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 training_args.bin from boronbrown48/wangchanberta-topic-classification: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/boronbrown48/wangchanberta-topic-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://boronbrown48/wangchanberta-topic-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/boronbrown48/wangchanberta-topic-classification/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- bcdb6b1f2583520834b116fdb04f63ed9ffbb2dbfd3266595020172f2798844b
- Size of remote file:
- 2.8 kB
- SHA256:
- b9f7478578af32abad243c1d31f139998878d82d7b8404aec7ee02dc318cca5b
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