Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ProceduralTree/chinese_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProceduralTree/chinese_qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProceduralTree/chinese_qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProceduralTree/chinese_qa_model") model = AutoModelForSequenceClassification.from_pretrained("ProceduralTree/chinese_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ProceduralTree/chinese_qa_model: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/ProceduralTree/chinese_qa_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ProceduralTree/chinese_qa_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ProceduralTree/chinese_qa_model/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- 02429fe366d29001d3fa911ffb7221476aa904006b0cee80859125e1747aeecb
- Size of remote file:
- 409 MB
- SHA256:
- 3354fc42cc98f854259634a7f73ec42bb2c1affb7a38295603eb95a2f4414380
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