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AdapterHub/bert-base-uncased-pf-stsb | https://huggingface.co/AdapterHub/bert-base-uncased-pf-stsb | An adapter for the bert-base-uncased model that was trained on the sts/sts-b dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-stsb
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-stsb
### Model Description : An adapter for the bert-base-uncased model that was trained on the sts/sts-b dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-swag | https://huggingface.co/AdapterHub/bert-base-uncased-pf-swag | An adapter for the bert-base-uncased model that was trained on the swag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-swag
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-swag
### Model Description : An adapter for the bert-base-uncased model that was trained on the swag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-trec | https://huggingface.co/AdapterHub/bert-base-uncased-pf-trec | An adapter for the bert-base-uncased model that was trained on the trec dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-trec
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-trec
### Model Description : An adapter for the bert-base-uncased model that was trained on the trec dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-ud_deprel | https://huggingface.co/AdapterHub/bert-base-uncased-pf-ud_deprel | An adapter for the bert-base-uncased model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-ud_deprel
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-ud_deprel
### Model Description : An adapter for the bert-base-uncased model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-ud_en_ewt | https://huggingface.co/AdapterHub/bert-base-uncased-pf-ud_en_ewt | An adapter for the bert-base-uncased model that was trained on the dp/ud_ewt dataset and includes a prediction head for dependency parsing. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: This adapter was trained using adapter-transformer's example script for dependency parsing.
See https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/dependency-parsing. Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-ud_en_ewt
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-ud_en_ewt
### Model Description : An adapter for the bert-base-uncased model that was trained on the dp/ud_ewt dataset and includes a prediction head for dependency parsing. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: This adapter was trained using adapter-transformer's example script for dependency parsing.
See https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/dependency-parsing. Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: |
AdapterHub/bert-base-uncased-pf-ud_pos | https://huggingface.co/AdapterHub/bert-base-uncased-pf-ud_pos | An adapter for the bert-base-uncased model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-ud_pos
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-ud_pos
### Model Description : An adapter for the bert-base-uncased model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-wic | https://huggingface.co/AdapterHub/bert-base-uncased-pf-wic | An adapter for the bert-base-uncased model that was trained on the wordsence/wic dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-wic
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-wic
### Model Description : An adapter for the bert-base-uncased model that was trained on the wordsence/wic dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-wikihop | https://huggingface.co/AdapterHub/bert-base-uncased-pf-wikihop | An adapter for the bert-base-uncased model that was trained on the qa/wikihop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-wikihop
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-wikihop
### Model Description : An adapter for the bert-base-uncased model that was trained on the qa/wikihop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-winogrande | https://huggingface.co/AdapterHub/bert-base-uncased-pf-winogrande | An adapter for the bert-base-uncased model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-winogrande
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-winogrande
### Model Description : An adapter for the bert-base-uncased model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-wnut_17 | https://huggingface.co/AdapterHub/bert-base-uncased-pf-wnut_17 | An adapter for the bert-base-uncased model that was trained on the wnut_17 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-wnut_17
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-wnut_17
### Model Description : An adapter for the bert-base-uncased model that was trained on the wnut_17 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bert-base-uncased-pf-yelp_polarity | https://huggingface.co/AdapterHub/bert-base-uncased-pf-yelp_polarity | An adapter for the bert-base-uncased model that was trained on the yelp_polarity dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bert-base-uncased-pf-yelp_polarity
### Model URL : https://huggingface.co/AdapterHub/bert-base-uncased-pf-yelp_polarity
### Model Description : An adapter for the bert-base-uncased model that was trained on the yelp_polarity dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/bioASQyesno | https://huggingface.co/AdapterHub/bioASQyesno | An adapter for the facebook/bart-base model that was trained on the qa/bioasq dataset. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: Trained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset. Achieved 75% accuracy on the test dataset of bioASQ 8b dataset. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/bioASQyesno
### Model URL : https://huggingface.co/AdapterHub/bioASQyesno
### Model Description : An adapter for the facebook/bart-base model that was trained on the qa/bioasq dataset. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: Trained for 15 epochs with early stopping, a learning rate of 1e-4, and a batch size of 4 on the yes-no questions of the bioASQ 8b dataset. Achieved 75% accuracy on the test dataset of bioASQ 8b dataset. |
AdapterHub/narrativeqa | https://huggingface.co/AdapterHub/narrativeqa | An adapter for the facebook/bart-base model that was trained on the qa/narrativeqa dataset. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/narrativeqa
### Model URL : https://huggingface.co/AdapterHub/narrativeqa
### Model Description : An adapter for the facebook/bart-base model that was trained on the qa/narrativeqa dataset. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: |
AdapterHub/roberta-base-pf-anli_r3 | https://huggingface.co/AdapterHub/roberta-base-pf-anli_r3 | An adapter for the roberta-base model that was trained on the anli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-anli_r3
