Fill-Mask
Transformers
PyTorch
luke
named entity recognition
relation classification
question answering
Instructions to use studio-ousia/mluke-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/mluke-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/mluke-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/mluke-base") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/mluke-base") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse filesDisable use_entity_aware_attention by default
- config.json +1 -1
config.json
CHANGED
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@@ -29,6 +29,6 @@
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"type_vocab_size": 1,
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"use_cache": true,
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"use_deepspeed_transformer_layer": false,
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-
"use_entity_aware_attention":
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"vocab_size": 250004
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}
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"type_vocab_size": 1,
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"use_cache": true,
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"use_deepspeed_transformer_layer": false,
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+
"use_entity_aware_attention": false,
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"vocab_size": 250004
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}
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