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--- |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- text-classification |
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--- |
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# Model description |
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This is a neural net classifier and distilbert model chained with sklearn Pipeline trained on 20 news groups dataset. |
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## Intended uses & limitations |
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This model is trained for a tutorial and is not ready to be used in production. |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------| |
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| memory | | |
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| steps | [('tokenizer', HuggingfacePretrainedTokenizer(tokenizer='distilbert-base-uncased')), ('net', <class 'skorch.classifier.NeuralNetClassifier'>[initialized]( |
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module_=BertModule( |
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(bert): DistilBertForSequenceClassification( |
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(distilbert): DistilBertModel( |
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(embeddings): Embeddings( |
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(word_embeddings): Embedding(30522, 768, padding_idx=0) |
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(position_embeddings): Embedding(512, 768) |
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(LayerNorm): LayerNorm((768,), 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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(transformer): Transformer( |
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(layer): ModuleList( |
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(0): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(1): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(2): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(3): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(4): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(5): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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) |
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) |
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) |
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(pre_classifier): Linear(in_features=768, out_features=768, bias=True) |
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(classifier): Linear(in_features=768, out_features=20, bias=True) |
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(dropout): Dropout(p=0.2, inplace=False) |
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) |
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), |
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))] | |
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| verbose | False | |
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| tokenizer | HuggingfacePretrainedTokenizer(tokenizer='distilbert-base-uncased') | |
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| net | <class 'skorch.classifier.NeuralNetClassifier'>[initialized]( |
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module_=BertModule( |
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(bert): DistilBertForSequenceClassification( |
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(distilbert): DistilBertModel( |
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(embeddings): Embeddings( |
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(word_embeddings): Embedding(30522, 768, padding_idx=0) |
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(position_embeddings): Embedding(512, 768) |
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(LayerNorm): LayerNorm((768,), 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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(transformer): Transformer( |
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(layer): ModuleList( |
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(0): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(1): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(2): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(3): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(4): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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(5): TransformerBlock( |
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(attention): MultiHeadSelfAttention( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(q_lin): Linear(in_features=768, out_features=768, bias=True) |
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(k_lin): Linear(in_features=768, out_features=768, bias=True) |
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(v_lin): Linear(in_features=768, out_features=768, bias=True) |
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(out_lin): Linear(in_features=768, out_features=768, bias=True) |
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) |
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(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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(ffn): FFN( |
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(dropout): Dropout(p=0.1, inplace=False) |
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(lin1): Linear(in_features=768, out_features=3072, bias=True) |
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(lin2): Linear(in_features=3072, out_features=768, bias=True) |
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(activation): GELUActivation() |
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) |
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(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
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) |
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) |
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) |
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) |
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(pre_classifier): Linear(in_features=768, out_features=768, bias=True) |
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(classifier): Linear(in_features=768, out_features=20, bias=True) |
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(dropout): Dropout(p=0.2, inplace=False) |
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) |
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), |
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) | |
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| tokenizer__max_length | 256 | |
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| tokenizer__return_attention_mask | True | |
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| tokenizer__return_length | False | |
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| tokenizer__return_tensors | pt | |
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| tokenizer__return_token_type_ids | False | |
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| tokenizer__tokenizer | distilbert-base-uncased | |
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| tokenizer__train | False | |
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| tokenizer__verbose | 0 | |
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| tokenizer__vocab_size | | |
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| net__module | <class '__main__.BertModule'> | |
