—
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>