Instructions to use Cube/ShijiBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cube/ShijiBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Cube/ShijiBERT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cube/ShijiBERT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -0
config.json
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@@ -9,6 +9,7 @@
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"model_type": "bert",
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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