Instructions to use hfl/chinese-lert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/chinese-lert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/chinese-lert-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-lert-small") model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-lert-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0712a039f1d6e212d29ef6212f2eca3fc3b83b338766b565876a90f4f92c298c
- Size of remote file:
- 82.9 MB
- SHA256:
- 734331f1759958bfe8aa02430950aac593c7fc520b23f81c6864dcd121ffd03c
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