Instructions to use HAO-K/powerinfer-seq-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HAO-K/powerinfer-seq-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HAO-K/powerinfer-seq-cls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HAO-K/powerinfer-seq-cls") model = AutoModelForSequenceClassification.from_pretrained("HAO-K/powerinfer-seq-cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19a7e7da368aeb429f4a6aad927cceee8af2e7af3a6e07db7792acbdb5813e58
- Size of remote file:
- 7.35 kB
- SHA256:
- 9b8bbb5c56dcab0084ded88a54742670b8aa4a01174c67d55bd39cae8e27a104
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.