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-anli_r3
### Model Description : An adapter for the roberta-base model that was trained on the anli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-art | https://huggingface.co/AdapterHub/roberta-base-pf-art | An adapter for the roberta-base model that was trained on the art dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-art
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-art
### Model Description : An adapter for the roberta-base model that was trained on the art dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-boolq | https://huggingface.co/AdapterHub/roberta-base-pf-boolq | An adapter for the roberta-base model that was trained on the qa/boolq dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-boolq
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-boolq
### Model Description : An adapter for the roberta-base model that was trained on the qa/boolq dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-cola | https://huggingface.co/AdapterHub/roberta-base-pf-cola | An adapter for the roberta-base model that was trained on the lingaccept/cola dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-cola
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-cola
### Model Description : An adapter for the roberta-base model that was trained on the lingaccept/cola dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-commonsense_qa | https://huggingface.co/AdapterHub/roberta-base-pf-commonsense_qa | An adapter for the roberta-base model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-commonsense_qa
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-commonsense_qa
### Model Description : An adapter for the roberta-base model that was trained on the comsense/csqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-comqa | https://huggingface.co/AdapterHub/roberta-base-pf-comqa | An adapter for the roberta-base model that was trained on the com_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-comqa
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-comqa
### Model Description : An adapter for the roberta-base model that was trained on the com_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-conll2000 | https://huggingface.co/AdapterHub/roberta-base-pf-conll2000 | An adapter for the roberta-base model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-conll2000
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-conll2000
### Model Description : An adapter for the roberta-base model that was trained on the chunk/conll2000 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-conll2003 | https://huggingface.co/AdapterHub/roberta-base-pf-conll2003 | An adapter for the roberta-base model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-conll2003
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-conll2003
### Model Description : An adapter for the roberta-base model that was trained on the ner/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-conll2003_pos | https://huggingface.co/AdapterHub/roberta-base-pf-conll2003_pos | An adapter for the roberta-base model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-conll2003_pos
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-conll2003_pos
### Model Description : An adapter for the roberta-base model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-copa | https://huggingface.co/AdapterHub/roberta-base-pf-copa | An adapter for the roberta-base model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-copa
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-copa
### Model Description : An adapter for the roberta-base model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-cosmos_qa | https://huggingface.co/AdapterHub/roberta-base-pf-cosmos_qa | An adapter for the roberta-base model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-cosmos_qa
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-cosmos_qa
### Model Description : An adapter for the roberta-base model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-cq | https://huggingface.co/AdapterHub/roberta-base-pf-cq | An adapter for the roberta-base model that was trained on the qa/cq dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-cq
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-cq
### Model Description : An adapter for the roberta-base model that was trained on the qa/cq dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-drop | https://huggingface.co/AdapterHub/roberta-base-pf-drop | An adapter for the roberta-base model that was trained on the drop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-drop
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-drop
### Model Description : An adapter for the roberta-base model that was trained on the drop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-duorc_p | https://huggingface.co/AdapterHub/roberta-base-pf-duorc_p | An adapter for the roberta-base model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-duorc_p
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-duorc_p
### Model Description : An adapter for the roberta-base model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-duorc_s | https://huggingface.co/AdapterHub/roberta-base-pf-duorc_s | An adapter for the roberta-base model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-duorc_s
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-duorc_s
### Model Description : An adapter for the roberta-base model that was trained on the duorc dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-emo | https://huggingface.co/AdapterHub/roberta-base-pf-emo | An adapter for the roberta-base model that was trained on the emo dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-emo
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-emo
### Model Description : An adapter for the roberta-base model that was trained on the emo dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-emotion | https://huggingface.co/AdapterHub/roberta-base-pf-emotion | An adapter for the roberta-base model that was trained on the emotion dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-emotion
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-emotion
