Instructions to use LiYuan/Amazon-Cup-Cross-Encoder-Regression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiYuan/Amazon-Cup-Cross-Encoder-Regression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LiYuan/Amazon-Cup-Cross-Encoder-Regression")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LiYuan/Amazon-Cup-Cross-Encoder-Regression") model = AutoModelForSequenceClassification.from_pretrained("LiYuan/Amazon-Cup-Cross-Encoder-Regression", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80f4871dccd17ad568ba537ae8d4317ef0f47993745065e9f233280e6c570e8e
- Size of remote file:
- 329 MB
- SHA256:
- 510303b626dff11f316882c39ba0e998d4736b72b723e33f2709a0560342229b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.