Datasets:

ftopal

/

huggingface-models-processed

like 0

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-conll2003_pos` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [pos/conll2003](https://adapterhub.ml/explore/pos/conll2003/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-conll2003_pos", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:pos/conll2003", "adapter-transformers", "token-classification"], "datasets": ["conll2003"]}

AdapterHub/roberta-base-pf-conll2003_pos

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #token-classification #adapterhub-pos/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-conll2003_pos' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-copa` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/copa](https://adapterhub.ml/explore/comsense/copa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-copa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapterhub:comsense/copa", "adapter-transformers"]}

AdapterHub/roberta-base-pf-copa

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #adapterhub-comsense/copa #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-copa' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-cosmos_qa` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/cosmosqa](https://adapterhub.ml/explore/comsense/cosmosqa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-cosmos_qa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapterhub:comsense/cosmosqa", "adapter-transformers"], "datasets": ["cosmos_qa"]}

AdapterHub/roberta-base-pf-cosmos_qa

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #adapterhub-comsense/cosmosqa #en #dataset-cosmos_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-cosmos_qa' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-cq` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/cq](https://adapterhub.ml/explore/qa/cq/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-cq", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/cq", "adapter-transformers"]}

AdapterHub/roberta-base-pf-cq

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #adapterhub-qa/cq #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-cq' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-drop` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [drop](https://huggingface.co/datasets/drop/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-drop", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["drop"]}

AdapterHub/roberta-base-pf-drop

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #en #dataset-drop #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-drop' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-duorc_p` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-duorc_p", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["duorc"]}

AdapterHub/roberta-base-pf-duorc_p

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-duorc_p' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-duorc_s` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-duorc_s", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["duorc"]}

AdapterHub/roberta-base-pf-duorc_s

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-duorc_s' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-emo` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [emo](https://huggingface.co/datasets/emo/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-emo", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["emo"]}

AdapterHub/roberta-base-pf-emo

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #en #dataset-emo #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-emo' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-emotion` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [emotion](https://huggingface.co/datasets/emotion/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-emotion", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["emotion"]}

AdapterHub/roberta-base-pf-emotion

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #en #dataset-emotion #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-emotion' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-fce_error_detection` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [ged/fce](https://adapterhub.ml/explore/ged/fce/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-fce_error_detection", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:ged/fce", "adapter-transformers"], "datasets": ["fce_error_detection"]}

AdapterHub/roberta-base-pf-fce_error_detection

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #token-classification #adapterhub-ged/fce #en #dataset-fce_error_detection #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-fce_error_detection' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-hellaswag` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/hellaswag](https://adapterhub.ml/explore/comsense/hellaswag/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-hellaswag", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapterhub:comsense/hellaswag", "adapter-transformers"], "datasets": ["hellaswag"]}

AdapterHub/roberta-base-pf-hellaswag

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #adapterhub-comsense/hellaswag #en #dataset-hellaswag #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-hellaswag' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-hotpotqa` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [hotpot_qa](https://huggingface.co/datasets/hotpot_qa/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-hotpotqa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["hotpot_qa"]}

AdapterHub/roberta-base-pf-hotpotqa

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #en #dataset-hotpot_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-hotpotqa' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-imdb` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/imdb](https://adapterhub.ml/explore/sentiment/imdb/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-imdb", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sentiment/imdb", "adapter-transformers"], "datasets": ["imdb"]}

AdapterHub/roberta-base-pf-imdb

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-sentiment/imdb #en #dataset-imdb #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-imdb' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-mit_movie_trivia` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [ner/mit_movie_trivia](https://adapterhub.ml/explore/ner/mit_movie_trivia/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-mit_movie_trivia", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:ner/mit_movie_trivia", "adapter-transformers"]}

AdapterHub/roberta-base-pf-mit_movie_trivia

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #token-classification #adapterhub-ner/mit_movie_trivia #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-mit_movie_trivia' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-mnli` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-mnli", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/multinli", "adapter-transformers"], "datasets": ["multi_nli"]}

AdapterHub/roberta-base-pf-mnli

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-nli/multinli #en #dataset-multi_nli #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-mnli' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-mrpc` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/mrpc](https://adapterhub.ml/explore/sts/mrpc/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-mrpc", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sts/mrpc", "adapter-transformers"]}

AdapterHub/roberta-base-pf-mrpc

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-sts/mrpc #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-mrpc' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-multirc` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [rc/multirc](https://adapterhub.ml/explore/rc/multirc/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-multirc", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "adapterhub:rc/multirc", "roberta", "adapter-transformers"]}

AdapterHub/roberta-base-pf-multirc

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-rc/multirc #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-multirc' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-newsqa` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [newsqa](https://huggingface.co/datasets/newsqa/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-newsqa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["newsqa"]}

AdapterHub/roberta-base-pf-newsqa

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #en #dataset-newsqa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-newsqa' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-pmb_sem_tagging` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [semtag/pmb](https://adapterhub.ml/explore/semtag/pmb/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-pmb_sem_tagging", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:semtag/pmb", "adapter-transformers"]}

AdapterHub/roberta-base-pf-pmb_sem_tagging

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #token-classification #adapterhub-semtag/pmb #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-pmb_sem_tagging' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-qnli` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/qnli](https://adapterhub.ml/explore/nli/qnli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-qnli", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/qnli", "adapter-transformers"]}

AdapterHub/roberta-base-pf-qnli

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-nli/qnli #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-qnli' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-qqp` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/qqp](https://adapterhub.ml/explore/sts/qqp/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-qqp", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "adapter-transformers", "adapterhub:sts/qqp", "roberta"]}

AdapterHub/roberta-base-pf-qqp

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-sts/qqp #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-qqp' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-quail` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [quail](https://huggingface.co/datasets/quail/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-quail", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["quail"]}

AdapterHub/roberta-base-pf-quail

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #en #dataset-quail #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-quail' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-quartz` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [quartz](https://huggingface.co/datasets/quartz/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-quartz", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["quartz"]}

AdapterHub/roberta-base-pf-quartz

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #en #dataset-quartz #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-quartz' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-quoref` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [quoref](https://huggingface.co/datasets/quoref/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-quoref", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["quoref"]}

AdapterHub/roberta-base-pf-quoref

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #en #dataset-quoref #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-quoref' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-race` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [rc/race](https://adapterhub.ml/explore/rc/race/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-race", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["adapterhub:rc/race", "roberta", "adapter-transformers"], "datasets": ["race"]}

AdapterHub/roberta-base-pf-race

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #adapterhub-rc/race #en #dataset-race #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-race' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-record` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [rc/record](https://adapterhub.ml/explore/rc/record/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-record", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:rc/record", "adapter-transformers"]}

AdapterHub/roberta-base-pf-record

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-rc/record #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-record' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-rotten_tomatoes` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/rotten_tomatoes](https://adapterhub.ml/explore/sentiment/rotten_tomatoes/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-rotten_tomatoes", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sentiment/rotten_tomatoes", "adapter-transformers"], "datasets": ["rotten_tomatoes"]}

AdapterHub/roberta-base-pf-rotten_tomatoes

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-sentiment/rotten_tomatoes #en #dataset-rotten_tomatoes #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-rotten_tomatoes' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-rte` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/rte](https://adapterhub.ml/explore/nli/rte/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-rte", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/rte", "adapter-transformers"]}

AdapterHub/roberta-base-pf-rte

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-nli/rte #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-rte' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-scicite` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [scicite](https://huggingface.co/datasets/scicite/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-scicite", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["scicite"]}

AdapterHub/roberta-base-pf-scicite

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #en #dataset-scicite #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-scicite' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-scitail` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/scitail](https://adapterhub.ml/explore/nli/scitail/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-scitail", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/scitail", "adapter-transformers"], "datasets": ["scitail"]}

AdapterHub/roberta-base-pf-scitail

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-nli/scitail #en #dataset-scitail #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-scitail' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-sick` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/sick](https://adapterhub.ml/explore/nli/sick/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-sick", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers", "adapterhub:nli/sick", "text-classification"], "datasets": ["sick"]}

AdapterHub/roberta-base-pf-sick

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-nli/sick #en #dataset-sick #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-sick' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-snli` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [snli](https://huggingface.co/datasets/snli/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-snli", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["snli"]}

AdapterHub/roberta-base-pf-snli

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #en #dataset-snli #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-snli' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-social_i_qa` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [social_i_qa](https://huggingface.co/datasets/social_i_qa/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-social_i_qa", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["social_i_qa"]}

AdapterHub/roberta-base-pf-social_i_qa

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #en #dataset-social_i_qa #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-social_i_qa' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-squad` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/squad1](https://adapterhub.ml/explore/qa/squad1/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-squad", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/squad1", "adapter-transformers"], "datasets": ["squad"]}

AdapterHub/roberta-base-pf-squad

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #adapterhub-qa/squad1 #en #dataset-squad #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-squad' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-squad_v2` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/squad2](https://adapterhub.ml/explore/qa/squad2/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-squad_v2", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/squad2", "adapter-transformers"], "datasets": ["squad_v2"]}

AdapterHub/roberta-base-pf-squad_v2

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #adapterhub-qa/squad2 #en #dataset-squad_v2 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-squad_v2' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-sst2` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/sst-2](https://adapterhub.ml/explore/sentiment/sst-2/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-sst2", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sentiment/sst-2", "adapter-transformers"]}

AdapterHub/roberta-base-pf-sst2

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-sentiment/sst-2 #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-sst2' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-stsb` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/sts-b](https://adapterhub.ml/explore/sts/sts-b/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-stsb", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sts/sts-b", "adapter-transformers"]}

AdapterHub/roberta-base-pf-stsb

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-sts/sts-b #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-stsb' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-swag` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [swag](https://huggingface.co/datasets/swag/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-swag", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["swag"]}

AdapterHub/roberta-base-pf-swag

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #en #dataset-swag #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-swag' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-trec` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [trec](https://huggingface.co/datasets/trec/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-trec", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["trec"]}

AdapterHub/roberta-base-pf-trec

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #en #dataset-trec #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-trec' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-ud_deprel` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [deprel/ud_ewt](https://adapterhub.ml/explore/deprel/ud_ewt/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-ud_deprel", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:deprel/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]}

AdapterHub/roberta-base-pf-ud_deprel

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #token-classification #adapterhub-deprel/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-ud_deprel' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-ud_en_ewt` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [dp/ud_ewt](https://adapterhub.ml/explore/dp/ud_ewt/) dataset and includes a prediction head for dependency parsing. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-ud_en_ewt", source="hf", set_active=True) ``` ## Architecture & Training 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. ## Evaluation results Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: | Model | UAS | LAS | | --- | --- | --- | | `bert-base-uncased` | 91.74 | 89.15 | | `roberta-base` | 91.43 | 88.43 | ## Citation <!-- Add some description here -->

