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---
library_name: sklearn
tags:
- sklearn
- skops
- text-classification
---
# Model description
This is a neural net classifier and distilbert model chained with sklearn Pipeline trained on 20 news groups dataset.
## Intended uses & limitations
This model is trained for a tutorial and is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| 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 | <built-in function print> |
| net__callbacks__print_log__tablefmt | simple |
| net__callbacks__print_log__floatfmt | .4f |
| net__callbacks__print_log__stralign | right |
</details>
### Model Plot
The model plot is below.
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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))),
))])</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))),
))])</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))),
)</pre></div></div></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|---------|
| accuracy | 0.90562 |
| f1 score | 0.90562 |
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
[More Information Needed]
```
</details>
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
# Additional Content
## Confusion matrix
## Classification Report
<details>
<summary> Click to expand </summary>
| index | precision | recall | f1-score | support |
|--------------------------|-------------|----------|------------|-----------|
| alt.atheism | 0.927273 | 0.85 | 0.886957 | 120 |
| comp.graphics | 0.85906 | 0.876712 | 0.867797 | 146 |
| comp.os.ms-windows.misc | 0.893617 | 0.851351 | 0.871972 | 148 |
| comp.sys.ibm.pc.hardware | 0.666667 | 0.837838 | 0.742515 | 148 |
| comp.sys.mac.hardware | 0.901515 | 0.826389 | 0.862319 | 144 |
| comp.windows.x | 0.923077 | 0.891892 | 0.907216 | 148 |
| misc.forsale | 0.875862 | 0.869863 | 0.872852 | 146 |
| rec.autos | 0.893082 | 0.95302 | 0.922078 | 149 |
| rec.motorcycles | 0.937931 | 0.906667 | 0.922034 | 150 |
| rec.sport.baseball | 0.954248 | 0.979866 | 0.966887 | 149 |
| rec.sport.hockey | 0.979866 | 0.973333 | 0.976589 | 150 |
| sci.crypt | 0.993103 | 0.966443 | 0.979592 | 149 |
| sci.electronics | 0.869565 | 0.810811 | 0.839161 | 148 |
| sci.med | 0.973154 | 0.973154 | 0.973154 | 149 |
| sci.space | 0.973333 | 0.986486 | 0.979866 | 148 |
| soc.religion.christian | 0.927152 | 0.933333 | 0.930233 | 150 |
| talk.politics.guns | 0.961538 | 0.919118 | 0.93985 | 136 |
| talk.politics.mideast | 0.978571 | 0.971631 | 0.975089 | 141 |
| talk.politics.misc | 0.925234 | 0.853448 | 0.887892 | 116 |
| talk.religion.misc | 0.728972 | 0.829787 | 0.776119 | 94 |
| macro avg | 0.907141 | 0.903057 | 0.904009 | 2829 |
| weighted avg | 0.909947 | 0.90562 | 0.906742 | 2829 |
</details> |