Instructions to use conan1024hao/cjkbert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use conan1024hao/cjkbert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="conan1024hao/cjkbert-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("conan1024hao/cjkbert-small") model = AutoModelForMaskedLM.from_pretrained("conan1024hao/cjkbert-small") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -23,7 +23,7 @@ tokenizer = AutoTokenizer.from_pretrained("conan1024hao/cjkbert-small")
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model = AutoModelForMaskedLM.from_pretrained("conan1024hao/cjkbert-small")
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```
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- Before you fine-tune downstream tasks, you don't need any text segmentation.
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- (Though you may obtain better results if you applied morphological analysis to the data before fine-tuning
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### Morphological analysis tools
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- ZH: For Chinese, we use [LTP](https://github.com/HIT-SCIR/ltp).
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model = AutoModelForMaskedLM.from_pretrained("conan1024hao/cjkbert-small")
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```
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- Before you fine-tune downstream tasks, you don't need any text segmentation.
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- (Though you may obtain better results if you applied morphological analysis to the data before fine-tuning)
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### Morphological analysis tools
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- ZH: For Chinese, we use [LTP](https://github.com/HIT-SCIR/ltp).
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