Added model cards for Tagalog BERT models (#7603)

model_cards/jcblaise/bert-tagalog-base-cased-WWM/README.md

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+---
+language: tl
+tags:
+- bert
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# BERT Tagalog Base Cased (Whole Word Masking)
+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.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased-WWM', do_lower_case=False)
+```
+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
+
+## Citations
+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:
+
+```
+@inproceedings{localization2020cruz,
+  title={{Localization of Fake News Detection via Multitask Transfer Learning}},
+  author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+  booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+  pages={2589--2597},
+  year={2020},
+  url={https://www.aclweb.org/anthology/2020.lrec-1.315}
+}
+
+@article{cruz2020establishing,
+  title={Establishing Baselines for Text Classification in Low-Resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:2005.02068},
+  year={2020}
+}
+
+@article{cruz2019evaluating,
+  title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:1907.00409},
+  year={2019}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## 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

model_cards/jcblaise/bert-tagalog-base-cased/README.md

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+---
+language: tl
+tags:
+- bert
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# BERT Tagalog Base Cased
+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.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-cased')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-cased', do_lower_case=False)
+```
+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
+
+## Citations
+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:
+
+```
+@inproceedings{localization2020cruz,
+  title={{Localization of Fake News Detection via Multitask Transfer Learning}},
+  author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+  booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+  pages={2589--2597},
+  year={2020},
+  url={https://www.aclweb.org/anthology/2020.lrec-1.315}
+}
+
+@article{cruz2020establishing,
+  title={Establishing Baselines for Text Classification in Low-Resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:2005.02068},
+  year={2020}
+}
+
+@article{cruz2019evaluating,
+  title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:1907.00409},
+  year={2019}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## 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

model_cards/jcblaise/bert-tagalog-base-uncased-WWM/README.md

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+---
+language: tl
+tags:
+- bert
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# BERT Tagalog Base Uncased (Whole Word Masking)
+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.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM', do_lower_case=True)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased-WWM', do_lower_case=True)
+```
+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
+
+## Citations
+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:
+
+```
+@inproceedings{localization2020cruz,
+  title={{Localization of Fake News Detection via Multitask Transfer Learning}},
+  author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+  booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+  pages={2589--2597},
+  year={2020},
+  url={https://www.aclweb.org/anthology/2020.lrec-1.315}
+}
+
+@article{cruz2020establishing,
+  title={Establishing Baselines for Text Classification in Low-Resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:2005.02068},
+  year={2020}
+}
+
+@article{cruz2019evaluating,
+  title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:1907.00409},
+  year={2019}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## 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

model_cards/jcblaise/bert-tagalog-base-uncased/README.md

@@ -0,0 +1,62 @@
+---
+language: tl
+tags:
+- bert
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# BERT Tagalog Base Uncased
+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.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased', do_lower_case=True)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/bert-tagalog-base-uncased')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/bert-tagalog-base-uncased', do_lower_case=True)
+```
+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
+
+## Citations
+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:
+
+```
+@inproceedings{localization2020cruz,
+  title={{Localization of Fake News Detection via Multitask Transfer Learning}},
+  author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+  booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+  pages={2589--2597},
+  year={2020},
+  url={https://www.aclweb.org/anthology/2020.lrec-1.315}
+}
+
+@article{cruz2020establishing,
+  title={Establishing Baselines for Text Classification in Low-Resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:2005.02068},
+  year={2020}
+}
+
+@article{cruz2019evaluating,
+  title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:1907.00409},
+  year={2019}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## 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

model_cards/jcblaise/distilbert-tagalog-base-cased/README.md

@@ -0,0 +1,63 @@
+---
+language: tl
+tags:
+- distilbert
+- bert
+- tagalog
+- filipino
+license: gpl-3.0
+inference: false
+---
+
+# DistilBERT Tagalog Base Cased
+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.
+
+## Usage
+The model can be loaded and used in both PyTorch and TensorFlow through the HuggingFace Transformers package.
+
+```python
+from transformers import TFAutoModel, AutoModel, AutoTokenizer
+
+# TensorFlow
+model = TFAutoModel.from_pretrained('jcblaise/distilbert-tagalog-base-cased', from_pt=True)
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/distilbert-tagalog-base-cased', do_lower_case=False)
+
+# PyTorch
+model = AutoModel.from_pretrained('jcblaise/distilbert-tagalog-base-cased')
+tokenizer = AutoTokenizer.from_pretrained('jcblaise/distilbert-tagalog-base-cased', do_lower_case=False)
+```
+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
+
+## Citations
+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:
+
+```
+@inproceedings{localization2020cruz,
+  title={{Localization of Fake News Detection via Multitask Transfer Learning}},
+  author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+  booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+  pages={2589--2597},
+  year={2020},
+  url={https://www.aclweb.org/anthology/2020.lrec-1.315}
+}
+
+@article{cruz2020establishing,
+  title={Establishing Baselines for Text Classification in Low-Resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:2005.02068},
+  year={2020}
+}
+
+@article{cruz2019evaluating,
+  title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:1907.00409},
+  year={2019}
+}
+```
+
+## Data and Other Resources
+Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
+
+## 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