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| net__criterion | <class 'torch.nn.modules.loss.CrossEntropyLoss'> | |
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| net__optimizer | <class 'torch.optim.adamw.AdamW'> | |
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| net__lr | 5e-05 | |
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| net__max_epochs | 3 | |
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| net__batch_size | 8 | |
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| net__iterator_train | <class 'torch.utils.data.dataloader.DataLoader'> | |
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| net__iterator_valid | <class 'torch.utils.data.dataloader.DataLoader'> | |
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| net__dataset | <class 'skorch.dataset.Dataset'> | |
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| net__train_split | <skorch.dataset.ValidSplit object at 0x7f9945e18c90> | |
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| net__callbacks | [<skorch.callbacks.lr_scheduler.LRScheduler object at 0x7f9945da85d0>, <skorch.callbacks.logging.ProgressBar object at 0x7f9945da8250>] | |
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| net__predict_nonlinearity | auto | |
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| net__warm_start | False | |
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| net__verbose | 1 | |
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| net__device | cuda | |
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| net___params_to_validate | {'module__num_labels', 'module__name', 'iterator_train__shuffle'} | |
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| net__module__name | distilbert-base-uncased | |
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| net__module__num_labels | 20 | |
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| net__iterator_train__shuffle | True | |
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| net__classes | | |
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| net__callbacks__epoch_timer | <skorch.callbacks.logging.EpochTimer object at 0x7f993cb300d0> | |
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| net__callbacks__train_loss | <skorch.callbacks.scoring.PassthroughScoring object at 0x7f993cb306d0> | |
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| net__callbacks__train_loss__name | train_loss | |
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| net__callbacks__train_loss__lower_is_better | True | |
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| net__callbacks__train_loss__on_train | True | |
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| net__callbacks__valid_loss | <skorch.callbacks.scoring.PassthroughScoring object at 0x7f993cb30ed0> | |
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| net__callbacks__valid_loss__name | valid_loss | |
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| net__callbacks__valid_loss__lower_is_better | True | |
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| net__callbacks__valid_loss__on_train | False | |
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| net__callbacks__valid_acc | <skorch.callbacks.scoring.EpochScoring object at 0x7f993cb30410> | |
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| net__callbacks__valid_acc__scoring | accuracy | |
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| net__callbacks__valid_acc__lower_is_better | False | |
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| net__callbacks__valid_acc__on_train | False | |
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| net__callbacks__valid_acc__name | valid_acc | |
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| net__callbacks__valid_acc__target_extractor | <function to_numpy at 0x7f9945e46a70> | |
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| net__callbacks__valid_acc__use_caching | True | |
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| net__callbacks__LRScheduler | <skorch.callbacks.lr_scheduler.LRScheduler object at 0x7f9945da85d0> | |
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| net__callbacks__LRScheduler__policy | <class 'torch.optim.lr_scheduler.LambdaLR'> | |
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| net__callbacks__LRScheduler__monitor | train_loss | |
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| net__callbacks__LRScheduler__event_name | event_lr | |
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| net__callbacks__LRScheduler__step_every | batch | |
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| net__callbacks__LRScheduler__lr_lambda | <function lr_schedule at 0x7f9945d9c440> | |
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| net__callbacks__ProgressBar | <skorch.callbacks.logging.ProgressBar object at 0x7f9945da8250> | |
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| net__callbacks__ProgressBar__batches_per_epoch | auto | |
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| net__callbacks__ProgressBar__detect_notebook | True | |
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| net__callbacks__ProgressBar__postfix_keys | ['train_loss', 'valid_loss'] | |
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| net__callbacks__print_log | <skorch.callbacks.logging.PrintLog object at 0x7f993cb30dd0> | |
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| net__callbacks__print_log__keys_ignored | | |
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| net__callbacks__print_log__sink | <built-in function print> | |
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| net__callbacks__print_log__tablefmt | simple | |
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| net__callbacks__print_log__floatfmt | .4f | |
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| net__callbacks__print_log__stralign | right | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb {color: black;background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb pre{padding: 0;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-toggleable {background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-estimator:hover {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-item {z-index: 1;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-parallel-item:only-child::after {width: 0;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb div.sk-text-repr-fallback {display: none;}</style><div id="sk-4e25a02e-dd88-4cf5-9fc1-aa5db6749fbb" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('tokenizer',HuggingfacePretrainedTokenizer(tokenizer='distilbert-base-uncased')),('net',<class 'skorch.classifier.NeuralNetClassifier'>[initialized](module_=BertModule((bert): DistilBertForSequenceClassification((distilbert): DistilBertModel((embeddings): Embeddings((word_embeddings): Embedding(30522, 768, padding_idx=0)(position_embeddin...(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)))))(pre_classifier): Linear(in_features=768, out_features=768, bias=True)(classifier): Linear(in_features=768, out_features=20, bias=True)(dropout): Dropout(p=0.2, inplace=False))), |
