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The Electra-Base-Discriminator model is a language model that has been trained using unsupervised learning techniques and the ELECTRA architecture. Its purpose is to discriminate between real and generated tokens in a given sequence of text. This model can be used as part of a larger system for tasks such as text generation, text classification, and natural language understanding.


	
		
	
	
		Model Card for FLAN-T5 large
	





	
		
	
	
		Table of Contents
	



TL;DR
Model Details
Usage
Uses
Bias, Risks, and Limitations
Training Details
Evaluation
Environmental Impact
Citation
Model Card Authors



	
		
	
	
		TL;DR
	


If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. 
As mentioned in the first few lines of the abstract : 

Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card.


	
		
	
	
		Model Details
	



	
		
	
	
		Model Description
	



Model type: Language model
Language(s) (NLP): English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
License: Apache 2.0
Related Models: All FLAN-T5 Checkpoints
Original Checkpoints: All Original FLAN-T5 Checkpoints
Resources for more information:
Research paper
GitHub Repo
Hugging Face FLAN-T5 Docs (Similar to T5) 





	
		
	
	
		Usage
	


Find below some example scripts on how to use the model in transformers:


	
		
	
	
		Using the Pytorch model
	



	
		
	
	
		Running the model on a CPU
	



	
		
	
	
		Running the model on a GPU
	



	
		
	
	
		Running the model on a GPU using different precisions
	



	
		
	
	
		Uses
	



	
		
	
	
		Direct Use and Downstream Use
	


The authors write in the original paper's model card that: 

See the research paper for further details.


	
		
	
	
		Out-of-Scope Use
	


More information needed.


	
		
	
	
		Bias, Risks, and Limitations
	


The information below in this section are copied from the model's official model card:


	
		
	
	
		Ethical considerations and risks
	



	
		
	
	
		Known Limitations
	



	
		
	
	
		Sensitive Use:
	



	
		
	
	
		Training Details
	



	
		
	
	
		Training Data
	


The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):




	
		
	
	
		Training Procedure
	


According to the model card from the original paper:

The model has been trained on TPU v3 or TPU v4 pods, using t5x codebase together with jax.


	
		
	
	
		Evaluation
	



	
		
	
	
		Testing Data, Factors & Metrics
	


The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:

For full details, please check the research paper.


	
		
	
	
		Results
	


For full results for FLAN-T5-Large, see the research paper, Table 3.


	
		
	
	
		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 - TPU v3 or TPU v4  | Number of chips ≥ 4.
Hours used: More information needed
Cloud Provider: GCP
Compute Region: More information needed
Carbon Emitted: More information needed



	
		
	
	
		Citation
	


BibTeX:



FLAN-T5-Large is a language model that has been fine-tuned on more than 1000 additional tasks and covers multiple languages. It is an improved version of the T5 model, offering better performance with the same number of parameters. The model can be used for various NLP tasks and is available for use in transformers. There are no specific out-of-scope uses mentioned, and the model's training details indicate that it was trained on TPU v3 or TPU v4 pods using the T5X codebase with JAX. The model has been evaluated on various tasks and languages, and the results can be found in the research paper. The environmental impact of the model has not been specified.

The flan-t5-xxl model is a text-to-text generation model. It takes in a specific prompt or input text and generates a corresponding output text based on the given prompt. The model is designed to handle large amounts of text and generate high-quality responses.


	
		
	
	
		Model Card for FLAN-T5 base
	





	
		
	
	
		Table of Contents
	



TL;DR
Model Details
Usage
Uses
Bias, Risks, and Limitations
Training Details
Evaluation
Environmental Impact
Citation
Model Card Authors



	
		
	
	
		TL;DR
	


If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. 
As mentioned in the first few lines of the abstract : 

Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card.


	
		
	
	
		Model Details
	



	
		
	
	
		Model Description
	



Model type: Language model
Language(s) (NLP): English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
License: Apache 2.0
Related Models: All FLAN-T5 Checkpoints
Original Checkpoints: All Original FLAN-T5 Checkpoints
Resources for more information:
Research paper
GitHub Repo
Hugging Face FLAN-T5 Docs (Similar to T5) 





	
		
	
	
		Usage
	


Find below some example scripts on how to use the model in transformers:


	
		
	
	
		Using the Pytorch model
	



	
		
	
	
		Running the model on a CPU
	



	
		
	
	
		Running the model on a GPU
	



	
		
	
	
		Running the model on a GPU using different precisions
	



	
		
	
	
		Uses
	



	
		
	
	
		Direct Use and Downstream Use
	


The authors write in the original paper's model card that: 

See the research paper for further details.


	
		
	
	
		Out-of-Scope Use
	


More information needed.


	
		
	
	
		Bias, Risks, and Limitations
	


The information below in this section are copied from the model's official model card:


	
		
	
	
		Ethical considerations and risks
	



	
		
	
	
		Known Limitations
	



	
		
	
	
		Sensitive Use:
	



	
		
	
	
		Training Details
	



	
		
	
	
		Training Data
	


The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):




	
		
	
	
		Training Procedure
	


According to the model card from the original paper:

The model has been trained on TPU v3 or TPU v4 pods, using t5x codebase together with jax.


	
		
	
	
		Evaluation
	



	
		
	
	
		Testing Data, Factors & Metrics
	


The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:

For full details, please check the research paper.


	
		
	
	
		Results
	


For full results for FLAN-T5-Base, see the research paper, Table 3.


	
		
	
	
		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 - TPU v3 or TPU v4  | Number of chips ≥ 4.
Hours used: More information needed
Cloud Provider: GCP
Compute Region: More information needed
Carbon Emitted: More information needed



	
		
	
	
		Citation
	


BibTeX:


	
		
	
	
		Model Recycling
	


Evaluation on 36 datasets using google/flan-t5-base as a base model yields average score of 77.98 in comparison to 68.82 by google/t5-v1_1-base.

The model is ranked 1st among all tested models for the google/t5-v1_1-base architecture as of 06/02/2023
Results:

For more information, see: Model Recycling



The flan-t5-base model is a language model trained on a large number of tasks and languages. It is an improved version of the T5 model, with fine-tuning on over 1000 additional tasks and support for multiple languages. The model can be used for various NLP tasks and has been evaluated on a wide range of tasks and languages. It is licensed under Apache 2.0 and is available for use in the transformers library. The model's training details, evaluation results, and environmental impact are also provided in the model card.

Google

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flan-t5-xxl

flan-t5-base

vit-base-patch16-224-in21k

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