Commit
•
eeea4d4
1
Parent(s):
aff5c65
Upload config
Browse files- config.json +2 -17
- configuration_bert.py +3 -24
config.json
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@@ -1,18 +1,10 @@
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{
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"affine":
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"alpha": 1,
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"auto_map": {
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"AutoConfig": "configuration_bert.BertConfig"
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"AutoModelForSequenceClassification": "modeling_bert.BertForSequenceClassification"
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},
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"center": false,
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"classifier_dropout": null,
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"do_pcr": true,
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"do_pfsa": true,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"normalize": false,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"num_iters": 1,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"q": 5,
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"r": 1,
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"return_mean": true,
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"return_std": true,
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"torch_dtype": "float32",
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"transformers_version": "4.33.3",
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"type_vocab_size": 2,
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"use_cache": true,
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{
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"affine": true,
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"attention_probs_dropout_prob": 0.1,
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"auto_map": {
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"AutoConfig": "configuration_bert.BertConfig"
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},
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"layer_norm_eps": 1e-12,
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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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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.33.3",
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"type_vocab_size": 2,
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"use_cache": true,
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configuration_bert.py
CHANGED
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@@ -22,18 +22,8 @@ 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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affine=
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q=5,
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r=1,
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center=False,
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num_iters=1,
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return_mean=True,
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return_std=True,
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normalize=False,
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do_pcr=True,
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do_pfsa=True,
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alpha=1,
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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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self.affine = affine
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self.q = q
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self.r = r
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self.center = center
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self.num_iters = num_iters
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self.return_mean = return_mean
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self.return_std = return_std
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self.normalize = normalize
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self.do_pcr = do_pcr
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self.do_pfsa = do_pfsa
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self.alpha = alpha
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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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affine=True,
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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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self.affine = affine
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