Instructions to use jiangwf/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jiangwf/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jiangwf/my_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jiangwf/my_model") model = AutoModelForSequenceClassification.from_pretrained("jiangwf/my_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f580ca3ecee22e896ea0035d699ec3089706ff189a507be595eef2f1618d07bb
- Size of remote file:
- 4.86 kB
- SHA256:
- ee439cf97939f05e5b65581458fff1094a2d629700310ea5adca77b9c906f6fa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.