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))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4905268f-3ec2-45fc-8bc7-80d9200ae5a5" type="checkbox" ><label for="4905268f-3ec2-45fc-8bc7-80d9200ae5a5" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('tokenizer',HuggingfacePretrainedTokenizer(tokenizer='distilbert-base-uncased')),('net',<class 'skorch.classifier.NeuralNetClassifier'>[initialized](module_=BertModule((bert): DistilBertForSequenceClassification((distilbert): DistilBertModel((embeddings): Embeddings((word_embeddings): Embedding(30522, 768, padding_idx=0)(position_embeddin...(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)))))(pre_classifier): Linear(in_features=768, out_features=768, bias=True)(classifier): Linear(in_features=768, out_features=20, bias=True)(dropout): Dropout(p=0.2, inplace=False))), |
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))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4c9a801f-37d5-4fdb-9892-222c86b927bf" type="checkbox" ><label for="4c9a801f-37d5-4fdb-9892-222c86b927bf" class="sk-toggleable__label sk-toggleable__label-arrow">HuggingfacePretrainedTokenizer</label><div class="sk-toggleable__content"><pre>HuggingfacePretrainedTokenizer(tokenizer='distilbert-base-uncased')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="062dd9ff-2b54-4166-90fc-fa3276cd482a" type="checkbox" ><label for="062dd9ff-2b54-4166-90fc-fa3276cd482a" class="sk-toggleable__label sk-toggleable__label-arrow">NeuralNetClassifier</label><div class="sk-toggleable__content"><pre><class 'skorch.classifier.NeuralNetClassifier'>[initialized](module_=BertModule((bert): DistilBertForSequenceClassification((distilbert): DistilBertModel((embeddings): Embeddings((word_embeddings): Embedding(30522, 768, padding_idx=0)(position_embeddings): Embedding(512, 768)(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(dropout): Dropout(p=0.1, inplace=False))(transformer): Transformer((layer): ModuleList((0): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(1): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(2): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(3): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(4): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True))(5): TransformerBlock((attention): MultiHeadSelfAttention((dropout): Dropout(p=0.1, inplace=False)(q_lin): Linear(in_features=768, out_features=768, bias=True)(k_lin): Linear(in_features=768, out_features=768, bias=True)(v_lin): Linear(in_features=768, out_features=768, bias=True)(out_lin): Linear(in_features=768, out_features=768, bias=True))(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)(ffn): FFN((dropout): Dropout(p=0.1, inplace=False)(lin1): Linear(in_features=768, out_features=3072, bias=True)(lin2): Linear(in_features=3072, out_features=768, bias=True)(activation): GELUActivation())(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)))))(pre_classifier): Linear(in_features=768, out_features=768, bias=True)(classifier): Linear(in_features=768, out_features=20, bias=True)(dropout): Dropout(p=0.2, inplace=False))), |
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## Evaluation Results |
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You can find the details about evaluation process and the evaluation results. |
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| Metric | Value | |
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| accuracy | 0.90562 | |
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| f1 score | 0.90562 | |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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[More Information Needed] |
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``` |
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</details> |
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# Model Card Authors |
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This model card is written by following authors: |
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[More Information Needed] |
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# Model Card Contact |
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You can contact the model card authors through following channels: |
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[More Information Needed] |
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# Citation |
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Below you can find information related to citation. |
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**BibTeX:** |
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``` |
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[More Information Needed] |
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``` |
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# Additional Content |
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## Confusion matrix |
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## Classification Report |
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<details> |
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<summary> Click to expand </summary> |
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| index | precision | recall | f1-score | support | |
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|--------------------------|-------------|----------|------------|-----------| |
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| alt.atheism | 0.927273 | 0.85 | 0.886957 | 120 | |
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| comp.graphics | 0.85906 | 0.876712 | 0.867797 | 146 | |
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| comp.os.ms-windows.misc | 0.893617 | 0.851351 | 0.871972 | 148 | |
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| comp.sys.ibm.pc.hardware | 0.666667 | 0.837838 | 0.742515 | 148 | |
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| comp.sys.mac.hardware | 0.901515 | 0.826389 | 0.862319 | 144 | |
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| comp.windows.x | 0.923077 | 0.891892 | 0.907216 | 148 | |
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| misc.forsale | 0.875862 | 0.869863 | 0.872852 | 146 | |
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| rec.autos | 0.893082 | 0.95302 | 0.922078 | 149 | |
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| rec.motorcycles | 0.937931 | 0.906667 | 0.922034 | 150 | |
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| rec.sport.baseball | 0.954248 | 0.979866 | 0.966887 | 149 | |
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| rec.sport.hockey | 0.979866 | 0.973333 | 0.976589 | 150 | |
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| sci.crypt | 0.993103 | 0.966443 | 0.979592 | 149 | |
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| sci.electronics | 0.869565 | 0.810811 | 0.839161 | 148 | |
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| sci.med | 0.973154 | 0.973154 | 0.973154 | 149 | |
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| sci.space | 0.973333 | 0.986486 | 0.979866 | 148 | |
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| soc.religion.christian | 0.927152 | 0.933333 | 0.930233 | 150 | |
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| talk.politics.guns | 0.961538 | 0.919118 | 0.93985 | 136 | |
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| talk.politics.mideast | 0.978571 | 0.971631 | 0.975089 | 141 | |
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| talk.politics.misc | 0.925234 | 0.853448 | 0.887892 | 116 | |
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| talk.religion.misc | 0.728972 | 0.829787 | 0.776119 | 94 | |
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| macro avg | 0.907141 | 0.903057 | 0.904009 | 2829 | |
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| weighted avg | 0.909947 | 0.90562 | 0.906742 | 2829 | |
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</details> |
