Instructions to use xiongzhongchi/transsion_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiongzhongchi/transsion_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiongzhongchi/transsion_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiongzhongchi/transsion_bert") model = AutoModelForSequenceClassification.from_pretrained("xiongzhongchi/transsion_bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03ec190277187aa91f1c60d619693387c6ae9bb740e1c9d470ed275a59938e7e
- Size of remote file:
- 438 MB
- SHA256:
- 4f982b904be9f6bd0907c212175487295013bb5debcbbe332069dc2e394edb49
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.