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Added model cards for Tagalog BERT models (#7603)
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Blaise Cruz
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62
model_cards/jcblaise/bert-tagalog-base-cased-WWM/README.md
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62
model_cards/jcblaise/bert-tagalog-base-cased-WWM/README.md
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---
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language: tl
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tags:
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- bert
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- tagalog
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- filipino
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license: gpl-3.0
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inference: false
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---
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# BERT Tagalog Base Cased (Whole Word Masking)
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Tagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This particular version uses whole word masking.
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## Usage
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The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
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```python
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from transformers import TFAutoModel, AutoModel, AutoTokenizer
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# TensorFlow
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model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM', from_pt=True)
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM', do_lower_case=False)
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# PyTorch
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model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM')
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM', do_lower_case=False)
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```
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Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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## Citations
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All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
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```
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@inproceedings{localization2020cruz,
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title={{Localization of Fake News Detection via Multitask Transfer Learning}},
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author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
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booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
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pages={2589--2597},
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year={2020},
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url={https://www.aclweb.org/anthology/2020.lrec-1.315}
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}
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@article{cruz2020establishing,
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title={Establishing Baselines for Text Classification in Low-Resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:2005.02068},
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year={2020}
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}
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@article{cruz2019evaluating,
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title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:1907.00409},
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year={2019}
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}
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```
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## Data and Other Resources
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Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
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## Contact
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If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph
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62
model_cards/jcblaise/bert-tagalog-base-cased/README.md
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62
model_cards/jcblaise/bert-tagalog-base-cased/README.md
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@ -0,0 +1,62 @@
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---
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language: tl
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tags:
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- bert
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- tagalog
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- filipino
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license: gpl-3.0
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inference: false
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---
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# BERT Tagalog Base Cased
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Tagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
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## Usage
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The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
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```python
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from transformers import TFAutoModel, AutoModel, AutoTokenizer
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# TensorFlow
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model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased', from_pt=True)
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased', do_lower_case=False)
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# PyTorch
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model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased')
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased', do_lower_case=False)
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```
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Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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## Citations
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All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
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```
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@inproceedings{localization2020cruz,
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title={{Localization of Fake News Detection via Multitask Transfer Learning}},
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author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
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booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
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pages={2589--2597},
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year={2020},
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url={https://www.aclweb.org/anthology/2020.lrec-1.315}
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}
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@article{cruz2020establishing,
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title={Establishing Baselines for Text Classification in Low-Resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:2005.02068},
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year={2020}
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}
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@article{cruz2019evaluating,
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title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:1907.00409},
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year={2019}
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}
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```
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## Data and Other Resources
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Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
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## Contact
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If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph
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62
model_cards/jcblaise/bert-tagalog-base-uncased-WWM/README.md
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62
model_cards/jcblaise/bert-tagalog-base-uncased-WWM/README.md
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@ -0,0 +1,62 @@
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---
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language: tl
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tags:
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- bert
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- tagalog
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- filipino
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license: gpl-3.0
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inference: false
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---
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# BERT Tagalog Base Uncased (Whole Word Masking)
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Tagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This particular version uses whole word masking.
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## Usage
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The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
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```python
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from transformers import TFAutoModel, AutoModel, AutoTokenizer
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# TensorFlow
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model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM', from_pt=True)
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM', do_lower_case=True)
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# PyTorch
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model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM')
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM', do_lower_case=True)
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```
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Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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## Citations
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All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
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```
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@inproceedings{localization2020cruz,
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title={{Localization of Fake News Detection via Multitask Transfer Learning}},
|
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author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
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booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
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pages={2589--2597},
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year={2020},
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url={https://www.aclweb.org/anthology/2020.lrec-1.315}
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}
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@article{cruz2020establishing,
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title={Establishing Baselines for Text Classification in Low-Resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:2005.02068},
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year={2020}
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}
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@article{cruz2019evaluating,
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title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:1907.00409},
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year={2019}
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}
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```
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## Data and Other Resources
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Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
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## Contact
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If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph
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62
model_cards/jcblaise/bert-tagalog-base-uncased/README.md
Normal file
62
model_cards/jcblaise/bert-tagalog-base-uncased/README.md
Normal file
@ -0,0 +1,62 @@
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---
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language: tl
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tags:
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- bert
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- tagalog
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- filipino
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license: gpl-3.0
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inference: false
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---
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# BERT Tagalog Base Uncased
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Tagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
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|
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## Usage
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The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
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```python
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from transformers import TFAutoModel, AutoModel, AutoTokenizer
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# TensorFlow
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model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased', from_pt=True)
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased', do_lower_case=True)
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# PyTorch
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model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased')
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased', do_lower_case=True)
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```
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Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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## Citations
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All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
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```
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@inproceedings{localization2020cruz,
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title={{Localization of Fake News Detection via Multitask Transfer Learning}},
|
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author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
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booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
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pages={2589--2597},
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year={2020},
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url={https://www.aclweb.org/anthology/2020.lrec-1.315}
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}
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@article{cruz2020establishing,
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title={Establishing Baselines for Text Classification in Low-Resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:2005.02068},
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year={2020}
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}
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@article{cruz2019evaluating,
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title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
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journal={arXiv preprint arXiv:1907.00409},
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year={2019}
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}
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```
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## Data and Other Resources
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Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
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## Contact
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If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph
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63
model_cards/jcblaise/distilbert-tagalog-base-cased/README.md
Normal file
63
model_cards/jcblaise/distilbert-tagalog-base-cased/README.md
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@ -0,0 +1,63 @@
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---
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language: tl
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tags:
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- distilbert
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- bert
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- tagalog
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- filipino
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license: gpl-3.0
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inference: false
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---
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# DistilBERT Tagalog Base Cased
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Tagalog version of DistilBERT, distilled from [`bert-tagalog-base-cased`](https://huggingface.co/jcblaise/bert-tagalog-base-cased). This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
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## Usage
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The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
|
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```python
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from transformers import TFAutoModel, AutoModel, AutoTokenizer
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# TensorFlow
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model = TFAutoModel.from_pretrained('jcblaise/distilbert-tagalog-base-cased', from_pt=True)
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/distilbert-tagalog-base-cased', do_lower_case=False)
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# PyTorch
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model = AutoModel.from_pretrained('jcblaise/distilbert-tagalog-base-cased')
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tokenizer = AutoTokenizer.from_pretrained('jcblaise/distilbert-tagalog-base-cased', do_lower_case=False)
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```
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Finetuning scripts and other utilities we use for our projects can be found in our centralized repository at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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## Citations
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All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
|
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|
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```
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@inproceedings{localization2020cruz,
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title={{Localization of Fake News Detection via Multitask Transfer Learning}},
|
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author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
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booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
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pages={2589--2597},
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year={2020},
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url={https://www.aclweb.org/anthology/2020.lrec-1.315}
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}
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@article{cruz2020establishing,
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title={Establishing Baselines for Text Classification in Low-Resource Languages},
|
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
|
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journal={arXiv preprint arXiv:2005.02068},
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year={2020}
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}
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@article{cruz2019evaluating,
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title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
|
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author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
|
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journal={arXiv preprint arXiv:1907.00409},
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year={2019}
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}
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```
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## Data and Other Resources
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Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
|
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|
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## Contact
|
||||
If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at jan_christian_cruz@dlsu.edu.ph
|
||||
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