Text Classification
Transformers
Safetensors
qwen2
text-generation
llama-factory
lora
news-classification
chinese
deepseek-r1
qwen
text-embeddings-inference
Instructions to use real-jiakai/DeepSeek-R1-Distill-Qwen-7B-News-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use real-jiakai/DeepSeek-R1-Distill-Qwen-7B-News-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="real-jiakai/DeepSeek-R1-Distill-Qwen-7B-News-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("real-jiakai/DeepSeek-R1-Distill-Qwen-7B-News-Classifier") model = AutoModelForMultimodalLM.from_pretrained("real-jiakai/DeepSeek-R1-Distill-Qwen-7B-News-Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c853156c1310d798ca6219adea9a9319e1afe66469f5fb3dbd97f5f969ca9453
- Size of remote file:
- 4.33 GB
- SHA256:
- 439cc3acbfa003e5b8fac8707264d5b8daafbb9e0ffa11b351891759afb951c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.