### Model Description : An adapter for the roberta-base model that was trained on the emotion dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-fce_error_detection | https://huggingface.co/AdapterHub/roberta-base-pf-fce_error_detection | An adapter for the roberta-base model that was trained on the ged/fce dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-fce_error_detection
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-fce_error_detection
### Model Description : An adapter for the roberta-base model that was trained on the ged/fce dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-hellaswag | https://huggingface.co/AdapterHub/roberta-base-pf-hellaswag | An adapter for the roberta-base model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-hellaswag
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-hellaswag
### Model Description : An adapter for the roberta-base model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-hotpotqa | https://huggingface.co/AdapterHub/roberta-base-pf-hotpotqa | An adapter for the roberta-base model that was trained on the hotpot_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-hotpotqa
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-hotpotqa
### Model Description : An adapter for the roberta-base model that was trained on the hotpot_qa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-imdb | https://huggingface.co/AdapterHub/roberta-base-pf-imdb | An adapter for the roberta-base model that was trained on the sentiment/imdb dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-imdb
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-imdb
### Model Description : An adapter for the roberta-base model that was trained on the sentiment/imdb dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-mit_movie_trivia | https://huggingface.co/AdapterHub/roberta-base-pf-mit_movie_trivia | An adapter for the roberta-base model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-mit_movie_trivia
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-mit_movie_trivia
### Model Description : An adapter for the roberta-base model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-mnli | https://huggingface.co/AdapterHub/roberta-base-pf-mnli | An adapter for the roberta-base model that was trained on the nli/multinli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-mnli
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-mnli
### Model Description : An adapter for the roberta-base model that was trained on the nli/multinli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-mrpc | https://huggingface.co/AdapterHub/roberta-base-pf-mrpc | An adapter for the roberta-base model that was trained on the sts/mrpc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-mrpc
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-mrpc
### Model Description : An adapter for the roberta-base model that was trained on the sts/mrpc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-multirc | https://huggingface.co/AdapterHub/roberta-base-pf-multirc | An adapter for the roberta-base model that was trained on the rc/multirc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-multirc
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-multirc
### Model Description : An adapter for the roberta-base model that was trained on the rc/multirc dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-newsqa | https://huggingface.co/AdapterHub/roberta-base-pf-newsqa | An adapter for the roberta-base model that was trained on the newsqa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-newsqa
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-newsqa
### Model Description : An adapter for the roberta-base model that was trained on the newsqa dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-pmb_sem_tagging | https://huggingface.co/AdapterHub/roberta-base-pf-pmb_sem_tagging | An adapter for the roberta-base model that was trained on the semtag/pmb dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-pmb_sem_tagging
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-pmb_sem_tagging
### Model Description : An adapter for the roberta-base model that was trained on the semtag/pmb dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-qnli | https://huggingface.co/AdapterHub/roberta-base-pf-qnli | An adapter for the roberta-base model that was trained on the nli/qnli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-qnli
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-qnli
### Model Description : An adapter for the roberta-base model that was trained on the nli/qnli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-qqp | https://huggingface.co/AdapterHub/roberta-base-pf-qqp | An adapter for the roberta-base model that was trained on the sts/qqp dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-qqp
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-qqp
### Model Description : An adapter for the roberta-base model that was trained on the sts/qqp dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-quail | https://huggingface.co/AdapterHub/roberta-base-pf-quail | An adapter for the roberta-base model that was trained on the quail dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-quail
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-quail
### Model Description : An adapter for the roberta-base model that was trained on the quail dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-quartz | https://huggingface.co/AdapterHub/roberta-base-pf-quartz | An adapter for the roberta-base model that was trained on the quartz dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-quartz
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-quartz
### Model Description : An adapter for the roberta-base model that was trained on the quartz dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-quoref | https://huggingface.co/AdapterHub/roberta-base-pf-quoref | An adapter for the roberta-base model that was trained on the quoref dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-quoref
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-quoref
### Model Description : An adapter for the roberta-base model that was trained on the quoref dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-race | https://huggingface.co/AdapterHub/roberta-base-pf-race | An adapter for the roberta-base model that was trained on the rc/race dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-race
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-race
### Model Description : An adapter for the roberta-base model that was trained on the rc/race dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-record | https://huggingface.co/AdapterHub/roberta-base-pf-record | An adapter for the roberta-base model that was trained on the rc/record dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-record
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-record