{"language": ["en"], "tags": ["roberta", "adapterhub:dp/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]}

AdapterHub/roberta-base-pf-ud_en_ewt

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #adapterhub-dp/ud_ewt #en #dataset-universal_dependencies #region-us

Adapter 'AdapterHub/roberta-base-pf-ud\_en\_ewt' for roberta-base ================================================================= 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. Usage ----- 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: Architecture & Training ----------------------- This adapter was trained using adapter-transformer's example script for dependency parsing. See URL Evaluation results ------------------ Scores achieved by dependency parsing adapters on the test set of UD English EWT after training: Model: 'bert-base-uncased', UAS: 91.74, LAS: 89.15 Model: 'roberta-base', UAS: 91.43, LAS: 88.43

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-ud_pos` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [pos/ud_ewt](https://adapterhub.ml/explore/pos/ud_ewt/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-ud_pos", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:pos/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]}

AdapterHub/roberta-base-pf-ud_pos

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #token-classification #adapterhub-pos/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-ud_pos' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-wic` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [wordsence/wic](https://adapterhub.ml/explore/wordsence/wic/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-wic", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:wordsence/wic", "adapter-transformers"]}

AdapterHub/roberta-base-pf-wic

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #adapterhub-wordsence/wic #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-wic' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

question-answering

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-wikihop` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/wikihop](https://adapterhub.ml/explore/qa/wikihop/) dataset and includes a prediction head for question answering. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-wikihop", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/wikihop", "adapter-transformers"]}

AdapterHub/roberta-base-pf-wikihop

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #question-answering #adapterhub-qa/wikihop #en #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-wikihop' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-winogrande` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/winogrande](https://adapterhub.ml/explore/comsense/winogrande/) dataset and includes a prediction head for multiple choice. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-winogrande", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-what-to-pre-train-on, title={What to Pre-Train on? Efficient Intermediate Task Selection}, author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2104.08247", pages = "to appear", } ```

{"language": ["en"], "tags": ["roberta", "adapterhub:comsense/winogrande", "adapter-transformers"], "datasets": ["winogrande"]}

AdapterHub/roberta-base-pf-winogrande

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #adapterhub-comsense/winogrande #en #dataset-winogrande #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-winogrande' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

token-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-wnut_17` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [wnut_17](https://huggingface.co/datasets/wnut_17/) dataset and includes a prediction head for tagging. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-wnut_17", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["token-classification", "roberta", "adapter-transformers"], "datasets": ["wnut_17"]}

AdapterHub/roberta-base-pf-wnut_17

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #token-classification #en #dataset-wnut_17 #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-wnut_17' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-classification

adapter-transformers

# Adapter `AdapterHub/roberta-base-pf-yelp_polarity` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [yelp_polarity](https://huggingface.co/datasets/yelp_polarity/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoModelWithHeads model = AutoModelWithHeads.from_pretrained("roberta-base") adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-yelp_polarity", source="hf") model.active_adapters = adapter_name ``` ## Architecture & Training 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). ## Evaluation results Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. ## Citation If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): ```bibtex @inproceedings{poth-etal-2021-pre, title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", author = {Poth, Clifton and Pfeiffer, Jonas and R{"u}ckl{'e}, Andreas and Gurevych, Iryna}, booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.827", pages = "10585--10605", } ```

{"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["yelp_polarity"]}

AdapterHub/roberta-base-pf-yelp_polarity

2022-03-02T23:29:04+00:00

TAGS #adapter-transformers #roberta #text-classification #en #dataset-yelp_polarity #arxiv-2104.08247 #region-us

# Adapter 'AdapterHub/roberta-base-pf-yelp_polarity' for roberta-base 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. ## Usage 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: ## Architecture & Training The training code for this adapter is available at URL In particular, training configurations for all tasks can be found here. ## Evaluation results 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":

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

AdharshJolly/HarryPotterBot-Model

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Harry Potter DialoGPT Model

text-classification

transformers

# Model - Problem type: Binary Classification - Model ID: 12592372 ## Validation Metrics - Loss: 0.23033875226974487 - Accuracy: 0.9138655462184874 - Precision: 0.9087136929460581 - Recall: 0.9201680672268907 - AUC: 0.9690346726926065 - F1: 0.9144050104384133 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Adi2K/autonlp-Priv-Consent-12592372 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Adi2K/autonlp-Priv-Consent-12592372", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Adi2K/autonlp-Priv-Consent-12592372", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```

{"language": "eng", "datasets": ["Adi2K/autonlp-data-Priv-Consent"], "widget": [{"text": "You can control cookies and tracking tools. To learn how to manage how we - and our vendors - use cookies and other tracking tools, please click here."}]}

Adi2K/Priv-Consent

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #eng #dataset-Adi2K/autonlp-data-Priv-Consent #autotrain_compatible #endpoints_compatible #region-us

# Model - Problem type: Binary Classification - Model ID: 12592372 ## Validation Metrics - Loss: 0.23033875226974487 - Accuracy: 0.9138655462184874 - Precision: 0.9087136929460581 - Recall: 0.9201680672268907 - AUC: 0.9690346726926065 - F1: 0.9144050104384133 ## Usage You can use cURL to access this model: Or Python API:

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9314 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 8.686 | 0.16 | 20 | 13.6565 | 1.0 | | 8.0711 | 0.32 | 40 | 12.5379 | 1.0 | | 6.9967 | 0.48 | 60 | 9.7215 | 1.0 | | 5.2368 | 0.64 | 80 | 5.8459 | 1.0 | | 3.4499 | 0.8 | 100 | 3.3413 | 1.0 | | 3.1261 | 0.96 | 120 | 3.2858 | 1.0 | | 3.0654 | 1.12 | 140 | 3.1945 | 1.0 | | 3.0421 | 1.28 | 160 | 3.1296 | 1.0 | | 3.0035 | 1.44 | 180 | 3.1172 | 1.0 | | 3.0067 | 1.6 | 200 | 3.1217 | 1.0 | | 2.9867 | 1.76 | 220 | 3.0715 | 1.0 | | 2.9653 | 1.92 | 240 | 3.0747 | 1.0 | | 2.9629 | 2.08 | 260 | 2.9984 | 1.0 | | 2.9462 | 2.24 | 280 | 2.9991 | 1.0 | | 2.9391 | 2.4 | 300 | 3.0391 | 1.0 | | 2.934 | 2.56 | 320 | 2.9682 | 1.0 | | 2.9193 | 2.72 | 340 | 2.9701 | 1.0 | | 2.8985 | 2.88 | 360 | 2.9314 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}

Adil617/wav2vec2-base-timit-demo-colab

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

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: * Loss: 2.9314 * Wer: 1.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu111 * Datasets 1.13.3 * Tokenizers 0.10.3

text-generation

transformers

# Harry Potter DialoGPT model

{"tags": ["conversational"]}

AdrianGzz/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Harry Potter DialoGPT model

text-generation

transformers

# DialoGPT Trained on the Speech of a Game Character ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("Tsubomi: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

{"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"}

Aero/Tsubomi-Haruno

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# DialoGPT Trained on the Speech of a Game Character

text-generation

#HAL

{"tags": ["conversational"]}

AetherIT/DialoGPT-small-Hal

2022-03-02T23:29:04+00:00

TAGS #conversational #region-us

#HAL

image-classification

transformers

# Tomato_Leaf_Classifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Bacterial_spot  #### Healthy 

{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}

Aftabhussain/Tomato_Leaf_Classifier

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us

# Tomato_Leaf_Classifier Autogenerated by HuggingPics️ Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo. ## Example Images #### Bacterial_spot !Bacterial_spot #### Healthy !Healthy

text2text-generation

transformers

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: ``` from transformers import T5ForConditionalGeneration,AutoTokenizer import torch model_name = "Ahmad/parsT5-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) input_ids = tokenizer.encode('دانش آموزان به <extra_id_0> میروند و <extra_id_1> میخوانند.', return_tensors='pt') with torch.no_grad(): hypotheses = model.generate(input_ids) for h in hypotheses: print(tokenizer.decode(h)) ``` Steps: 725000 Accuracy: 0.66 Training More? ======== To train the model further please refer to its github repository at: https://github.com/puraminy/parsT5

{}

Ahmad/parsT5-base

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

A monolingual T5 model for Persian trained on OSCAR 21.09 (URL 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 Training More? ======== To train the model further please refer to its github repository at: URL

text2text-generation

transformers

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

{}

Ahmad/parsT5

2022-03-02T23:29:04+00:00

TAGS #transformers #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

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: URL

text-classification

transformers

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]

{"language": ["ar"], "license": "apache-2.0", "tags": ["sentiment analysis", "classification", "arabic dialect", "tunisian dialect"]}

AhmedBou/TuniBert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #sentiment analysis #classification #arabic dialect #tunisian dialect #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

