Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use shed-e/thucnews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/thucnews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shed-e/thucnews")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shed-e/thucnews") model = AutoModelForSequenceClassification.from_pretrained("shed-e/thucnews", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shed-e/thucnews: direct link, hf CLI and curl.
- Browser
- Download file 239 MB
-
https://huggingface.co/shed-e/thucnews/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shed-e/thucnews/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shed-e/thucnews/resolve/main/pytorch_model.bin
239 MB
- Xet hash:
- 2ac9bf6e9a1c2de2c1318fd661d127646c0cc0e54ccce275fd9a97f9bf8d7b3c
- Size of remote file:
- 239 MB
- SHA256:
- e8b90c2e557a04572ad79024354c482b45bb1ce04bfecf10a2087f1c75521b22
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