### Model Description : An adapter for the roberta-base model that was trained on the rc/record dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-rotten_tomatoes | https://huggingface.co/AdapterHub/roberta-base-pf-rotten_tomatoes | An adapter for the roberta-base model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-rotten_tomatoes
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-rotten_tomatoes
### Model Description : An adapter for the roberta-base model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-rte | https://huggingface.co/AdapterHub/roberta-base-pf-rte | An adapter for the roberta-base model that was trained on the nli/rte dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-rte
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-rte
### Model Description : An adapter for the roberta-base model that was trained on the nli/rte dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-scicite | https://huggingface.co/AdapterHub/roberta-base-pf-scicite | An adapter for the roberta-base model that was trained on the scicite dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-scicite
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-scicite
### Model Description : An adapter for the roberta-base model that was trained on the scicite dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-scitail | https://huggingface.co/AdapterHub/roberta-base-pf-scitail | An adapter for the roberta-base model that was trained on the nli/scitail dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-scitail
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-scitail
### Model Description : An adapter for the roberta-base model that was trained on the nli/scitail dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-sick | https://huggingface.co/AdapterHub/roberta-base-pf-sick | An adapter for the roberta-base model that was trained on the nli/sick dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-sick
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-sick
### Model Description : An adapter for the roberta-base model that was trained on the nli/sick dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-snli | https://huggingface.co/AdapterHub/roberta-base-pf-snli | An adapter for the roberta-base model that was trained on the snli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-snli
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-snli
### Model Description : An adapter for the roberta-base model that was trained on the snli dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-social_i_qa | https://huggingface.co/AdapterHub/roberta-base-pf-social_i_qa | An adapter for the roberta-base model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-social_i_qa
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-social_i_qa
### Model Description : An adapter for the roberta-base model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-squad | https://huggingface.co/AdapterHub/roberta-base-pf-squad | An adapter for the roberta-base model that was trained on the qa/squad1 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-squad
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-squad
### Model Description : An adapter for the roberta-base model that was trained on the qa/squad1 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-squad_v2 | https://huggingface.co/AdapterHub/roberta-base-pf-squad_v2 | An adapter for the roberta-base model that was trained on the qa/squad2 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-squad_v2
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-squad_v2
### Model Description : An adapter for the roberta-base model that was trained on the qa/squad2 dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-sst2 | https://huggingface.co/AdapterHub/roberta-base-pf-sst2 | An adapter for the roberta-base model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-sst2
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-sst2
### Model Description : An adapter for the roberta-base model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-stsb | https://huggingface.co/AdapterHub/roberta-base-pf-stsb | An adapter for the roberta-base model that was trained on the sts/sts-b dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-stsb
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-stsb
### Model Description : An adapter for the roberta-base model that was trained on the sts/sts-b dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-swag | https://huggingface.co/AdapterHub/roberta-base-pf-swag | An adapter for the roberta-base model that was trained on the swag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-swag
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-swag
### Model Description : An adapter for the roberta-base model that was trained on the swag dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-trec | https://huggingface.co/AdapterHub/roberta-base-pf-trec | An adapter for the roberta-base model that was trained on the trec dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-trec
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-trec
### Model Description : An adapter for the roberta-base model that was trained on the trec dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-ud_deprel | https://huggingface.co/AdapterHub/roberta-base-pf-ud_deprel | An adapter for the roberta-base model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-ud_deprel
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-ud_deprel
### Model Description : An adapter for the roberta-base model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-ud_en_ewt | https://huggingface.co/AdapterHub/roberta-base-pf-ud_en_ewt | An adapter for the roberta-base model that was trained on the dp/ud_ewt dataset and includes a prediction head for dependency parsing. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: This adapter was trained using adapter-transformer's example script for dependency parsing.
See https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/dependency-parsing. Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-ud_en_ewt
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-ud_en_ewt
### Model Description : An adapter for the roberta-base model that was trained on the dp/ud_ewt dataset and includes a prediction head for dependency parsing. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: This adapter was trained using adapter-transformer's example script for dependency parsing.