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. [URL

text2text-generation

transformers

``` ``` [](https://paperswithcode.com/sota/code-generation-on-conala?p=mariancg-a-code-generation-transformer-model) ``` ``` # MarianCG: a code generation transformer model inspired by machine translation This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset. MarianCG model and its implemetation with the code of training and the generated output is available at this repository: https://github.com/AhmedSSoliman/MarianCG-NL-to-Code CoNaLa Dataset for Code Generation is available at https://huggingface.co/datasets/AhmedSSoliman/CoNaLa This is the model is avialable on the huggingface hub https://huggingface.co/AhmedSSoliman/MarianCG-CoNaLa ```python # Model and Tokenizer from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # model_name = "AhmedSSoliman/MarianCG-NL-to-Code" model = AutoModelForSeq2SeqLM.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa") tokenizer = AutoTokenizer.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa") # Input (Natural Language) and Output (Python Code) NL_input = "create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]" output = model.generate(**tokenizer(NL_input, padding="max_length", truncation=True, max_length=512, return_tensors="pt")) output_code = tokenizer.decode(output[0], skip_special_tokens=True) ``` This model is available in spaces using gradio at: https://huggingface.co/spaces/AhmedSSoliman/MarianCG-CoNaLa --- Tasks: - Translation - Code Generation - Text2Text Generation - Text Generation --- # Citation We now have a [paper](https://doi.org/10.1186/s44147-022-00159-4) for this work and you can cite: ``` @article{soliman2022mariancg, title={MarianCG: a code generation transformer model inspired by machine translation}, author={Soliman, Ahmed S and Hadhoud, Mayada M and Shaheen, Samir I}, journal={Journal of Engineering and Applied Science}, volume={69}, number={1}, pages={1--23}, year={2022}, publisher={SpringerOpen} url={https://doi.org/10.1186/s44147-022-00159-4} } ```

{"widget": [{"text": "create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]"}, {"text": "check if all elements in list `mylist` are identical"}, {"text": "enable debug mode on flask application `app`"}, {"text": "getting the length of `my_tuple`"}, {"text": "find all files in directory \"/mydir\" with extension \".txt\""}]}

AhmedSSoliman/MarianCG-CoNaLa

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us

![PWC](URL # MarianCG: a code generation transformer model inspired by machine translation This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset. MarianCG model and its implemetation with the code of training and the generated output is available at this repository: URL CoNaLa Dataset for Code Generation is available at URL This is the model is avialable on the huggingface hub URL This model is available in spaces using gradio at: URL --- Tasks: - Translation - Code Generation - Text2Text Generation - Text Generation --- We now have a paper for this work and you can cite:

text-generation

transformers

# Back to the Future DialoGPT Model

{"tags": ["conversational"]}

AiPorter/DialoGPT-small-Back_to_the_future

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Back to the Future DialoGPT Model

text-generation

transformers

# Rick DialoGPT Model

{"tags": ["conversational"]}

Aibox/DialoGPT-small-rick

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Rick DialoGPT Model

Trained on Stephen King's top 50 books as .txt files.

{}

Aidan8756/stephenKingModel

2022-03-02T23:29:04+00:00

TAGS #region-us

Trained on Stephen King's top 50 books as .txt files.

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-bashkir-cv7_opt This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset. It achieves the following results on the evaluation set: - Training Loss: 0.268400 - Validation Loss: 0.088252 - WER without LM: 0.085588 - WER with LM: 0.04440795062008041 - CER with LM: 0.010491234992390509 ## Model description Trained with this [jupiter notebook](https://drive.google.com/file/d/1KohDXZtKBWXVPZYlsLtqfxJGBzKmTtSh/view?usp=sharing) ## Intended uses & limitations In order to reduce the number of characters, the following letters have been replaced or removed: - 'я' -> 'йа' - 'ю' -> 'йу' - 'ё' -> 'йо' - 'е' -> 'йэ' for first letter - 'е' -> 'э' for other cases - 'ъ' -> deleted - 'ь' -> deleted Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 50 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu113 - Datasets 1.18.2 - Tokenizers 0.10.3

{"language": ["ba"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bashkir-cv7_opt", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "ba"}, "metrics": [{"type": "wer", "value": 0.04440795062008041, "name": "Test WER"}, {"type": "cer", "value": 0.010491234992390509, "name": "Test CER"}]}]}]}

AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ba #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us

# wav2vec2-large-xls-r-300m-bashkir-cv7_opt This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset. It achieves the following results on the evaluation set: - Training Loss: 0.268400 - Validation Loss: 0.088252 - WER without LM: 0.085588 - WER with LM: 0.04440795062008041 - CER with LM: 0.010491234992390509 ## Model description Trained with this jupiter notebook ## Intended uses & limitations In order to reduce the number of characters, the following letters have been replaced or removed: - 'я' -> 'йа' - 'ю' -> 'йу' - 'ё' -> 'йо' - 'е' -> 'йэ' for first letter - 'е' -> 'э' for other cases - 'ъ' -> deleted - 'ь' -> deleted Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 50 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu113 - Datasets 1.18.2 - Tokenizers 0.10.3

text2text-generation

transformers

you can use this model with simpletransfomers. ``` !pip install simpletransformers from simpletransformers.t5 import T5Model model = T5Model("mt5", "AimB/mT5-en-kr-natural") print(model.predict(["I feel good today"])) print(model.predict(["우리집 고양이는 세상에서 제일 귀엽습니다"])) ```

{}

AimB/mT5-en-kr-natural

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

you can use this model with simpletransfomers.

text-classification

transformers

# Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 35248482 - CO2 Emissions (in grams): 7.989144645413398 ## Validation Metrics - Loss: 0.13783401250839233 - Accuracy: 0.9728654124457308 - Macro F1: 0.949537871674076 - Micro F1: 0.9728654124457308 - Weighted F1: 0.9732422812610365 - Macro Precision: 0.9380372699332605 - Micro Precision: 0.9728654124457308 - Weighted Precision: 0.974548513256663 - Macro Recall: 0.9689346153591594 - Micro Recall: 0.9728654124457308 - Weighted Recall: 0.9728654124457308 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Aimendo/autonlp-triage-35248482 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Aimendo/autonlp-triage-35248482", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Aimendo/autonlp-triage-35248482", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```

{"language": "en", "tags": "autonlp", "datasets": ["Aimendo/autonlp-data-triage"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 7.989144645413398}

Aimendo/autonlp-triage-35248482

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #autonlp #en #dataset-Aimendo/autonlp-data-triage #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us

# Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 35248482 - CO2 Emissions (in grams): 7.989144645413398 ## Validation Metrics - Loss: 0.13783401250839233 - Accuracy: 0.9728654124457308 - Macro F1: 0.949537871674076 - Micro F1: 0.9728654124457308 - Weighted F1: 0.9732422812610365 - Macro Precision: 0.9380372699332605 - Micro Precision: 0.9728654124457308 - Weighted Precision: 0.974548513256663 - Macro Recall: 0.9689346153591594 - Micro Recall: 0.9728654124457308 - Weighted Recall: 0.9728654124457308 ## Usage You can use cURL to access this model: Or Python API:

text-classification

transformers

# Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 530014983 - CO2 Emissions (in grams): 55.10196329868386 ## Validation Metrics - Loss: 0.23171618580818176 - Accuracy: 0.9298837645294338 - Precision: 0.9314414866901055 - Recall: 0.9279459594696022 - AUC: 0.979447403984557 - F1: 0.9296904373981703 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Ajay191191/autonlp-Test-530014983 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Ajay191191/autonlp-Test-530014983", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Ajay191191/autonlp-Test-530014983", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```

{"language": "en", "tags": "autonlp", "datasets": ["Ajay191191/autonlp-data-Test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 55.10196329868386}

Ajay191191/autonlp-Test-530014983

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #autonlp #en #dataset-Ajay191191/autonlp-data-Test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us

# Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 530014983 - CO2 Emissions (in grams): 55.10196329868386 ## Validation Metrics - Loss: 0.23171618580818176 - Accuracy: 0.9298837645294338 - Precision: 0.9314414866901055 - Recall: 0.9279459594696022 - AUC: 0.979447403984557 - F1: 0.9296904373981703 ## Usage You can use cURL to access this model: Or Python API:

text2text-generation

transformers

# Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 16122692 ## Validation Metrics - Loss: 1.1877621412277222 - Rouge1: 42.0713 - Rouge2: 23.3043 - RougeL: 37.3755 - RougeLsum: 37.8961 - Gen Len: 60.7117 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/Ajaykannan6/autonlp-manthan-16122692 ```

{"language": "unk", "tags": "autonlp", "datasets": ["Ajaykannan6/autonlp-data-manthan"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}

Ajaykannan6/autonlp-manthan-16122692

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bart #text2text-generation #autonlp #unk #dataset-Ajaykannan6/autonlp-data-manthan #autotrain_compatible #endpoints_compatible #region-us

# Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 16122692 ## Validation Metrics - Loss: 1.1877621412277222 - Rouge1: 42.0713 - Rouge2: 23.3043 - RougeL: 37.3755 - RougeLsum: 37.8961 - Gen Len: 60.7117 ## Usage You can use cURL to access this model:

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert-base-v2-finetuned-squad This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9492 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8695 | 1.0 | 8248 | 0.8813 | | 0.6333 | 2.0 | 16496 | 0.8042 | | 0.4372 | 3.0 | 24744 | 0.9492 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.7.1 - Datasets 1.15.1 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]}

Akari/albert-base-v2-finetuned-squad

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us

albert-base-v2-finetuned-squad ============================== This model is a fine-tuned version of albert-base-v2 on the squad\_v2 dataset. It achieves the following results on the evaluation set: * Loss: 0.9492 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.7.1 * Datasets 1.15.1 * Tokenizers 0.10.3

fill-mask

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.0915 | 1.0 | 2346 | 7.0517 | | 6.905 | 2.0 | 4692 | 6.8735 | | 6.8565 | 3.0 | 7038 | 6.8924 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]}

