Instructions to use lixugang/ch_text_001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lixugang/ch_text_001 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lixugang/ch_text_001")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lixugang/ch_text_001") model = AutoModelForSequenceClassification.from_pretrained("lixugang/ch_text_001", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 046fb69c6ac2790278a897a5541d98e6feaebcb01baa69b35c797dd373d7eb7d
- Size of remote file:
- 409 MB
- SHA256:
- 9f720c97725329014da2abb4bc0aa3a635379beaa3debf94362cd9107609059b
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