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--- |
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library_name: transformers |
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license: apache-2.0 |
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--- |
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# BEE-spoke-data/tiny-random-MPNetForMaskedLM |
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test model and test tokenizer (has smaller vocab) |
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``` |
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MPNetForMaskedLM( |
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(mpnet): MPNetModel( |
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(embeddings): MPNetEmbeddings( |
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(word_embeddings): Embedding(1125, 64, padding_idx=1) |
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(position_embeddings): Embedding(512, 64, padding_idx=1) |
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(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(encoder): MPNetEncoder( |
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(layer): ModuleList( |
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(0-4): 5 x MPNetLayer( |
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(attention): MPNetAttention( |
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(attn): MPNetSelfAttention( |
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(q): Linear(in_features=64, out_features=64, bias=True) |
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(k): Linear(in_features=64, out_features=64, bias=True) |
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(v): Linear(in_features=64, out_features=64, bias=True) |
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(o): Linear(in_features=64, out_features=64, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(intermediate): MPNetIntermediate( |
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(dense): Linear(in_features=64, out_features=64, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): MPNetOutput( |
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(dense): Linear(in_features=64, out_features=64, bias=True) |
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(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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) |
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(relative_attention_bias): Embedding(32, 4) |
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) |
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) |
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(lm_head): MPNetLMHead( |
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(dense): Linear(in_features=64, out_features=64, bias=True) |
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(layer_norm): LayerNorm((64,), eps=1e-12, elementwise_affine=True) |
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(decoder): Linear(in_features=64, out_features=1125, bias=True) |
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) |
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) |
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``` |