Akash7897/bert-base-cased-wikitext2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

bert-base-cased-wikitext2 ========================= This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 6.8544 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.6

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0789 - Matthews Correlation: 0.5222 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.1472 | 1.0 | 535 | 0.8407 | 0.4915 | | 0.1365 | 2.0 | 1070 | 0.9236 | 0.4990 | | 0.1194 | 3.0 | 1605 | 0.8753 | 0.4953 | | 0.1313 | 4.0 | 2140 | 0.9684 | 0.5013 | | 0.0895 | 5.0 | 2675 | 1.0789 | 0.5222 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.522211073949747, "name": "Matthews Correlation"}]}]}]}

Akash7897/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-cola ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 1.0789 * Matthews Correlation: 0.5222 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.6

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3010 - Accuracy: 0.9037 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1793 | 1.0 | 4210 | 0.3010 | 0.9037 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.9036697247706422, "name": "Accuracy"}]}]}]}

Akash7897/distilbert-base-uncased-finetuned-sst2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-sst2 ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.3010 * Accuracy: 0.9037 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.6

text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1079 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.558 | 1.0 | 2249 | 6.4672 | | 6.1918 | 2.0 | 4498 | 6.1970 | | 6.0019 | 3.0 | 6747 | 6.1079 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]}

Akash7897/gpt2-wikitext2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

gpt2-wikitext2 ============== This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 6.1079 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.6

automatic-speech-recognition

transformers

# Akashpb13/Central_kurdish_xlsr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): - Loss: 0.348580 - Wer: 0.401147 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Central Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000095637994662983496 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 200 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |-------|---------------|-----------------|----------| | 500 | 5.097800 | 2.190326 | 1.001207 | | 1000 | 0.797500 | 0.331392 | 0.576819 | | 1500 | 0.405100 | 0.262009 | 0.549049 | | 2000 | 0.322100 | 0.248178 | 0.479626 | | 2500 | 0.264600 | 0.258866 | 0.488983 | | 3000 | 0.228300 | 0.261523 | 0.469665 | | 3500 | 0.201000 | 0.270135 | 0.451856 | | 4000 | 0.180900 | 0.279302 | 0.448536 | | 4500 | 0.163800 | 0.280921 | 0.459704 | | 5000 | 0.147300 | 0.319249 | 0.471778 | | 5500 | 0.137600 | 0.289546 | 0.449140 | | 6000 | 0.132000 | 0.311350 | 0.458195 | | 6500 | 0.117100 | 0.316726 | 0.432840 | | 7000 | 0.109200 | 0.302210 | 0.439481 | | 7500 | 0.104900 | 0.325913 | 0.439481 | | 8000 | 0.097500 | 0.329446 | 0.431935 | | 8500 | 0.088600 | 0.345259 | 0.425898 | | 9000 | 0.084900 | 0.342891 | 0.428313 | | 9500 | 0.080900 | 0.353081 | 0.424389 | | 10000 | 0.075600 | 0.347063 | 0.424992 | | 10500 | 0.072800 | 0.330086 | 0.424691 | | 11000 | 0.068100 | 0.350658 | 0.421974 | | 11500 | 0.064700 | 0.342949 | 0.413522 | | 12000 | 0.061500 | 0.341704 | 0.415334 | | 12500 | 0.059500 | 0.346279 | 0.411410 | | 13000 | 0.057400 | 0.349901 | 0.407184 | | 13500 | 0.056400 | 0.347733 | 0.402656 | | 14000 | 0.053300 | 0.344899 | 0.405976 | | 14500 | 0.052900 | 0.346708 | 0.402656 | | 15000 | 0.050600 | 0.344118 | 0.400845 | | 15500 | 0.050200 | 0.348396 | 0.402958 | | 16000 | 0.049800 | 0.348312 | 0.401751 | | 16500 | 0.051900 | 0.348372 | 0.401147 | | 17000 | 0.049800 | 0.348580 | 0.401147 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.1 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/Central_kurdish_xlsr --dataset mozilla-foundation/common_voice_8_0 --config ckb --split test ```

{"language": ["ckb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ckb", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Central_kurdish_xlsr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ckb"}, "metrics": [{"type": "wer", "value": 0.36754389884276845, "name": "Test WER"}, {"type": "cer", "value": 0.07827896768334217, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ckb"}, "metrics": [{"type": "wer", "value": 0.36754389884276845, "name": "Test WER"}, {"type": "cer", "value": 0.07827896768334217, "name": "Test CER"}]}]}]}

Akashpb13/Central_kurdish_xlsr

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ckb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us

Akashpb13/Central\_kurdish\_xlsr ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): * Loss: 0.348580 * Wer: 0.401147 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice Central Kurdish URL, URL, URL, URL, and URL Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000095637994662983496 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 13 * gradient\_accumulation\_steps: 2 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 200 * num\_epochs: 100 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.1 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

automatic-speech-recognition

transformers

# Akashpb13/Galician_xlsr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): - Loss: 0.137096 - Wer: 0.196230 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Galician train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000096 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 500 | 5.038100 | 3.035432 | 1.000000 | | 1000 | 2.180000 | 0.406300 | 0.557964 | | 1500 | 0.331700 | 0.153797 | 0.262394 | | 2000 | 0.171600 | 0.145268 | 0.235627 | | 2500 | 0.125900 | 0.136622 | 0.228087 | | 3000 | 0.105400 | 0.131650 | 0.224128 | | 3500 | 0.087600 | 0.141032 | 0.217531 | | 4000 | 0.078300 | 0.143675 | 0.214515 | | 4500 | 0.070000 | 0.144607 | 0.208106 | | 5000 | 0.061500 | 0.135259 | 0.202828 | | 5500 | 0.055600 | 0.130638 | 0.203959 | | 6000 | 0.050500 | 0.137416 | 0.202451 | | 6500 | 0.046600 | 0.140379 | 0.200000 | | 7000 | 0.040800 | 0.140179 | 0.200377 | | 7500 | 0.041000 | 0.138089 | 0.196795 | | 8000 | 0.038400 | 0.136927 | 0.197172 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/Galician_xlsr --dataset mozilla-foundation/common_voice_8_0 --config gl --split test ```

{"language": ["gl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "gl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Galician_xlsr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "kmr"}, "metrics": [{"type": "wer", "value": 0.11308483789555426, "name": "Test WER"}, {"type": "cer", "value": 0.023982371794871796, "name": "Test CER"}, {"type": "wer", "value": 11.31, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "gl"}, "metrics": [{"type": "wer", "value": 0.11308483789555426, "name": "Test WER"}, {"type": "cer", "value": 0.023982371794871796, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "gl"}, "metrics": [{"type": "wer", "value": 39.05, "name": "Test WER"}]}]}]}

Akashpb13/Galician_xlsr

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #gl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

Akashpb13/Galician\_xlsr ======================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): * Loss: 0.137096 * Wer: 0.196230 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice Galician URL, URL, URL, URL, and URL Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000096 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 13 * gradient\_accumulation\_steps: 2 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 100 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.3 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

automatic-speech-recognition

transformers

# Akashpb13/Hausa_xlsr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): - Loss: 0.275118 - Wer: 0.329955 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000096 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 500 | 5.175900 | 2.750914 | 1.000000 | | 1000 | 1.028700 | 0.338649 | 0.497999 | | 1500 | 0.332200 | 0.246896 | 0.402241 | | 2000 | 0.227300 | 0.239640 | 0.395839 | | 2500 | 0.175000 | 0.239577 | 0.373966 | | 3000 | 0.140400 | 0.243272 | 0.356095 | | 3500 | 0.119200 | 0.263761 | 0.365164 | | 4000 | 0.099300 | 0.265954 | 0.353428 | | 4500 | 0.084400 | 0.276367 | 0.349693 | | 5000 | 0.073700 | 0.282631 | 0.343825 | | 5500 | 0.068000 | 0.282344 | 0.341158 | | 6000 | 0.064500 | 0.281591 | 0.342491 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/Hausa_xlsr --dataset mozilla-foundation/common_voice_8_0 --config ha --split test ```

{"language": ["ha"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "ha", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Hausa_xlsr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ha"}, "metrics": [{"type": "wer", "value": 0.20614541257934219, "name": "Test WER"}, {"type": "cer", "value": 0.04358048053214061, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ha"}, "metrics": [{"type": "wer", "value": 0.20614541257934219, "name": "Test WER"}, {"type": "cer", "value": 0.04358048053214061, "name": "Test CER"}]}]}]}

Akashpb13/Hausa_xlsr

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #ha #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us

Akashpb13/Hausa\_xlsr ===================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): * Loss: 0.275118 * Wer: 0.329955 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice Hausa URL, URL, URL, URL and URL Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000096 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 13 * gradient\_accumulation\_steps: 2 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.3 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

automatic-speech-recognition

transformers