See https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/dependency-parsing. Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: |
AdapterHub/roberta-base-pf-ud_pos | https://huggingface.co/AdapterHub/roberta-base-pf-ud_pos | An adapter for the roberta-base model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-ud_pos
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-ud_pos
### Model Description : An adapter for the roberta-base model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-wic | https://huggingface.co/AdapterHub/roberta-base-pf-wic | An adapter for the roberta-base model that was trained on the wordsence/wic dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-wic
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-wic
### Model Description : An adapter for the roberta-base model that was trained on the wordsence/wic dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-wikihop | https://huggingface.co/AdapterHub/roberta-base-pf-wikihop | An adapter for the roberta-base model that was trained on the qa/wikihop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-wikihop
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-wikihop
### Model Description : An adapter for the roberta-base model that was trained on the qa/wikihop dataset and includes a prediction head for question answering. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-winogrande | https://huggingface.co/AdapterHub/roberta-base-pf-winogrande | An adapter for the roberta-base model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-winogrande
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-winogrande
### Model Description : An adapter for the roberta-base model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-wnut_17 | https://huggingface.co/AdapterHub/roberta-base-pf-wnut_17 | An adapter for the roberta-base model that was trained on the wnut_17 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-wnut_17
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-wnut_17
### Model Description : An adapter for the roberta-base model that was trained on the wnut_17 dataset and includes a prediction head for tagging. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
AdapterHub/roberta-base-pf-yelp_polarity | https://huggingface.co/AdapterHub/roberta-base-pf-yelp_polarity | An adapter for the roberta-base model that was trained on the yelp_polarity dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdapterHub/roberta-base-pf-yelp_polarity
### Model URL : https://huggingface.co/AdapterHub/roberta-base-pf-yelp_polarity
### Model Description : An adapter for the roberta-base model that was trained on the yelp_polarity dataset and includes a prediction head for classification. This adapter was created for usage with the adapter-transformers library. First, install adapter-transformers: Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More Now, the adapter can be loaded and activated like this: The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found here. Refer to the paper for more information on results. If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection": |
Adarsh123/distilbert-base-uncased-finetuned-ner | https://huggingface.co/Adarsh123/distilbert-base-uncased-finetuned-ner | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adarsh123/distilbert-base-uncased-finetuned-ner
### Model URL : https://huggingface.co/Adarsh123/distilbert-base-uncased-finetuned-ner
### Model Description : No model card New: Create and edit this model card directly on the website! |
Addixz/Sanyx | https://huggingface.co/Addixz/Sanyx | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Addixz/Sanyx
### Model URL : https://huggingface.co/Addixz/Sanyx
### Model Description : No model card New: Create and edit this model card directly on the website! |
Adharsh2608/DialoGPT-small-harrypotter | https://huggingface.co/Adharsh2608/DialoGPT-small-harrypotter | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adharsh2608/DialoGPT-small-harrypotter
### Model URL : https://huggingface.co/Adharsh2608/DialoGPT-small-harrypotter
### Model Description : No model card New: Create and edit this model card directly on the website! |
AdharshJolly/HarryPotterBot-Model | https://huggingface.co/AdharshJolly/HarryPotterBot-Model | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdharshJolly/HarryPotterBot-Model
### Model URL : https://huggingface.co/AdharshJolly/HarryPotterBot-Model
### Model Description : |
Adi2K/Priv-Consent | https://huggingface.co/Adi2K/Priv-Consent | You can use cURL to access this model: Or Python API: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adi2K/Priv-Consent
### Model URL : https://huggingface.co/Adi2K/Priv-Consent
### Model Description : You can use cURL to access this model: Or Python API: |
AdiShenoy0807/DialoGPT-medium-joshua | https://huggingface.co/AdiShenoy0807/DialoGPT-medium-joshua | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdiShenoy0807/DialoGPT-medium-joshua
