
# Model Card for EntityT5
 
# Model Details
 
## Model Description
 
T5+Trie to predict entities of the last sentence in a dialogue.
 
- **Developed by:** Jaren Yang
- **Shared by [Optional]:** Jaren Yang
- **Model type:** Text2text Generation 
- **Language(s) (NLP):** More information needed
- **License:** More information needed 
- **Parent Model:** T5
- **Resources for more information:** More information needed 
 	


# 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
 
More information needed 
 
## 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]
 
Jaren Yang 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("Jaren/EntityT5")

model = AutoModelForSeq2SeqLM.from_pretrained("Jaren/EntityT5")
 ```
</details>

{"tags": ["text-2-text-generation", "t5"]}
transformers pytorch t5 text2text-generation text-2-text-generation arxiv:1910.09700 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #text-2-text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Model Card for EntityT5
 
# Model Details
 
## Model Description
 
T5+Trie to predict entities of the last sentence in a dialogue.
 
- Developed by: Jaren Yang
- Shared by [Optional]: Jaren Yang
- Model type: Text2text Generation 
- Language(s) (NLP): More information needed
- License: More information needed 
- Parent Model: T5
- Resources for more information: More information needed 
 	


# 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
 
More information needed 
 
## 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 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:

More information needed.
 
 
 
 
# Glossary [optional]
More information needed 
 
# More Information [optional]
More information needed 

 
# Model Card Authors [optional]
 
Jaren Yang 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>


</details>

# Model Card for EntityT5 # Model Details ## Model Description
 
T5+Trie to predict entities of the last sentence in a dialogue.
 
- Developed by: Jaren Yang
- Shared by [Optional]: Jaren Yang
- Model type: Text2text Generation 
- Language(s) (NLP): More information needed
- License: More information needed 
- Parent Model: T5
- Resources for more information: More information needed # 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
 
More information needed ## 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 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:

More information needed. # Glossary [optional]
More information needed # More Information [optional]
More information needed # Model Card Authors [optional]
 
Jaren Yang 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>


</details>
TAGS
#transformers #pytorch #t5 #text2text-generation #text-2-text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for EntityT5 # Model Details ## Model Description
 
T5+Trie to predict entities of the last sentence in a dialogue.
 
- Developed by: Jaren Yang
- Shared by [Optional]: Jaren Yang
- Model type: Text2text Generation 
- Language(s) (NLP): More information needed
- License: More information needed 
- Parent Model: T5
- Resources for more information: More information needed # 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
 
More information needed ## 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 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:

More information needed. # Glossary [optional]
More information needed # More Information [optional]
More information needed # Model Card Authors [optional]
 
Jaren Yang 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>


</details>
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #text-2-text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Model Card for EntityT5# Model Details## Model Description
 
T5+Trie to predict entities of the last sentence in a dialogue.
 
- Developed by: Jaren Yang
- Shared by [Optional]: Jaren Yang
- Model type: Text2text Generation 
- Language(s) (NLP): More information needed
- License: More information needed 
- Parent Model: T5
- Resources for more information: More information needed# 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
 
More information needed## 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 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]