Instructions to use airesearch/wangchanbart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use airesearch/wangchanbart-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="airesearch/wangchanbart-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("airesearch/wangchanbart-base") model = AutoModel.from_pretrained("airesearch/wangchanbart-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
artifact: Add pre-trained weight and vocab files
Browse files- pytorch_model.bin +3 -0
- sentencepiece.bpe.model +3 -0
- sentencepiece.bpe.vocab +3 -0
pytorch_model.bin
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oid sha256:49072d5fe0f8bcbec76f00bfb6f4f79cb59e5781459463c0472fbb055362d7e4
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size 573367257
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sentencepiece.bpe.model
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oid sha256:7d541df5b549108278772bc65678eac34c37d8bf055592be5f6d284fab115495
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size 1538715
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sentencepiece.bpe.vocab
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c47450bee38c05f041eb9b75c3142c8fd36f138dc6e5de2d1b0cfba8d5d3204
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size 964312
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