### Model URL : https://huggingface.co/AdiShenoy0807/DialoGPT-medium-joshua
### Model Description : No model card New: Create and edit this model card directly on the website! |
Adielcane/Adiel | https://huggingface.co/Adielcane/Adiel | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adielcane/Adiel
### Model URL : https://huggingface.co/Adielcane/Adiel
### Model Description : No model card New: Create and edit this model card directly on the website! |
Adielcane/Adielcane | https://huggingface.co/Adielcane/Adielcane | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adielcane/Adielcane
### Model URL : https://huggingface.co/Adielcane/Adielcane
### Model Description : No model card New: Create and edit this model card directly on the website! |
Adil617/wav2vec2-base-timit-demo-colab | https://huggingface.co/Adil617/wav2vec2-base-timit-demo-colab | This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adil617/wav2vec2-base-timit-demo-colab
### Model URL : https://huggingface.co/Adil617/wav2vec2-base-timit-demo-colab
### Model Description : This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set: More information needed More information needed More information needed The following hyperparameters were used during training: |
Adinda/Adinda | https://huggingface.co/Adinda/Adinda | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adinda/Adinda
### Model URL : https://huggingface.co/Adinda/Adinda
### Model Description : |
Adityanawal/testmodel_1 | https://huggingface.co/Adityanawal/testmodel_1 | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adityanawal/testmodel_1
### Model URL : https://huggingface.co/Adityanawal/testmodel_1
### Model Description : No model card New: Create and edit this model card directly on the website! |
Adnan/UrduNewsHeadlines | https://huggingface.co/Adnan/UrduNewsHeadlines | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adnan/UrduNewsHeadlines
### Model URL : https://huggingface.co/Adnan/UrduNewsHeadlines
### Model Description : No model card New: Create and edit this model card directly on the website! |
AdrianGzz/DialoGPT-small-harrypotter | https://huggingface.co/AdrianGzz/DialoGPT-small-harrypotter | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AdrianGzz/DialoGPT-small-harrypotter
### Model URL : https://huggingface.co/AdrianGzz/DialoGPT-small-harrypotter
### Model Description : |
Adrianaforididk/Jinx | https://huggingface.co/Adrianaforididk/Jinx | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Adrianaforididk/Jinx
### Model URL : https://huggingface.co/Adrianaforididk/Jinx
### Model Description : No model card New: Create and edit this model card directly on the website! |
Advertisement/FischlUWU | https://huggingface.co/Advertisement/FischlUWU | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Advertisement/FischlUWU
### Model URL : https://huggingface.co/Advertisement/FischlUWU
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aero/Tsubomi-Haruno | https://huggingface.co/Aero/Tsubomi-Haruno | null | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aero/Tsubomi-Haruno
### Model URL : https://huggingface.co/Aero/Tsubomi-Haruno
### Model Description : |
Aeroxas/Botroxas-small | https://huggingface.co/Aeroxas/Botroxas-small | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aeroxas/Botroxas-small
### Model URL : https://huggingface.co/Aeroxas/Botroxas-small
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aeskybunnie/Me | https://huggingface.co/Aeskybunnie/Me | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aeskybunnie/Me
### Model URL : https://huggingface.co/Aeskybunnie/Me
### Model Description : No model card New: Create and edit this model card directly on the website! |
AetherIT/DialoGPT-small-Hal | https://huggingface.co/AetherIT/DialoGPT-small-Hal | #HAL | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AetherIT/DialoGPT-small-Hal
### Model URL : https://huggingface.co/AetherIT/DialoGPT-small-Hal
### Model Description : #HAL |
AethiQs-Max/AethiQs_GemBERT_bertje_50k | https://huggingface.co/AethiQs-Max/AethiQs_GemBERT_bertje_50k | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AethiQs-Max/AethiQs_GemBERT_bertje_50k
### Model URL : https://huggingface.co/AethiQs-Max/AethiQs_GemBERT_bertje_50k
### Model Description : No model card New: Create and edit this model card directly on the website! |
AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10 | https://huggingface.co/AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10 | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10
### Model URL : https://huggingface.co/AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10
### Model Description : No model card New: Create and edit this model card directly on the website! |
AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_30-epoch_30 | https://huggingface.co/AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_30-epoch_30 | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_30-epoch_30
### Model URL : https://huggingface.co/AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_30-epoch_30
### Model Description : No model card New: Create and edit this model card directly on the website! |
AethiQs-Max/cross_encoder | https://huggingface.co/AethiQs-Max/cross_encoder | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AethiQs-Max/cross_encoder
