Translation
Transformers
PyTorch
TensorFlow
JAX
Safetensors
t5
text2text-generation
summarization
text-generation-inference
Instructions to use google-t5/t5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-t5/t5-large with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="google-t5/t5-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-large") model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-large") - Inference
- Notebooks
- Google Colab
- Kaggle
Optimize the summarization time
#1
by yassinr - opened
Hello everyone!
I wonder if there is a solution to optimize the text summary time.
it takes about 1-2 minutes to generate a summary using (10vCPU)
I wonder if it is possible to use GPU (may not make any sense)
Thanks
Surely it'll be faster on GPU!