Instructions to use magicslabnu/OutEffHop_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magicslabnu/OutEffHop_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="magicslabnu/OutEffHop_bert_base", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("magicslabnu/OutEffHop_bert_base", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("magicslabnu/OutEffHop_bert_base", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update configuration_bert.py
Browse files- configuration_bert.py +1 -3
configuration_bert.py
CHANGED
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@@ -115,8 +115,7 @@ class BertConfig(PretrainedConfig):
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pad_token_id=0,
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position_embedding_type="absolute",
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use_cache=True,
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classifier_dropout=None
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attn_implementation='OutEffHop',
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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@@ -136,7 +135,6 @@ class BertConfig(PretrainedConfig):
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.classifier_dropout = classifier_dropout
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self.attn_implementation = attn_implementation
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class BertOnnxConfig(OnnxConfig):
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pad_token_id=0,
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position_embedding_type="absolute",
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use_cache=True,
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+
classifier_dropout=None
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, **kwargs)
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.classifier_dropout = classifier_dropout
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class BertOnnxConfig(OnnxConfig):
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