### Model URL : https://huggingface.co/AethiQs-Max/cross_encoder
### Model Description : No model card New: Create and edit this model card directly on the website! |
AethiQs-Max/s3-v1-20_epochs | https://huggingface.co/AethiQs-Max/s3-v1-20_epochs | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AethiQs-Max/s3-v1-20_epochs
### Model URL : https://huggingface.co/AethiQs-Max/s3-v1-20_epochs
### Model Description : No model card New: Create and edit this model card directly on the website! |
Aftabhussain/Tomato_Leaf_Classifier | https://huggingface.co/Aftabhussain/Tomato_Leaf_Classifier | Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo. | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Aftabhussain/Tomato_Leaf_Classifier
### Model URL : https://huggingface.co/Aftabhussain/Tomato_Leaf_Classifier
### Model Description : Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo. |
Ahda/M | https://huggingface.co/Ahda/M | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahda/M
### Model URL : https://huggingface.co/Ahda/M
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ahmad/parsT5-base | https://huggingface.co/Ahmad/parsT5-base | A monolingual T5 model for Persian trained on OSCAR 21.09 (https://oscar-corpus.com/) corpus with self-supervised method. 35 Gig deduplicated version of Persian data was used for pre-training the model. It's similar to the English T5 model but just for Persian. You may need to fine-tune it on your specific task. Example code: Steps: 725000 Accuracy: 0.66 To train the model further please refer to its github repository at: https://github.com/puraminy/parsT5 | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahmad/parsT5-base
### Model URL : https://huggingface.co/Ahmad/parsT5-base
### Model Description : A monolingual T5 model for Persian trained on OSCAR 21.09 (https://oscar-corpus.com/) corpus with self-supervised method. 35 Gig deduplicated version of Persian data was used for pre-training the model. It's similar to the English T5 model but just for Persian. You may need to fine-tune it on your specific task. Example code: Steps: 725000 Accuracy: 0.66 To train the model further please refer to its github repository at: https://github.com/puraminy/parsT5 |
Ahmad/parsT5 | https://huggingface.co/Ahmad/parsT5 | A checkpoint for training Persian T5 model. This repository can be cloned and pre-training can be resumed. This model uses flax and is for training. For more information and getting the training code please refer to: https://github.com/puraminy/parsT5 | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahmad/parsT5
### Model URL : https://huggingface.co/Ahmad/parsT5
### Model Description : A checkpoint for training Persian T5 model. This repository can be cloned and pre-training can be resumed. This model uses flax and is for training. For more information and getting the training code please refer to: https://github.com/puraminy/parsT5 |
Ahmadatiya97/Alannah | https://huggingface.co/Ahmadatiya97/Alannah | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahmadatiya97/Alannah
### Model URL : https://huggingface.co/Ahmadatiya97/Alannah
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ahmadvakili/A | https://huggingface.co/Ahmadvakili/A | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahmadvakili/A
### Model URL : https://huggingface.co/Ahmadvakili/A
### Model Description : No model card New: Create and edit this model card directly on the website! |
Ahmed59/Demo-Team-5-SIAD | https://huggingface.co/Ahmed59/Demo-Team-5-SIAD | No model card New: Create and edit this model card directly on the website! | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : Ahmed59/Demo-Team-5-SIAD
### Model URL : https://huggingface.co/Ahmed59/Demo-Team-5-SIAD
### Model Description : No model card New: Create and edit this model card directly on the website! |
AhmedBou/TuniBert | https://huggingface.co/AhmedBou/TuniBert | This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks. LABEL_1: Positive LABEL_2: Negative LABEL_0: Neutral This work is an integral component of my Master's degree thesis and represents the culmination of extensive research and labor.
If you wish to utilize the Tunisian-Dialect-Corpus or the TuniBert model, kindly refer to the directory provided. [huggingface.co/AhmedBou][github.com/BoulahiaAhmed] | Indicators looking for configurations to recommend AI models for configuring AI agents
### Model Name : AhmedBou/TuniBert
### Model URL : https://huggingface.co/AhmedBou/TuniBert
### Model Description : This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks. LABEL_1: Positive LABEL_2: Negative LABEL_0: Neutral This work is an integral component of my Master's degree thesis and represents the culmination of extensive research and labor.
If you wish to utilize the Tunisian-Dialect-Corpus or the TuniBert model, kindly refer to the directory provided. [huggingface.co/AhmedBou][github.com/BoulahiaAhmed] |