| — | |
| license: mit | |
| tags: | |
| - text-2-text-generation | |
| --- | |
| # Model Card for Key Phrase Transformer | |
| # Model Details | |
| ## Model Description | |
| KeyPhraseTransformer lets you quickly extract key phrases, topics, themes from your text data with T5 transformer | |
| - **Developed by:** Shivanand Roy | |
| - **Shared by [Optional]:** Shivanand Roy | |
| - **Model type:** Text2Text Generation | |
| - **Language(s) (NLP):** More information needed | |
| - **License:** MIT | |
| - **Parent Model:** T5 | |
| - **Resources for more information:** | |
| - [GitHub Repo](https://github.com/Shivanandroy/KeyPhraseTransformer) | |
| - [Blog Post](https://snrspeaks.medium.com/keyphrasetransformer-quickly-extract-keyphrases-topics-from-text-documents-with-t5-transformer-dfb819716c23) | |
| # Uses | |
| ## Direct Use | |
| This model can be used for the task of text2text generation. | |
| ## Downstream Use [Optional] | |
| More information needed. | |
| ## Out-of-Scope Use | |
| The model should not be used to intentionally create hostile or alienating environments for people. | |
| # Bias, Risks, and Limitations | |
| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. | |
| ## Recommendations | |
| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. | |
| # Training Details | |
| ## Training Data | |
| The model authors notes in the [GitHub Repo](https://github.com/Shivanandroy/KeyPhraseTransformer): | |
| > Trained on 500,000 training samples | |
| ## Training Procedure | |
| ### Preprocessing | |
| More information needed | |
| ### Speeds, Sizes, Times | |
| More information needed | |
| # Evaluation | |
| ## Testing Data, Factors & Metrics | |
| ### Testing Data | |
| More information needed | |
| ### Factors | |
| More information needed | |
| ### Metrics | |
| More information needed | |
| ## Results | |
| More information needed | |
| # Model Examination | |
| More information needed | |
| # Environmental Impact | |
| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). | |
| - **Hardware Type:** More information needed | |
| - **Hours used:** More information needed | |
| - **Cloud Provider:** More information needed | |
| - **Compute Region:** More information needed | |
| - **Carbon Emitted:** More information needed | |
| # Technical Specifications [optional] | |
| ## Model Architecture and Objective | |
| More information needed | |
| ## Compute Infrastructure | |
| More information needed | |
| ### Hardware | |
| More information needed | |
| ### Software | |
| More information needed. | |
| # Citation | |
| **BibTeX:** | |
| More information needed. | |
| # Glossary [optional] | |
| More information needed | |
| # More Information [optional] | |
| More information needed | |
| # Model Card Authors [optional] | |
| Shivanand Roy in collaboration with Ezi Ozoani and the Hugging Face team | |
| # Model Card Contact | |
| More information needed | |
| # How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| <details> | |
| <summary> Click to expand </summary> | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("snrspeaks/KeyPhraseTransformer") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("snrspeaks/KeyPhraseTransformer") | |
| ``` | |
| </details> | |