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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

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Results

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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

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Hardware

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Software

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Citation

BibTeX:

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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

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How to Get Started with the Model

Use the code below to get started with the model.

Click to expand
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Jaren/EntityT5")

model = AutoModelForSeq2SeqLM.from_pretrained("Jaren/EntityT5")
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