metadata
datasets:
- EleutherAI/pile
language:
- en
pipeline_tag: text2text-generation
tags:
- summarization
- translation
Model Card for T5v2 Base
Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Environmental Impact
- Citation
- Model Card Authors
- How To Get Started With the Model
Model Details
Model Description
More information needed.
Uses
Direct Use and Downstream Use
More information needed.
Out-of-Scope Use
More information needed.
Bias, Risks, and Limitations
More information needed.
Recommendations
More information needed.
Training Details
Training Data
The model was pre-trained on the Pile using an unsupervised denoising objective,
Training Procedure
More information needed.
Evaluation
Testing Data, Factors & Metrics
More information needed.
Results
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
Citation
BibTeX:
@article{2024t5v2,
author = {Lintang Sutawika and Aran Komatsuzaki and Colin Raffel},
title = {T5v2, an update of T5},
year = {2024},
url = {}
}
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
from transformers import UMT5Tokenizer, UMT5Model
tokenizer = UMT5Tokenizer.from_pretrained("EleutherAI/t5-v2-base")
model = UMT5Model.from_pretrained("EleutherAI/t5-v2-base")
input_ids = tokenizer(
"Studies have been shown that owning a dog is good for you", return_tensors="pt"
).input_ids # Batch size 1
decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
# forward pass
outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
last_hidden_states = outputs.last_hidden_state