# Model Card for  diabetes-t5-small 
 
 
# Model Details
 
## Model Description
 
- **Developed by:** UCI NLP
- **Shared by [Optional]:** More information needed
- **Model type:** Text2Text Generation
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Related Models:** T5-small
  - **Parent Model:** T5
- **Resources for more information:** 
    - [Associated Paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf)
 	 - [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) 
 	 - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer)
  	- [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5)
  
 
 
# 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 is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5.
 
The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**.
See the [T5-small model card](https://huggingface.co/t5-small?text=My+name+is+Wolfgang+and+I+live+in+Berlin) for further training data details.
 
## Training Procedure
 
 
### Preprocessing
 
More information needed
 
### Speeds, Sizes, Times
 
More information needed
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed
 
### Factors
 
 
### 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:**
```bibtex
@article{2020t5,
  author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
  title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {140},
  pages   = {1-67},
  url     = {http://jmlr.org/papers/v21/20-074.html}
}
```
 
 
**APA:**
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.
 
 
# Glossary [optional]
More information needed
 
# More Information [optional]
 
More information needed
 
# Model Card Authors [optional]
 
 
UCI NLPin 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("ucinlp/diabetes-t5-small")
 
model = AutoModelForSeq2SeqLM.from_pretrained("ucinlp/diabetes-t5-small")
 
```
 
</details>


{"tags": ["t5"]}
transformers pytorch t5 text2text-generation arxiv:1910.09700 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# Model Card for  diabetes-t5-small 
 
 
# Model Details
 
## Model Description
 
- Developed by: UCI NLP
- Shared by [Optional]: More information needed
- Model type: Text2Text Generation
- Language(s) (NLP): More information needed
- License: More information needed
- Related Models: T5-small
  - Parent Model: T5
- Resources for more information: 
    - Associated Paper
 	 - Google's T5 Blog Post 
 	 - GitHub Repo
  	- Hugging Face T5 Docs
  
 
 
# 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) and Bender et al. (2021)). 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 is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.
 
The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).
See the T5-small model card for further training data details.
 
## Training Procedure
 
 
### Preprocessing
 
More information needed
 
### Speeds, Sizes, Times
 
More information needed
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed
 
### Factors
 
 
### 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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX:

 
 
APA:
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.
 
 
# Glossary [optional]
More information needed
 
# More Information [optional]
 
More information needed
 
# Model Card Authors [optional]
 
 
UCI NLPin 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>


 
</details>


# Model Card for  diabetes-t5-small # Model Details ## Model Description
 
- Developed by: UCI NLP
- Shared by [Optional]: More information needed
- Model type: Text2Text Generation
- Language(s) (NLP): More information needed
- License: More information needed
- Related Models: T5-small
  - Parent Model: T5
- Resources for more information: 
    - Associated Paper
 	 - Google's T5 Blog Post 
 	 - GitHub Repo
  	- Hugging Face T5 Docs # 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) and Bender et al. (2021)). 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 is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.
 
The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).
See the T5-small model card for further training data details. ## Training Procedure ### Preprocessing
 
More information needed ### Speeds, Sizes, Times
 
More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data
 
More information needed ### Factors ### 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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX:

 
 
APA:
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. # Glossary [optional]
More information needed # More Information [optional]
 
More information needed # Model Card Authors [optional]
 
 
UCI NLPin 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>


 
</details>
TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for  diabetes-t5-small # Model Details ## Model Description
 
- Developed by: UCI NLP
- Shared by [Optional]: More information needed
- Model type: Text2Text Generation
- Language(s) (NLP): More information needed
- License: More information needed
- Related Models: T5-small
  - Parent Model: T5
- Resources for more information: 
    - Associated Paper
 	 - Google's T5 Blog Post 
 	 - GitHub Repo
  	- Hugging Face T5 Docs # 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) and Bender et al. (2021)). 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 is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.
 
The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).
See the T5-small model card for further training data details. ## Training Procedure ### Preprocessing
 
More information needed ### Speeds, Sizes, Times
 
More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data
 
More information needed ### Factors ### 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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX:

 
 
APA:
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. # Glossary [optional]
More information needed # More Information [optional]
 
More information needed # Model Card Authors [optional]
 
 
UCI NLPin 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>


 
</details>
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Model Card for  diabetes-t5-small# Model Details## Model Description
 
- Developed by: UCI NLP
- Shared by [Optional]: More information needed
- Model type: Text2Text Generation
- Language(s) (NLP): More information needed
- License: More information needed
- Related Models: T5-small
  - Parent Model: T5
- Resources for more information: 
    - Associated Paper
 	 - Google's T5 Blog Post 
 	 - GitHub Repo
  	- Hugging Face T5 Docs# 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) and Bender et al. (2021)). 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 is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.
 
The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).
See the T5-small model card for further training data details.## Training Procedure### Preprocessing
 
More information needed### Speeds, Sizes, Times
 
More information needed# Evaluation## Testing Data, Factors & Metrics### Testing Data
 
More information needed### Factors### Metrics
 
More information needed