Instructions to use boronbrown48/wangchanberta-topic-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boronbrown48/wangchanberta-topic-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boronbrown48/wangchanberta-topic-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boronbrown48/wangchanberta-topic-classification") model = AutoModelForSequenceClassification.from_pretrained("boronbrown48/wangchanberta-topic-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from boronbrown48/wangchanberta-topic-classification: direct link, hf CLI and curl.
- Browser
- Download file 421 MB
-
https://huggingface.co/boronbrown48/wangchanberta-topic-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://boronbrown48/wangchanberta-topic-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/boronbrown48/wangchanberta-topic-classification/resolve/main/pytorch_model.bin
421 MB
- Xet hash:
- b671779f42333892329c31745fc05a66d2a2cf00a999617e617625f3fbeb6a7b
- Size of remote file:
- 421 MB
- SHA256:
- 272b9bf82a8ff57d7ed7edaaf7183eed9391c95f3b74be752c58c0d66012da2a
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