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—
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>
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