClassification 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-PerceiverForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc0e35cf9b2a38262006285ca712735b0ebfa73c8b43dbad7a98eaa27475f50e
- Size of remote file:
- 1.14 MB
- SHA256:
- a334723bb67bb80da2ad6c4a7adc6de32def8ca8203d61558a8dd5c95cede343
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.