t5 3b
简介
核心亮点
- 统一文本到文本框架，兼容多种 NLP 任务
- 参数规模适中，支持私有化部署与高效微调
- 在机器翻译和文本摘要场景中表现稳健
- Apache-2.0 协议，对商业应用极其友好
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("google-t5/t5-3b")
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-3b")Hugging Face 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download google-t5/t5-3b下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download google-t5/t5-3b config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('google-t5/t5-3b')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/google-t5/t5-3b如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/google-t5/t5-3b模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('google-t5/t5-3b')
tokenizer = AutoTokenizer.from_pretrained('google-t5/t5-3b')完整文档
---
language:
- en
- fr
- ro
- de
- multilingual
license: apache-2.0
tags:
- summarization
- translation
datasets:
- c4
---
Model Card for T5-3B
Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Citation
8. Model Card Authors
9. How To Get Started With the Model
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.
T5-3B is the checkpoint with 3 billion parameters.
- Developed by: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See associated paper and GitHub repo
- Model type: Language model
- Language(s) (NLP): English, French, Romanian, German
- License: Apache 2.0
- Related Models: All T5 Checkpoints
- Resources for more information:
Uses
Direct Use and Downstream 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.
See the blog post and research paper for further details.
Out-of-Scope Use
More information needed.
Bias, Risks, and Limitations
More information needed.
Recommendations
More information needed.
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.).
Thereby, the following datasets were being used for (1.) and (2.):
1. Datasets used for Unsupervised denoising objective:
2. Datasets used for Supervised text-to-text language modeling objective
- Sentence acceptability judgment
- Sentiment analysis
- Paraphrasing/sentence similarity
- Natural language inference
- Sentence completion
- Word sense disambiguation
- Question answering
Training Procedure
In their abstract, the model developers write:
> In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks.
The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the research paper for further details.
Evaluation
Testing Data, Factors & Metrics
The developers evaluated the model on 24 tasks, see the research paper for full details.
Results
For full results for T5-3B, see the research paper, Table 14.
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
Citation
BibTeX:
@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.
Model Card Authors
This model card was written by the team at Hugging Face.
How to Get Started with the Model
See the Hugging Face T5 docs and a Colab Notebook created by the model developers for more context on how to get started with this checkpoint.