# Akashpb13/Kabyle_xlsr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): - Loss: 0.159032 - Wer: 0.187934 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset. Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000096 - train_batch_size: 8 - seed: 13 - gradient_accumulation_steps: 4 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |-------|---------------|-----------------|----------| | 500 | 7.199800 | 3.130564 | 1.000000 | | 1000 | 1.570200 | 0.718097 | 0.734682 | | 1500 | 0.850800 | 0.524227 | 0.640532 | | 2000 | 0.712200 | 0.468694 | 0.603454 | | 2500 | 0.651200 | 0.413833 | 0.573025 | | 3000 | 0.603100 | 0.403680 | 0.552847 | | 3500 | 0.553300 | 0.372638 | 0.541719 | | 4000 | 0.537200 | 0.353759 | 0.531191 | | 4500 | 0.506300 | 0.359109 | 0.519601 | | 5000 | 0.479600 | 0.343937 | 0.511336 | | 5500 | 0.479800 | 0.338214 | 0.503948 | | 6000 | 0.449500 | 0.332600 | 0.495221 | | 6500 | 0.439200 | 0.323905 | 0.492635 | | 7000 | 0.434900 | 0.310417 | 0.484555 | | 7500 | 0.403200 | 0.311247 | 0.483262 | | 8000 | 0.401500 | 0.295637 | 0.476566 | | 8500 | 0.397000 | 0.301321 | 0.471672 | | 9000 | 0.371600 | 0.295639 | 0.468440 | | 9500 | 0.370700 | 0.294039 | 0.468902 | | 10000 | 0.364900 | 0.291195 | 0.468440 | | 10500 | 0.348300 | 0.284898 | 0.461098 | | 11000 | 0.350100 | 0.281764 | 0.459805 | | 11500 | 0.336900 | 0.291022 | 0.461606 | | 12000 | 0.330700 | 0.280467 | 0.455234 | | 12500 | 0.322500 | 0.271714 | 0.452694 | | 13000 | 0.307400 | 0.289519 | 0.455465 | | 13500 | 0.309300 | 0.281922 | 0.451217 | | 14000 | 0.304800 | 0.271514 | 0.452186 | | 14500 | 0.288100 | 0.286801 | 0.446830 | | 15000 | 0.293200 | 0.276309 | 0.445399 | | 15500 | 0.289800 | 0.287188 | 0.446230 | | 16000 | 0.274800 | 0.286406 | 0.441243 | | 16500 | 0.271700 | 0.284754 | 0.441520 | | 17000 | 0.262500 | 0.275431 | 0.442167 | | 17500 | 0.255500 | 0.276575 | 0.439858 | | 18000 | 0.260200 | 0.269911 | 0.435425 | | 18500 | 0.250600 | 0.270519 | 0.434686 | | 19000 | 0.243300 | 0.267655 | 0.437826 | | 19500 | 0.240600 | 0.277109 | 0.431731 | | 20000 | 0.237200 | 0.266622 | 0.433994 | | 20500 | 0.231300 | 0.273015 | 0.428868 | | 21000 | 0.227200 | 0.263024 | 0.430161 | | 21500 | 0.220400 | 0.272880 | 0.429607 | | 22000 | 0.218600 | 0.272340 | 0.426883 | | 22500 | 0.213100 | 0.277066 | 0.428407 | | 23000 | 0.205000 | 0.278404 | 0.424020 | | 23500 | 0.200900 | 0.270877 | 0.418987 | | 24000 | 0.199000 | 0.289120 | 0.425821 | | 24500 | 0.196100 | 0.275831 | 0.424066 | | 25000 | 0.191100 | 0.282822 | 0.421850 | | 25500 | 0.190100 | 0.275820 | 0.418248 | | 26000 | 0.178800 | 0.279208 | 0.419125 | | 26500 | 0.183100 | 0.271464 | 0.419218 | | 27000 | 0.177400 | 0.280869 | 0.419680 | | 27500 | 0.171800 | 0.279593 | 0.414924 | | 28000 | 0.172900 | 0.276949 | 0.417648 | | 28500 | 0.164900 | 0.283491 | 0.417786 | | 29000 | 0.164800 | 0.283122 | 0.416078 | | 29500 | 0.165500 | 0.281969 | 0.415801 | | 30000 | 0.163800 | 0.283319 | 0.412753 | | 30500 | 0.153500 | 0.285702 | 0.414046 | | 31000 | 0.156500 | 0.285041 | 0.412615 | | 31500 | 0.150900 | 0.284336 | 0.413723 | | 32000 | 0.151800 | 0.285922 | 0.412292 | | 32500 | 0.149200 | 0.289461 | 0.412153 | | 33000 | 0.145400 | 0.291322 | 0.409567 | | 33500 | 0.145600 | 0.294361 | 0.409614 | | 34000 | 0.144200 | 0.290686 | 0.409059 | | 34500 | 0.143400 | 0.289474 | 0.409844 | | 35000 | 0.143500 | 0.290340 | 0.408367 | | 35500 | 0.143200 | 0.289581 | 0.407351 | | 36000 | 0.138400 | 0.292782 | 0.408736 | | 36500 | 0.137900 | 0.289108 | 0.408044 | | 37000 | 0.138200 | 0.292127 | 0.407166 | | 37500 | 0.134600 | 0.291797 | 0.408413 | | 38000 | 0.139800 | 0.290056 | 0.408090 | | 38500 | 0.136500 | 0.291198 | 0.408090 | | 39000 | 0.137700 | 0.289696 | 0.408044 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/Kabyle_xlsr --dataset mozilla-foundation/common_voice_8_0 --config kab --split test ```

{"language": ["kab"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "sw", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Kabyle_xlsr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "kab"}, "metrics": [{"type": "wer", "value": 0.3188425282720088, "name": "Test WER"}, {"type": "cer", "value": 0.09443079928558358, "name": "Test CER"}]}]}]}

Akashpb13/Kabyle_xlsr

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sw #robust-speech-event #model_for_talk #hf-asr-leaderboard #kab #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

Akashpb13/Kabyle\_xlsr ====================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): * Loss: 0.159032 * Wer: 0.187934 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice Kabyle URL. Only 50,000 records were sampled randomly and trained due to huge size of dataset. Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000096 * train\_batch\_size: 8 * seed: 13 * gradient\_accumulation\_steps: 4 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.3 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

automatic-speech-recognition

transformers

# Akashpb13/Swahili_xlsr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): - Loss: 0.159032 - Wer: 0.187934 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Hausa train.tsv and dev.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000096 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 2 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 80 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 500 | 4.810000 | 2.168847 | 0.995747 | | 1000 | 0.564200 | 0.209411 | 0.303485 | | 1500 | 0.217700 | 0.153959 | 0.239534 | | 2000 | 0.150700 | 0.139901 | 0.216327 | | 2500 | 0.119400 | 0.137543 | 0.208828 | | 3000 | 0.099500 | 0.140921 | 0.203045 | | 3500 | 0.087100 | 0.138835 | 0.199649 | | 4000 | 0.074600 | 0.141297 | 0.195844 | | 4500 | 0.066600 | 0.148560 | 0.194127 | | 5000 | 0.060400 | 0.151214 | 0.194388 | | 5500 | 0.054400 | 0.156072 | 0.192187 | | 6000 | 0.051100 | 0.154726 | 0.190322 | | 6500 | 0.048200 | 0.159847 | 0.189538 | | 7000 | 0.046400 | 0.158727 | 0.188307 | | 7500 | 0.046500 | 0.159032 | 0.187934 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/Swahili_xlsr --dataset mozilla-foundation/common_voice_8_0 --config sw --split test ```

{"language": ["sw"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "sw"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Swahili_xlsr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sw"}, "metrics": [{"type": "wer", "value": 0.11763625454589981, "name": "Test WER"}, {"type": "cer", "value": 0.02884228669922436, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "kmr"}, "metrics": [{"type": "wer", "value": 0.11763625454589981, "name": "Test WER"}, {"type": "cer", "value": 0.02884228669922436, "name": "Test CER"}]}]}]}

Akashpb13/Swahili_xlsr

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #sw #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us

Akashpb13/Swahili\_xlsr ======================= This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): * Loss: 0.159032 * Wer: 0.187934 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice Hausa URL and URL Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000096 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 13 * gradient\_accumulation\_steps: 2 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 80 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.3 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

automatic-speech-recognition

transformers

# Akashpb13/xlsr_hungarian_new This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): - Loss: 0.197464 - Wer: 0.330094 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice hungarian train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000095637994662983496 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 16 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 500 | 4.785300 | 0.952295 | 0.796236 | | 1000 | 0.535800 | 0.217474 | 0.381613 | | 1500 | 0.258400 | 0.205524 | 0.345056 | | 2000 | 0.202800 | 0.198680 | 0.336264 | | 2500 | 0.182700 | 0.197464 | 0.330094 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/xlsr_hungarian_new --dataset mozilla-foundation/common_voice_8_0 --config hu --split test ```

{"language": ["hu"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "hu", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/xlsr_hungarian_new", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hu"}, "metrics": [{"type": "wer", "value": 0.2851621517163838, "name": "Test WER"}, {"type": "cer", "value": 0.06112982522287432, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hu"}, "metrics": [{"type": "wer", "value": 0.2851621517163838, "name": "Test WER"}, {"type": "cer", "value": 0.06112982522287432, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "hu"}, "metrics": [{"type": "wer", "value": 47.15, "name": "Test WER"}]}]}]}

Akashpb13/xlsr_hungarian_new

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hu #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

Akashpb13/xlsr\_hungarian\_new ============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - hu dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets): * Loss: 0.197464 * Wer: 0.330094 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice hungarian URL, URL, URL, URL, and URL Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the train dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000095637994662983496 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 13 * gradient\_accumulation\_steps: 16 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 100 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.3 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

automatic-speech-recognition

transformers

# Akashpb13/xlsr_kurmanji_kurdish This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): - Loss: 0.292389 - Wer: 0.388585 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Kurmanji Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000096 - train_batch_size: 16 - eval_batch_size: 16 - seed: 13 - gradient_accumulation_steps: 16 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 200 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |------|---------------|-----------------|----------| | 200 | 4.382500 | 3.183725 | 1.000000 | | 400 | 2.870200 | 0.996664 | 0.781117 | | 600 | 0.609900 | 0.333755 | 0.445052 | | 800 | 0.326800 | 0.305729 | 0.403157 | | 1000 | 0.255000 | 0.290734 | 0.391621 | | 1200 | 0.226300 | 0.292389 | 0.388585 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.1 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/xlsr_kurmanji_kurdish --dataset mozilla-foundation/common_voice_8_0 --config kmr --split test ```

{"language": ["kmr", "ku"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "kmr", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/xlsr_kurmanji_kurdish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "kmr"}, "metrics": [{"type": "wer", "value": 0.33073206986250464, "name": "Test WER"}, {"type": "cer", "value": 0.08035244447163924, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "kmr"}, "metrics": [{"type": "wer", "value": 0.33073206986250464, "name": "Test WER"}, {"type": "cer", "value": 0.08035244447163924, "name": "Test CER"}]}]}]}

Akashpb13/xlsr_kurmanji_kurdish

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #kmr #robust-speech-event #model_for_talk #hf-asr-leaderboard #ku #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us

Akashpb13/xlsr\_kurmanji\_kurdish ================================= This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets): * Loss: 0.292389 * Wer: 0.388585 Model description ----------------- "facebook/wav2vec2-xls-r-300m" was finetuned. Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- Training data - Common voice Kurmanji Kurdish URL, URL, URL, URL, and URL Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 Training procedure ------------------ For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000096 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 13 * gradient\_accumulation\_steps: 16 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 200 * num\_epochs: 100 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.18.1 * Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'

automatic-speech-recognition

transformers

# Wav2Vec2-Large-XLSR-53-Maltese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Maltese using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys model_name = "Akashpb13/xlsr_maltese_wav2vec2" device = "cuda" chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\)\\(\\*)]' model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(model_name) ds = load_dataset("common_voice", "mt", split="test", data_dir="./cv-corpus-6.1-2020-12-11") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " return batch ds = ds.map(map_to_array) def map_to_pred(batch): features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) batch["target"] = batch["sentence"] return batch result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` **Test Result**: 29.42 %

{"language": "mt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Maltese by Akash PB", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice mt", "type": "common_voice", "args": {}}, "metrics": [{"type": "wer", "value": 29.42, "name": "Test WER"}]}]}]}

