Instructions to use uer/roberta-base-finetuned-jd-binary-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uer/roberta-base-finetuned-jd-binary-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="uer/roberta-base-finetuned-jd-binary-chinese")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("uer/roberta-base-finetuned-jd-binary-chinese") model = AutoModelForSequenceClassification.from_pretrained("uer/roberta-base-finetuned-jd-binary-chinese") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9d91781d741845e149011515d794981aeafd2fd2564c5fd051d0bb692106b8b
- Size of remote file:
- 409 MB
- SHA256:
- 57d7f201ca4951f2ce1b4c7c2fbe9cc55cc2246c9ca03ea29863a23b9f7bee05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.