Instructions to use RichardLyu/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RichardLyu/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RichardLyu/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RichardLyu/results") model = AutoModelForSequenceClassification.from_pretrained("RichardLyu/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bedadbd07f55536fc16fee55f989658ea07deb2a497809be0f431d2fcfcb6185
- Size of remote file:
- 4.92 kB
- SHA256:
- 3e3f06a01cc308245a77f7a2d9f6fa51b1b55f642c3653e81468140a133eacb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.