This is a neural net classifier and distilbert model chained with sklearn Pipeline trained on 20 news groups dataset.
This model is trained for a tutorial and is not ready to be used in production.
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|---|
| memory | |
| 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_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)
)
),
))] |
| verbose | False |
| 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_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)
)
),
) |
| tokenizer__max_length | 256 |
| tokenizer__return_attention_mask | True |
| tokenizer__return_length | False |
| tokenizer__return_tensors | pt |
| tokenizer__return_token_type_ids | False |
| tokenizer__tokenizer | distilbert-base-uncased |
| tokenizer__train | False |
| tokenizer__verbose | 0 |
| tokenizer__vocab_size | |
| net__module | <class 'main.BertModule'> |
| net__criterion | <class 'torch.nn.modules.loss.CrossEntropyLoss'> |
| net__optimizer | <class 'torch.optim.adamw.AdamW'> |
| net__lr | 5e-05 |
| net__max_epochs | 3 |
| net__batch_size | 8 |
| net__iterator_train | <class 'torch.utils.data.dataloader.DataLoader'> |
| net__iterator_valid | <class 'torch.utils.data.dataloader.DataLoader'> |
| net__dataset | <class 'skorch.dataset.Dataset'> |
| net__train_split | <skorch.dataset.ValidSplit object at 0x7f9945e18c90> |
| net__callbacks | [<skorch.callbacks.lr_scheduler.LRScheduler object at 0x7f9945da85d0>, <skorch.callbacks.logging.ProgressBar object at 0x7f9945da8250>] |
| net__predict_nonlinearity | auto |
| net__warm_start | False |
| net__verbose | 1 |
| net__device | cuda |
| net___params_to_validate | {'module__num_labels', 'module__name', 'iterator_train__shuffle'} |
| net__module__name | distilbert-base-uncased |
| net__module__num_labels | 20 |
| net__iterator_train__shuffle | True |
| net__classes | |
| net__callbacks__epoch_timer | <skorch.callbacks.logging.EpochTimer object at 0x7f993cb300d0> |
| net__callbacks__train_loss | <skorch.callbacks.scoring.PassthroughScoring object at 0x7f993cb306d0> |
| net__callbacks__train_loss__name | train_loss |
| net__callbacks__train_loss__lower_is_better | True |
| net__callbacks__train_loss__on_train | True |
| net__callbacks__valid_loss | <skorch.callbacks.scoring.PassthroughScoring object at 0x7f993cb30ed0> |
| net__callbacks__valid_loss__name | valid_loss |
| net__callbacks__valid_loss__lower_is_better | True |
| net__callbacks__valid_loss__on_train | False |
| net__callbacks__valid_acc | <skorch.callbacks.scoring.EpochScoring object at 0x7f993cb30410> |
| net__callbacks__valid_acc__scoring | accuracy |
| net__callbacks__valid_acc__lower_is_better | False |
| net__callbacks__valid_acc__on_train | False |
| net__callbacks__valid_acc__name | valid_acc |
| net__callbacks__valid_acc__target_extractor | <function to_numpy at 0x7f9945e46a70> |
| net__callbacks__valid_acc__use_caching | True |
| net__callbacks__LRScheduler | <skorch.callbacks.lr_scheduler.LRScheduler object at 0x7f9945da85d0> |
| net__callbacks__LRScheduler__policy | <class 'torch.optim.lr_scheduler.LambdaLR'> |
| net__callbacks__LRScheduler__monitor | train_loss |
| net__callbacks__LRScheduler__event_name | event_lr |
| net__callbacks__LRScheduler__step_every | batch |
| net__callbacks__LRScheduler__lr_lambda | <function lr_schedule at 0x7f9945d9c440> |
| net__callbacks__ProgressBar | <skorch.callbacks.logging.ProgressBar object at 0x7f9945da8250> |
| net__callbacks__ProgressBar__batches_per_epoch | auto |
| net__callbacks__ProgressBar__detect_notebook | True |
| net__callbacks__ProgressBar__postfix_keys | ['train_loss', 'valid_loss'] |
| net__callbacks__print_log | <skorch.callbacks.logging.PrintLog object at 0x7f993cb30dd0> |
| net__callbacks__print_log__keys_ignored | |
| net__callbacks__print_log__sink | |
| net__callbacks__print_log__tablefmt | simple |
| net__callbacks__print_log__floatfmt | .4f |
| net__callbacks__print_log__stralign | right |
The model plot is below.
You can find the details about evaluation process and the evaluation results.
Use the code below to get started with the model.
