Instructions to use powerpuf-bot/wangchanberta-xet_ref-params with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use powerpuf-bot/wangchanberta-xet_ref-params with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="powerpuf-bot/wangchanberta-xet_ref-params")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("powerpuf-bot/wangchanberta-xet_ref-params") model = AutoModelForQuestionAnswering.from_pretrained("powerpuf-bot/wangchanberta-xet_ref-params", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from powerpuf-bot/wangchanberta-xet_ref-params: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/powerpuf-bot/wangchanberta-xet_ref-params/resolve/main/training_args.bin
- Command line
-
hf download hf://powerpuf-bot/wangchanberta-xet_ref-params/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/powerpuf-bot/wangchanberta-xet_ref-params/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 910dd67c7abc8ddd2041e745b48e544c7c1e15daf34957affce68033d7a3564b
- Size of remote file:
- 4.6 kB
- SHA256:
- 9ab88df1895a629b21ef36a3f7166b4c7dee7525d30cfc4b7f3c95f4b9bfc897
路
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