---
tags:
- text-2-text-generation
- t5
---

# Model Card for t5_sentence_paraphraser
 
# Model Details
 
## Model Description
 

Using this model you can generate paraphrases of any given question.


 
- **Developed by:** Ramsri Goutham Golla
- **Shared by [Optional]:** Ramsri Goutham Golla
- **Model type:** Text2Text Generation
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:** [All T5 Checkpoints](https://huggingface.co/models?search=t5)
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/ramsrigouthamg/Paraphrase-any-question-with-T5-Text-To-Text-Transfer-Transformer-)
   - [Blog Post](https://towardsdatascience.com/paraphrase-any-question-with-t5-text-to-text-transfer-transformer-pretrained-model-and-cbb9e35f1555)
# 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 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: 
 
>  [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.
 
 
 
## Training Procedure

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: 
 
> I trained T5 with the **original sentence** as **input** and **paraphrased** (duplicate sentence from Quora Question pairs) sentence as **output**.
 
### 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:** p2.xlarge
- **Hours used:** ~20 hrs
- **Cloud Provider:** AWS ec2
- **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





**APA:**

More information needed
  
# Glossary [optional]
 
More information needed

# More Information [optional]
More information needed 

# Model Card Authors [optional]
 
Ramsri Goutham Golla  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("ramsrigouthamg/t5_sentence_paraphraser")

model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5_sentence_paraphraser")
 ```

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.
</details>