Instructions to use Classical/Yinka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Classical/Yinka with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Classical/Yinka")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Classical/Yinka") model = AutoModel.from_pretrained("Classical/Yinka") - Notebooks
- Google Colab
- Kaggle
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## Yinka
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Yinka embedding 模型是在开
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## 使用方法
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该模型的使用方法同[stella-v3.5-mrl](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d)一样, 无需任何前缀。
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## Yinka
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Yinka embedding 模型是在开源模型[stella-v3.5-mrl](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d)上续训的,采用了[piccolo2](https://huggingface.co/sensenova/piccolo-large-zh-v2)提到的多任务混合损失(multi-task hybrid loss training)。同样本模型也支持了可变的向量维度。
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## 使用方法
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该模型的使用方法同[stella-v3.5-mrl](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d)一样, 无需任何前缀。
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