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Task Adapters

Pre-trained model:

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All architectures
bert xlm-roberta distilbert roberta

SST-2

The Stanford Sentiment Treebank is a binary single-sentence classification task consisting of sentences extracted from movie reviews with human annotations of their sentiment.
sentiment/sst-2@ukp distilbert-base-uncased
1 version Architecture: houlsby Head: 

Adapter for distilbert-base-uncased in Houlsby architecture trained on the SST-2 dataset for 15 epochs with early stopping and a learning rate of 1e-4.

sentiment/sst-2@ukp distilbert-base-uncased
1 version Architecture: pfeiffer Head: 

Adapter for distilbert-base-uncased in Pfeiffer architecture trained on the SST-2 dataset for 15 epochs with early stopping and a learning rate of 1e-4.

sentiment/sst-2@ukp bert-base-uncased
1 version Architecture: houlsby Head: 

Adapter in Houlsby architecture trained on the binary SST task for 20 epochs with early stopping and a learning rate of 1e-4. See https://arxiv.org/pdf/2007.07779.pdf.

sentiment/sst-2@ukp roberta-base
1 version Architecture: pfeiffer Head: 

Pfeiffer Adapter trained on the SST-2 task.

sentiment/sst-2@ukp roberta-large
1 version Architecture: pfeiffer Head: 

Adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs).

sentiment/sst-2@ukp roberta-base
1 version Architecture: houlsby Head: 

Adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs).

sentiment/sst-2@ukp bert-base-uncased
1 version Architecture: pfeiffer Head: 

Adapter in Pfeiffer architecture trained on the binary SST task for 20 epochs with early stopping and a learning rate of 1e-4. See https://arxiv.org/pdf/2007.07779.pdf.

sentiment/sst-2@ukp roberta-large
1 version Architecture: houlsby Head: 

Adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs).

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