Instructions to use SBB/sbb_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SBB/sbb_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SBB/sbb_ner")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SBB/sbb_ner", dtype="auto") - Notebooks
- Google Colab
- Kaggle
add "model_type" to config.json
Browse files- config.json +1 -0
config.json
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@@ -7,6 +7,7 @@
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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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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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 512,
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"model_type": "bert",
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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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