Model Card for ke-t5-base

Model Details

Model Description

The developers of the Text-To-Text Transfer Transformer (T5) write:

With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.

T5-Base is the checkpoint with 220 million parameters.

Developed by: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
Shared by [Optional]: Korea Electronics Technology Institute Artificial Intelligence Research Center
Model type: Text Generation
**Language(s) (NLP):**More information needed
License: More information needed
Related Models:
- Parent Model: T5
Resources for more information:

Uses

Direct Use

The developers write in a blog post that the model:

Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself

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-base model card for further information.

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

The developers evaluated the model on 24 tasks, see the research paper for full details.

Factors

More information needed

Metrics

More information needed

Results

For full results for T5-Base, see the research paper, Table 14.

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: Google Cloud TPU Pods
Hours used: More information needed
Cloud Provider: GCP
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:

@inproceedings{kim-etal-2021-model-cross,
    title = "A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems",
    author = "Kim, San  and
      Jang, Jin Yea  and
      Jung, Minyoung  and
      Shin, Saim",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.33",
    doi = "10.18653/v1/2021.findings-emnlp.33",
    pages = "352--365",
    abstract = "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.",
}

@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]

Korea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Model Database team

Model Card Contact

More information needed

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("KETI-AIR/ke-t5-base")

model = AutoModelForSeq2SeqLM.from_pretrained("KETI-AIR/ke-t5-base")

See the Model Database T5 docs and a Colab Notebook created by the model developers for more examples.