Akashpb13/xlsr_maltese_wav2vec2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us

# Wav2Vec2-Large-XLSR-53-Maltese Fine-tuned facebook/wav2vec2-large-xlsr-53 in Maltese using the Common Voice When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: Test Result: 29.42 %

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

Akjder/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Harry Potter DialoGPT Model

image-classification

transformers

# BEiT for Face Mask Detection BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. ## Model description The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that. ## Training Metrics epoch = 0.55 total_flos = 576468516GF train_loss = 0.151 train_runtime = 0:58:16.56 train_samples_per_second = 16.505 train_steps_per_second = 1.032 --- ## Evaluation Metrics epoch = 0.55 eval_accuracy = 0.975 eval_loss = 0.0803 eval_runtime = 0:03:13.02 eval_samples_per_second = 18.629 eval_steps_per_second = 2.331

{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]}

AkshatSurolia/BEiT-FaceMask-Finetuned

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #beit #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# BEiT for Face Mask Detection BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. ## Model description The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that. ## Training Metrics epoch = 0.55 total_flos = 576468516GF train_loss = 0.151 train_runtime = 0:58:16.56 train_samples_per_second = 16.505 train_steps_per_second = 1.032 --- ## Evaluation Metrics epoch = 0.55 eval_accuracy = 0.975 eval_loss = 0.0803 eval_runtime = 0:03:13.02 eval_samples_per_second = 18.629 eval_steps_per_second = 2.331

image-classification

transformers

# ConvNeXt for Face Mask Detection ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al. ## Training Metrics epoch = 3.54 total_flos = 1195651761GF train_loss = 0.0079 train_runtime = 1:08:20.25 train_samples_per_second = 14.075 train_steps_per_second = 0.22 --- ## Evaluation Metrics epoch = 3.54 eval_accuracy = 0.9961 eval_loss = 0.0151 eval_runtime = 0:01:23.47 eval_samples_per_second = 43.079 eval_steps_per_second = 5.391

{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]}

AkshatSurolia/ConvNeXt-FaceMask-Finetuned

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #convnext #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# ConvNeXt for Face Mask Detection ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al. ## Training Metrics epoch = 3.54 total_flos = 1195651761GF train_loss = 0.0079 train_runtime = 1:08:20.25 train_samples_per_second = 14.075 train_steps_per_second = 0.22 --- ## Evaluation Metrics epoch = 3.54 eval_accuracy = 0.9961 eval_loss = 0.0151 eval_runtime = 0:01:23.47 eval_samples_per_second = 43.079 eval_steps_per_second = 5.391

image-classification

transformers

# Distilled Data-efficient Image Transformer for Face Mask Detection Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. ## Model description This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. ## Training Metrics epoch = 2.0 total_flos = 2078245655GF train_loss = 0.0438 train_runtime = 1:37:16.87 train_samples_per_second = 9.887 train_steps_per_second = 0.309 --- ## Evaluation Metrics epoch = 2.0 eval_accuracy = 0.9922 eval_loss = 0.0271 eval_runtime = 0:03:17.36 eval_samples_per_second = 18.22 eval_steps_per_second = 2.28

{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]}

AkshatSurolia/DeiT-FaceMask-Finetuned

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #deit #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# Distilled Data-efficient Image Transformer for Face Mask Detection Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. ## Model description This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. ## Training Metrics epoch = 2.0 total_flos = 2078245655GF train_loss = 0.0438 train_runtime = 1:37:16.87 train_samples_per_second = 9.887 train_steps_per_second = 0.309 --- ## Evaluation Metrics epoch = 2.0 eval_accuracy = 0.9922 eval_loss = 0.0271 eval_runtime = 0:03:17.36 eval_samples_per_second = 18.22 eval_steps_per_second = 2.28

text-classification

transformers

# Clinical BERT for ICD-10 Prediction The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. --- ## How to use the model Load the model via the transformers library: from transformers import AutoTokenizer, BertForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") config = model.config Run the model with clinical diagonosis text: text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) Return the Top-5 predicted ICD-10 codes: results = output.logits.detach().cpu().numpy()[0].argsort()[::-1][:5] return [ config.id2label[ids] for ids in results]

{"license": "apache-2.0", "tags": ["text-classification"]}

AkshatSurolia/ICD-10-Code-Prediction

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #text-classification #license-apache-2.0 #endpoints_compatible #has_space #region-us

# Clinical BERT for ICD-10 Prediction The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. --- ## How to use the model Load the model via the transformers library: from transformers import AutoTokenizer, BertForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") config = URL Run the model with clinical diagonosis text: text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive" encoded_input = tokenizer(text, return_tensors='pt') output = model(encoded_input) Return the Top-5 predicted ICD-10 codes: results = URL().cpu().numpy()[0].argsort()[::-1][:5] return [ config.id2label[ids] for ids in results]

image-classification

transformers

# Vision Transformer (ViT) for Face Mask Detection Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Training Metrics epoch = 0.89 total_flos = 923776502GF train_loss = 0.057 train_runtime = 0:40:10.40 train_samples_per_second = 23.943 train_steps_per_second = 1.497 --- ## Evaluation Metrics epoch = 0.89 eval_accuracy = 0.9894 eval_loss = 0.0395 eval_runtime = 0:00:36.81 eval_samples_per_second = 97.685 eval_steps_per_second = 12.224

{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]}

AkshatSurolia/ViT-FaceMask-Finetuned

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #vit #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Vision Transformer (ViT) for Face Mask Detection Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Training Metrics epoch = 0.89 total_flos = 923776502GF train_loss = 0.057 train_runtime = 0:40:10.40 train_samples_per_second = 23.943 train_steps_per_second = 1.497 --- ## Evaluation Metrics epoch = 0.89 eval_accuracy = 0.9894 eval_loss = 0.0395 eval_runtime = 0:00:36.81 eval_samples_per_second = 97.685 eval_steps_per_second = 12.224

# Spoken Language Identification Model ## Model description The model can classify a speech utterance according to the language spoken. It covers following different languages ( English, Indonesian, Japanese, Korean, Thai, Vietnamese, Mandarin Chinese).

{"language": "multilingual", "license": "apache-2.0", "tags": ["LID", "spoken language recognition"], "datasets": ["VoxLingua107"], "metrics": ["ER"], "inference": false}

AkshaySg/LanguageIdentification

2022-03-02T23:29:04+00:00

TAGS #LID #spoken language recognition #multilingual #dataset-VoxLingua107 #license-apache-2.0 #region-us

# Spoken Language Identification Model ## Model description The model can classify a speech utterance according to the language spoken. It covers following different languages ( English, Indonesian, Japanese, Korean, Thai, Vietnamese, Mandarin Chinese).

audio-classification

speechbrain

# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model ## Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese). ## Intended uses & limitations The model has two uses: - use 'as is' for spoken language recognition - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data The model is trained on automatically collected YouTube data. For more information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/). #### How to use ```python import torchaudio from speechbrain.pretrained import EncoderClassifier language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp") # Download Thai language sample from Omniglot and cvert to suitable form signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3") prediction = language_id.classify_batch(signal) print(prediction) (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508, 0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997, 0.4106, 0.3886, 0.4177, 0.3870, 0.3764, 0.3763, 0.3672, 0.4000, 0.4256, 0.4091, 0.3563, 0.3695, 0.3320, 0.3838, 0.3850, 0.3867, 0.3878, 0.3944, 0.3924, 0.4063, 0.3803, 0.3830, 0.2996, 0.4187, 0.3976, 0.3651, 0.3950, 0.3744, 0.4295, 0.3807, 0.3613, 0.4710, 0.3530, 0.4156, 0.3651, 0.3777, 0.3813, 0.6063, 0.3708, 0.3886, 0.3766, 0.4023, 0.3785, 0.3612, 0.4193, 0.3720, 0.4406, 0.3243, 0.3866, 0.3866, 0.4104, 0.4294, 0.4175, 0.3364, 0.3595, 0.3443, 0.3565, 0.3776, 0.3985, 0.3778, 0.2382, 0.4115, 0.4017, 0.4070, 0.3266, 0.3648, 0.3888, 0.3907, 0.3755, 0.3631, 0.4460, 0.3464, 0.3898, 0.3661, 0.3883, 0.3772, 0.9289, 0.3687, 0.4298, 0.4211, 0.3838, 0.3521, 0.3515, 0.3465, 0.4772, 0.4043, 0.3844, 0.3973, 0.4343]]), tensor([0.9289]), tensor([94]), ['th']) # The scores in the prediction[0] tensor can be interpreted as cosine scores between # the languages and the given utterance (i.e., the larger the better) # The identified language ISO code is given in prediction[3] print(prediction[3]) ['th'] # Alternatively, use the utterance embedding extractor: emb = language_id.encode_batch(signal) print(emb.shape) torch.Size([1, 1, 256]) ``` #### Limitations and bias Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: - Probably it's accuracy on smaller languages is quite limited - Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) - Based on subjective experiments, it doesn't work well on speech with a foreign accent - Probably it doesn't work well on children's speech and on persons with speech disorders ## Training data The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/). VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. ## Training procedure We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model. Training recipe will be published soon. ## Evaluation results Error rate: 7% on the development dataset ### BibTeX entry and citation info ```bibtex @inproceedings{valk2021slt, title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, booktitle={Proc. IEEE SLT Workshop}, year={2021}, } ```

{"language": "multilingual", "license": "apache-2.0", "tags": ["audio-classification", "speechbrain", "embeddings", "Language", "Identification", "pytorch", "ECAPA-TDNN", "TDNN", "VoxLingua107"], "datasets": ["VoxLingua107"], "metrics": ["Accuracy"], "widget": [{"example_title": "English Sample", "src": "https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac"}]}

