Flan T5 Large

🚀 Model Card for FLAN-T5 large

FLAN-T5 large is a powerful language model that has been fine - tuned on a wide range of tasks and languages, offering better performance in zero - shot and few - shot learning scenarios compared to its base model.

🚀 Quick Start

✨ Features

• Multilingual Support: Supports multiple languages including English, Spanish, Japanese, Persian, and many others.
• Diverse Task Handling: Capable of handling various tasks such as text translation, question answering, logical reasoning, and math reasoning.
• Fine - Tuned for Performance: Based on pretrained T5 and fine - tuned with instructions for better zero - shot and few - shot performance.

📦 Installation

This section doesn't have specific installation commands, so it is skipped.

💻 Usage Examples

Basic Usage

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Advanced Usage

# Running the model on a GPU using FP16 precision
# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto", torch_dtype=torch.float16)

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

📚 Documentation

Model Details

Uses

Direct Use and Downstream Use

The primary use is research on language models, including: research on zero - shot NLP tasks and in - context few - shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models. See the research paper for further details.

Out - of - Scope Use

More information needed.

Bias, Risks, and Limitations

⚠️ Important Note

Language models, including Flan - T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan - T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

Ethical considerations and risks

Flan - T5 is fine - tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.

Known Limitations

Flan - T5 has not been tested in real world applications.

Sensitive Use

Flan - T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech.

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

These models are based on pretrained T5 (Raffel et al., 2020) and fine - tuned with instructions for better zero - shot and few - shot performance. There is one fine - tuned Flan model per T5 model size. The model has been trained on TPU v3 or TPU v4 pods, using [t5x](https://github.com/google - research/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).

Citation

@misc{https://doi.org/10.48550/arxiv.2210.11416,
  doi = {10.48550/ARXIV.2210.11416},
  
  url = {https://arxiv.org/abs/2210.11416},
  
  author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason},
  
  keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Scaling Instruction-Finetuned Language Models},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}

🔧 Technical Details

This section doesn't have specific technical details (more than 50 words), so it is skipped.

📄 License

The model is licensed under the Apache - 2.0 license.