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tags: |
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- text-2-text-generation |
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- t5 |
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--- |
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# Model Card for t5_sentence_paraphraser |
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# Model Details |
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## Model Description |
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Using this model you can generate paraphrases of any given question. |
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- **Developed by:** Ramsri Goutham Golla |
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- **Shared by [Optional]:** Ramsri Goutham Golla |
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- **Model type:** Text2Text Generation |
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- **Language(s) (NLP):** More information needed |
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- **License:** More information needed |
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- **Parent Model:** [All T5 Checkpoints](https://huggingface.co/models?search=t5) |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/ramsrigouthamg/Paraphrase-any-question-with-T5-Text-To-Text-Transfer-Transformer-) |
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- [Blog Post](https://towardsdatascience.com/paraphrase-any-question-with-t5-text-to-text-transfer-transformer-pretrained-model-and-cbb9e35f1555) |
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# Uses |
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## Direct Use |
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This model can be used for the task of Text2Text Generation. |
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## Downstream Use [Optional] |
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More information needed. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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The developers also write in a [blog post](https://towardsdatascience.com/paraphrase-any-question-with-t5-text-to-text-transfer-transformer-pretrained-model-and-cbb9e35f1555) that the model: |
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> [Quora Question Pairs](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset to collect all the questions marked as **duplicates** and prepared training and validation sets. Questions that are duplicates serve our purpose of getting **paraphrase** pairs. |
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## Training Procedure |
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The developers also write in a [blog post](https://towardsdatascience.com/paraphrase-any-question-with-t5-text-to-text-transfer-transformer-pretrained-model-and-cbb9e35f1555) that the model: |
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> I trained T5 with the **original sentence** as **input** and **paraphrased** (duplicate sentence from Quora Question pairs) sentence as **output**. |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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More information needed |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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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). |
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- **Hardware Type:** p2.xlarge |
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- **Hours used:** ~20 hrs |
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- **Cloud Provider:** AWS ec2 |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed. |
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# Citation |
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**BibTeX:** |
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More information needed |
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**APA:** |
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More information needed |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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Ramsri Goutham Golla in collaboration with Ezi Ozoani and the Hugging Face team |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5_sentence_paraphraser") |
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model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5_sentence_paraphraser") |
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``` |
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See the [blog post](https://towardsdatascience.com/paraphrase-any-question-with-t5-text-to-text-transfer-transformer-pretrained-model-and-cbb9e35f1555) and this [Colab Notebook](https://colab.research.google.com/drive/176NSaYjc2eeI-78oLH_F9-YV3po3qQQO?usp=sharing#scrollTo=SDVQ04fGRb1v) for more examples. |
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</details> |
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