AkshaySg/langid

2022-03-02T23:29:04+00:00

TAGS #speechbrain #audio-classification #embeddings #Language #Identification #pytorch #ECAPA-TDNN #TDNN #VoxLingua107 #multilingual #dataset-VoxLingua107 #license-apache-2.0 #region-us

# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model ## Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese). ## Intended uses & limitations The model has two uses: - use 'as is' for spoken language recognition - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data The model is trained on automatically collected YouTube data. For more information about the dataset, see here. #### How to use #### Limitations and bias Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: - Probably it's accuracy on smaller languages is quite limited - Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) - Based on subjective experiments, it doesn't work well on speech with a foreign accent - Probably it doesn't work well on children's speech and on persons with speech disorders ## Training data The model is trained on VoxLingua107. VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. ## Training procedure We used SpeechBrain to train the model. Training recipe will be published soon. ## Evaluation results Error rate: 7% on the development dataset ### BibTeX entry and citation info

fill-mask

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-srb-base-cased-oscar This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "model_index": [{"name": "bert-srb-base-cased-oscar", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]}

Aleksandar/bert-srb-base-cased-oscar

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# bert-srb-base-cased-oscar This model is a fine-tuned version of [](URL on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-srb-ner-setimes This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1955 - Precision: 0.8229 - Recall: 0.8465 - F1: 0.8345 - Accuracy: 0.9645 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 104 | 0.2281 | 0.6589 | 0.7001 | 0.6789 | 0.9350 | | No log | 2.0 | 208 | 0.1833 | 0.7105 | 0.7694 | 0.7388 | 0.9470 | | No log | 3.0 | 312 | 0.1573 | 0.7461 | 0.7778 | 0.7616 | 0.9525 | | No log | 4.0 | 416 | 0.1489 | 0.7665 | 0.8091 | 0.7872 | 0.9557 | | 0.1898 | 5.0 | 520 | 0.1445 | 0.7881 | 0.8327 | 0.8098 | 0.9587 | | 0.1898 | 6.0 | 624 | 0.1473 | 0.7913 | 0.8316 | 0.8109 | 0.9601 | | 0.1898 | 7.0 | 728 | 0.1558 | 0.8101 | 0.8347 | 0.8222 | 0.9620 | | 0.1898 | 8.0 | 832 | 0.1616 | 0.8026 | 0.8302 | 0.8162 | 0.9612 | | 0.1898 | 9.0 | 936 | 0.1716 | 0.8127 | 0.8409 | 0.8266 | 0.9631 | | 0.0393 | 10.0 | 1040 | 0.1751 | 0.8140 | 0.8369 | 0.8253 | 0.9628 | | 0.0393 | 11.0 | 1144 | 0.1775 | 0.8096 | 0.8420 | 0.8255 | 0.9626 | | 0.0393 | 12.0 | 1248 | 0.1763 | 0.8161 | 0.8386 | 0.8272 | 0.9636 | | 0.0393 | 13.0 | 1352 | 0.1949 | 0.8259 | 0.8400 | 0.8329 | 0.9634 | | 0.0393 | 14.0 | 1456 | 0.1842 | 0.8205 | 0.8420 | 0.8311 | 0.9642 | | 0.0111 | 15.0 | 1560 | 0.1862 | 0.8160 | 0.8493 | 0.8323 | 0.9646 | | 0.0111 | 16.0 | 1664 | 0.1989 | 0.8176 | 0.8367 | 0.8270 | 0.9627 | | 0.0111 | 17.0 | 1768 | 0.1945 | 0.8246 | 0.8409 | 0.8327 | 0.9638 | | 0.0111 | 18.0 | 1872 | 0.1997 | 0.8270 | 0.8426 | 0.8347 | 0.9634 | | 0.0111 | 19.0 | 1976 | 0.1917 | 0.8258 | 0.8491 | 0.8373 | 0.9651 | | 0.0051 | 20.0 | 2080 | 0.1955 | 0.8229 | 0.8465 | 0.8345 | 0.9645 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-srb-ner-setimes", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9645112274185379}}]}]}

Aleksandar/bert-srb-ner-setimes

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

bert-srb-ner-setimes ==================== This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.1955 * Precision: 0.8229 * Recall: 0.8465 * F1: 0.8345 * Accuracy: 0.9645 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0 * Datasets 1.11.0 * Tokenizers 0.10.1

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-srb-ner This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3561 - Precision: 0.8909 - Recall: 0.9082 - F1: 0.8995 - Accuracy: 0.9547 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3907 | 1.0 | 625 | 0.2316 | 0.8255 | 0.8314 | 0.8285 | 0.9259 | | 0.2091 | 2.0 | 1250 | 0.1920 | 0.8598 | 0.8731 | 0.8664 | 0.9420 | | 0.1562 | 3.0 | 1875 | 0.1833 | 0.8608 | 0.8820 | 0.8713 | 0.9441 | | 0.0919 | 4.0 | 2500 | 0.1985 | 0.8712 | 0.8886 | 0.8798 | 0.9476 | | 0.0625 | 5.0 | 3125 | 0.2195 | 0.8762 | 0.8923 | 0.8842 | 0.9485 | | 0.0545 | 6.0 | 3750 | 0.2320 | 0.8706 | 0.9004 | 0.8852 | 0.9495 | | 0.0403 | 7.0 | 4375 | 0.2459 | 0.8817 | 0.8957 | 0.8887 | 0.9505 | | 0.0269 | 8.0 | 5000 | 0.2603 | 0.8813 | 0.9021 | 0.8916 | 0.9516 | | 0.0193 | 9.0 | 5625 | 0.2916 | 0.8812 | 0.8949 | 0.8880 | 0.9500 | | 0.0162 | 10.0 | 6250 | 0.2938 | 0.8814 | 0.9025 | 0.8918 | 0.9520 | | 0.0134 | 11.0 | 6875 | 0.3330 | 0.8809 | 0.8961 | 0.8885 | 0.9497 | | 0.0076 | 12.0 | 7500 | 0.3141 | 0.8840 | 0.9025 | 0.8932 | 0.9524 | | 0.0069 | 13.0 | 8125 | 0.3292 | 0.8819 | 0.9065 | 0.8940 | 0.9535 | | 0.0053 | 14.0 | 8750 | 0.3454 | 0.8844 | 0.9018 | 0.8930 | 0.9523 | | 0.0038 | 15.0 | 9375 | 0.3519 | 0.8912 | 0.9061 | 0.8986 | 0.9539 | | 0.0034 | 16.0 | 10000 | 0.3437 | 0.8894 | 0.9038 | 0.8965 | 0.9539 | | 0.0024 | 17.0 | 10625 | 0.3518 | 0.8896 | 0.9072 | 0.8983 | 0.9543 | | 0.0018 | 18.0 | 11250 | 0.3572 | 0.8877 | 0.9072 | 0.8973 | 0.9543 | | 0.0015 | 19.0 | 11875 | 0.3554 | 0.8910 | 0.9081 | 0.8994 | 0.9549 | | 0.0011 | 20.0 | 12500 | 0.3561 | 0.8909 | 0.9082 | 0.8995 | 0.9547 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-srb-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "sr"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9546696220907545}}]}]}

Aleksandar/bert-srb-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #dataset-wikiann #autotrain_compatible #endpoints_compatible #region-us

bert-srb-ner ============ This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set: * Loss: 0.3561 * Precision: 0.8909 * Recall: 0.9082 * F1: 0.8995 * Accuracy: 0.9547 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0 * Datasets 1.11.0 * Tokenizers 0.10.1

fill-mask

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-srb-base-cased-oscar This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "model_index": [{"name": "distilbert-srb-base-cased-oscar", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]}

Aleksandar/distilbert-srb-base-cased-oscar

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# distilbert-srb-base-cased-oscar This model is a fine-tuned version of [](URL on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-srb-ner-setimes This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1838 - Precision: 0.8370 - Recall: 0.8617 - F1: 0.8492 - Accuracy: 0.9665 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 104 | 0.2319 | 0.6668 | 0.7029 | 0.6844 | 0.9358 | | No log | 2.0 | 208 | 0.1850 | 0.7265 | 0.7508 | 0.7385 | 0.9469 | | No log | 3.0 | 312 | 0.1584 | 0.7555 | 0.7937 | 0.7741 | 0.9538 | | No log | 4.0 | 416 | 0.1484 | 0.7644 | 0.8128 | 0.7879 | 0.9571 | | 0.1939 | 5.0 | 520 | 0.1383 | 0.7850 | 0.8131 | 0.7988 | 0.9604 | | 0.1939 | 6.0 | 624 | 0.1409 | 0.7914 | 0.8359 | 0.8130 | 0.9632 | | 0.1939 | 7.0 | 728 | 0.1526 | 0.8176 | 0.8392 | 0.8283 | 0.9637 | | 0.1939 | 8.0 | 832 | 0.1536 | 0.8195 | 0.8409 | 0.8301 | 0.9641 | | 0.1939 | 9.0 | 936 | 0.1538 | 0.8242 | 0.8523 | 0.8380 | 0.9661 | | 0.0364 | 10.0 | 1040 | 0.1612 | 0.8228 | 0.8413 | 0.8319 | 0.9652 | | 0.0364 | 11.0 | 1144 | 0.1721 | 0.8289 | 0.8503 | 0.8395 | 0.9656 | | 0.0364 | 12.0 | 1248 | 0.1645 | 0.8301 | 0.8590 | 0.8443 | 0.9663 | | 0.0364 | 13.0 | 1352 | 0.1747 | 0.8352 | 0.8540 | 0.8445 | 0.9665 | | 0.0364 | 14.0 | 1456 | 0.1703 | 0.8277 | 0.8573 | 0.8422 | 0.9663 | | 0.011 | 15.0 | 1560 | 0.1770 | 0.8314 | 0.8624 | 0.8466 | 0.9665 | | 0.011 | 16.0 | 1664 | 0.1903 | 0.8399 | 0.8537 | 0.8467 | 0.9661 | | 0.011 | 17.0 | 1768 | 0.1837 | 0.8363 | 0.8590 | 0.8475 | 0.9665 | | 0.011 | 18.0 | 1872 | 0.1820 | 0.8338 | 0.8570 | 0.8453 | 0.9667 | | 0.011 | 19.0 | 1976 | 0.1855 | 0.8382 | 0.8620 | 0.8499 | 0.9666 | | 0.0053 | 20.0 | 2080 | 0.1838 | 0.8370 | 0.8617 | 0.8492 | 0.9665 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-srb-ner-setimes", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9665376552169005}}]}]}

Aleksandar/distilbert-srb-ner-setimes

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

distilbert-srb-ner-setimes ========================== This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.1838 * Precision: 0.8370 * Recall: 0.8617 * F1: 0.8492 * Accuracy: 0.9665 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0 * Datasets 1.11.0 * Tokenizers 0.10.1

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-srb-ner This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2972 - Precision: 0.8871 - Recall: 0.9100 - F1: 0.8984 - Accuracy: 0.9577 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3818 | 1.0 | 625 | 0.2175 | 0.8175 | 0.8370 | 0.8272 | 0.9306 | | 0.198 | 2.0 | 1250 | 0.1766 | 0.8551 | 0.8732 | 0.8640 | 0.9458 | | 0.1423 | 3.0 | 1875 | 0.1702 | 0.8597 | 0.8763 | 0.8679 | 0.9473 | | 0.079 | 4.0 | 2500 | 0.1774 | 0.8674 | 0.8875 | 0.8773 | 0.9515 | | 0.0531 | 5.0 | 3125 | 0.2011 | 0.8688 | 0.8965 | 0.8825 | 0.9522 | | 0.0429 | 6.0 | 3750 | 0.2082 | 0.8769 | 0.8970 | 0.8868 | 0.9538 | | 0.032 | 7.0 | 4375 | 0.2268 | 0.8764 | 0.8916 | 0.8839 | 0.9528 | | 0.0204 | 8.0 | 5000 | 0.2423 | 0.8726 | 0.8959 | 0.8841 | 0.9529 | | 0.0148 | 9.0 | 5625 | 0.2522 | 0.8774 | 0.8991 | 0.8881 | 0.9538 | | 0.0125 | 10.0 | 6250 | 0.2544 | 0.8823 | 0.9024 | 0.8922 | 0.9559 | | 0.0108 | 11.0 | 6875 | 0.2592 | 0.8780 | 0.9041 | 0.8909 | 0.9553 | | 0.007 | 12.0 | 7500 | 0.2672 | 0.8877 | 0.9056 | 0.8965 | 0.9571 | | 0.0048 | 13.0 | 8125 | 0.2714 | 0.8879 | 0.9089 | 0.8982 | 0.9583 | | 0.0049 | 14.0 | 8750 | 0.2872 | 0.8873 | 0.9068 | 0.8970 | 0.9573 | | 0.0034 | 15.0 | 9375 | 0.2915 | 0.8883 | 0.9114 | 0.8997 | 0.9577 | | 0.0027 | 16.0 | 10000 | 0.2890 | 0.8865 | 0.9103 | 0.8983 | 0.9581 | | 0.0028 | 17.0 | 10625 | 0.2885 | 0.8877 | 0.9085 | 0.8980 | 0.9576 | | 0.0014 | 18.0 | 11250 | 0.2928 | 0.8860 | 0.9073 | 0.8965 | 0.9577 | | 0.0013 | 19.0 | 11875 | 0.2963 | 0.8856 | 0.9099 | 0.8976 | 0.9576 | | 0.001 | 20.0 | 12500 | 0.2972 | 0.8871 | 0.9100 | 0.8984 | 0.9577 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"language": ["sr"], "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-srb-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "sr"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9576561462374611}}]}]}

Aleksandar/distilbert-srb-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #token-classification #generated_from_trainer #sr #dataset-wikiann #autotrain_compatible #endpoints_compatible #region-us

distilbert-srb-ner ================== This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set: * Loss: 0.2972 * Precision: 0.8871 * Recall: 0.9100 * F1: 0.8984 * Accuracy: 0.9577 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0 * Datasets 1.11.0 * Tokenizers 0.10.1

