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 eval.txt from boronbrown48/wangchanberta-topic-classification: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/boronbrown48/wangchanberta-topic-classification/resolve/main/eval.txt
- Command line
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hf download hf://boronbrown48/wangchanberta-topic-classification/eval.txt
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curl -L -o eval.txt https://huggingface.co/boronbrown48/wangchanberta-topic-classification/resolve/main/eval.txt
297 Bytes
| {'epoch': 1.68, | |
| 'eval_accuracy': 0.7569043532532377, | |
| 'eval_f1-macro': 0.7521077090954053, | |
| 'eval_loss': 0.7462636232376099, | |
| 'eval_precision': 0.7506802860775812, | |
| 'eval_recall': 0.7569043532532377, | |
| 'eval_runtime': 25.5598, | |
| 'eval_samples_per_second': 250.745, | |
| 'eval_steps_per_second': 31.377} |