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-srb-ner-setimes This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2804 - Precision: 0.8286 - Recall: 0.8081 - F1: 0.8182 - Accuracy: 0.9547 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 104 | 0.2981 | 0.6737 | 0.6113 | 0.6410 | 0.9174 | | No log | 2.0 | 208 | 0.2355 | 0.7279 | 0.6701 | 0.6978 | 0.9307 | | No log | 3.0 | 312 | 0.2079 | 0.7707 | 0.7062 | 0.7371 | 0.9402 | | No log | 4.0 | 416 | 0.2078 | 0.7689 | 0.7479 | 0.7582 | 0.9449 | | 0.2391 | 5.0 | 520 | 0.2089 | 0.8083 | 0.7476 | 0.7767 | 0.9484 | | 0.2391 | 6.0 | 624 | 0.2199 | 0.7981 | 0.7726 | 0.7851 | 0.9487 | | 0.2391 | 7.0 | 728 | 0.2528 | 0.8205 | 0.7749 | 0.7971 | 0.9511 | | 0.2391 | 8.0 | 832 | 0.2265 | 0.8074 | 0.8003 | 0.8038 | 0.9524 | | 0.2391 | 9.0 | 936 | 0.2843 | 0.8265 | 0.7716 | 0.7981 | 0.9504 | | 0.0378 | 10.0 | 1040 | 0.2450 | 0.8024 | 0.8019 | 0.8021 | 0.9520 | | 0.0378 | 11.0 | 1144 | 0.2550 | 0.8116 | 0.7986 | 0.8051 | 0.9519 | | 0.0378 | 12.0 | 1248 | 0.2706 | 0.8208 | 0.7957 | 0.8081 | 0.9532 | | 0.0378 | 13.0 | 1352 | 0.2664 | 0.8040 | 0.8035 | 0.8038 | 0.9530 | | 0.0378 | 14.0 | 1456 | 0.2571 | 0.8011 | 0.8110 | 0.8060 | 0.9529 | | 0.0099 | 15.0 | 1560 | 0.2673 | 0.8051 | 0.8129 | 0.8090 | 0.9534 | | 0.0099 | 16.0 | 1664 | 0.2733 | 0.8074 | 0.8087 | 0.8081 | 0.9529 | | 0.0099 | 17.0 | 1768 | 0.2835 | 0.8254 | 0.8074 | 0.8163 | 0.9543 | | 0.0099 | 18.0 | 1872 | 0.2771 | 0.8222 | 0.8081 | 0.8151 | 0.9545 | | 0.0099 | 19.0 | 1976 | 0.2776 | 0.8237 | 0.8084 | 0.8160 | 0.9546 | | 0.0044 | 20.0 | 2080 | 0.2804 | 0.8286 | 0.8081 | 0.8182 | 0.9547 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "electra-srb-ner-setimes", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9546789604788638}}]}]}

Aleksandar/electra-srb-ner-setimes

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #electra #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

electra-srb-ner-setimes ======================= This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.2804 * Precision: 0.8286 * Recall: 0.8081 * F1: 0.8182 * Accuracy: 0.9547 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0 * Datasets 1.11.0 * Tokenizers 0.10.1

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-srb-ner This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.3406 - Precision: 0.8934 - Recall: 0.9087 - F1: 0.9010 - Accuracy: 0.9568 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3686 | 1.0 | 625 | 0.2108 | 0.8326 | 0.8494 | 0.8409 | 0.9335 | | 0.1886 | 2.0 | 1250 | 0.1784 | 0.8737 | 0.8713 | 0.8725 | 0.9456 | | 0.1323 | 3.0 | 1875 | 0.1805 | 0.8654 | 0.8870 | 0.8760 | 0.9468 | | 0.0675 | 4.0 | 2500 | 0.2018 | 0.8736 | 0.8880 | 0.8807 | 0.9502 | | 0.0425 | 5.0 | 3125 | 0.2162 | 0.8818 | 0.8945 | 0.8881 | 0.9512 | | 0.0343 | 6.0 | 3750 | 0.2492 | 0.8790 | 0.8928 | 0.8859 | 0.9513 | | 0.0253 | 7.0 | 4375 | 0.2562 | 0.8821 | 0.9006 | 0.8912 | 0.9525 | | 0.0142 | 8.0 | 5000 | 0.2788 | 0.8807 | 0.9013 | 0.8909 | 0.9524 | | 0.0114 | 9.0 | 5625 | 0.2793 | 0.8861 | 0.9002 | 0.8931 | 0.9534 | | 0.0095 | 10.0 | 6250 | 0.2967 | 0.8887 | 0.9034 | 0.8960 | 0.9550 | | 0.008 | 11.0 | 6875 | 0.2993 | 0.8899 | 0.9067 | 0.8982 | 0.9556 | | 0.0048 | 12.0 | 7500 | 0.3215 | 0.8887 | 0.9038 | 0.8962 | 0.9545 | | 0.0034 | 13.0 | 8125 | 0.3242 | 0.8897 | 0.9068 | 0.8982 | 0.9554 | | 0.003 | 14.0 | 8750 | 0.3311 | 0.8884 | 0.9085 | 0.8983 | 0.9559 | | 0.0025 | 15.0 | 9375 | 0.3383 | 0.8943 | 0.9062 | 0.9002 | 0.9562 | | 0.0011 | 16.0 | 10000 | 0.3346 | 0.8941 | 0.9112 | 0.9026 | 0.9574 | | 0.0015 | 17.0 | 10625 | 0.3362 | 0.8944 | 0.9081 | 0.9012 | 0.9567 | | 0.001 | 18.0 | 11250 | 0.3464 | 0.8877 | 0.9100 | 0.8987 | 0.9559 | | 0.0012 | 19.0 | 11875 | 0.3415 | 0.8944 | 0.9089 | 0.9016 | 0.9568 | | 0.0005 | 20.0 | 12500 | 0.3406 | 0.8934 | 0.9087 | 0.9010 | 0.9568 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "electra-srb-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "sr"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9568394937134688}}]}]}

Aleksandar/electra-srb-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #electra #token-classification #generated_from_trainer #dataset-wikiann #autotrain_compatible #endpoints_compatible #region-us

electra-srb-ner =============== This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set: * Loss: 0.3406 * Precision: 0.8934 * Recall: 0.9087 * F1: 0.9010 * Accuracy: 0.9568 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0 * Datasets 1.11.0 * Tokenizers 0.10.1

fill-mask

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-srb-oscar This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

{"tags": ["generated_from_trainer"], "model_index": [{"name": "electra-srb-oscar", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]}

Aleksandar/electra-srb-oscar

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #electra #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us

# electra-srb-oscar This model is a fine-tuned version of [](URL on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0 - Datasets 1.11.0 - Tokenizers 0.10.1

question-answering

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # herbert-base-cased-finetuned-squad This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2071 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 233 | 1.2474 | | No log | 2.0 | 466 | 1.1951 | | 1.3459 | 3.0 | 699 | 1.2071 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "herbert-base-cased-finetuned-squad", "results": []}]}

Aleksandra/herbert-base-cased-finetuned-squad

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us

herbert-base-cased-finetuned-squad ================================== This model is a fine-tuned version of allegro/herbert-base-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.2071 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3

text-classification

transformers

# xlm-roberta-en-ru-emoji - Problem type: Multi-class Classification

{"language": ["en", "ru"], "datasets": ["tweet_eval"], "model_index": [{"name": "xlm-roberta-en-ru-emoji", "results": [{"task": {"name": "Sentiment Analysis", "type": "sentiment-analysis"}, "dataset": {"name": "Tweet Eval", "type": "tweet_eval", "args": "emoji"}}]}], "widget": [{"text": "\u041e\u0442\u043b\u0438\u0447\u043d\u043e!"}, {"text": "Awesome!"}, {"text": "lol"}]}

adorkin/xlm-roberta-en-ru-emoji

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #xlm-roberta #text-classification #en #ru #dataset-tweet_eval #autotrain_compatible #endpoints_compatible #region-us

# xlm-roberta-en-ru-emoji - Problem type: Multi-class Classification

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5316 - Accuracy: 0.2936 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.5355 | 1.0 | 6195 | 1.5339 | 0.2923 | | 1.5248 | 2.0 | 12390 | 1.5316 | 0.2936 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert", "results": []}]}

AlekseyKorshuk/bert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

bert ==== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.5316 * Accuracy: 0.2936 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 4 * total\_train\_batch\_size: 64 * total\_eval\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.20.1 * Pytorch 1.10.1+cu113 * Datasets 2.3.2 * Tokenizers 0.12.1

text2text-generation

transformers

**Usage HuggingFace Transformers for header generation task** ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-HeaderGeneration") tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large') input_text # your text input_ = tokenizer.batch_encode_plus([input_text], max_length=1024, pad_to_max_length=True, truncation=True, padding='longest', return_tensors='pt') input_ids = input_['input_ids'] input_mask = input_['attention_mask'] headers = model.generate(input_ids=input_ids, attention_mask=input_mask, num_beams=32, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=10) headers = tokenizer.batch_decode(headers, skip_special_tokens=True) ``` **Decoder configuration examples:** [**Input text you can see here**](https://www.bbc.com/news/science-environment-59775105) ``` headers = model.generate(input_ids=input_ids, attention_mask=input_mask, num_beams=32, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=20) tokenizer.batch_decode(headers, skip_special_tokens=True) ``` output: 1. *the impact of climate change on tropical cyclones* 2. *the impact of human induced climate change on tropical cyclones* 3. *the impact of climate change on tropical cyclone formation in the midlatitudes* 4. *how climate change will expand the range of tropical cyclones?* 5. *the impact of climate change on tropical cyclones in the midlatitudes* 6. *global warming will expand the range of tropical cyclones* 7. *climate change will expand the range of tropical cyclones* 8. *the impact of climate change on tropical cyclone formation* 9. *the impact of human induced climate change on tropical cyclone formation* 10. *tropical cyclones in the mid-latitudes* 11. *climate change will expand the range of tropical cyclones in the middle latitudes* 12. *global warming will expand the range of tropical cyclones, a new study says* 13. *the impacts of climate change on tropical cyclones* 14. *the impact of global warming on tropical cyclones* 15. *climate change will expand the range of tropical cyclones, a new study says* 16. *global warming will expand the range of tropical cyclones in the middle latitudes* 17. *the effects of climate change on tropical cyclones* 18. *how climate change will expand the range of tropical cyclones* 19. *climate change will expand the range of tropical cyclones over the equator* 20. *the impact of human induced climate change on tropical cyclones.* Also you can play with the following parameters in generate method: -top_k -top_p [**Meaning of parameters to generate text you can see here**](https://huggingface.co/blog/how-to-generate)

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AlekseyKulnevich/Pegasus-HeaderGeneration

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us

Usage HuggingFace Transformers for header generation task Decoder configuration examples: Input text you can see here output: 1. *the impact of climate change on tropical cyclones* 2. *the impact of human induced climate change on tropical cyclones* 3. *the impact of climate change on tropical cyclone formation in the midlatitudes* 4. *how climate change will expand the range of tropical cyclones?* 5. *the impact of climate change on tropical cyclones in the midlatitudes* 6. *global warming will expand the range of tropical cyclones* 7. *climate change will expand the range of tropical cyclones* 8. *the impact of climate change on tropical cyclone formation* 9. *the impact of human induced climate change on tropical cyclone formation* 10. *tropical cyclones in the mid-latitudes* 11. *climate change will expand the range of tropical cyclones in the middle latitudes* 12. *global warming will expand the range of tropical cyclones, a new study says* 13. *the impacts of climate change on tropical cyclones* 14. *the impact of global warming on tropical cyclones* 15. *climate change will expand the range of tropical cyclones, a new study says* 16. *global warming will expand the range of tropical cyclones in the middle latitudes* 17. *the effects of climate change on tropical cyclones* 18. *how climate change will expand the range of tropical cyclones* 19. *climate change will expand the range of tropical cyclones over the equator* 20. *the impact of human induced climate change on tropical cyclones.* Also you can play with the following parameters in generate method: -top_k -top_p Meaning of parameters to generate text you can see here