Translation
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
t5
text2text-generation
summarization
text-generation-inference
Instructions to use google-t5/t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-t5/t5-base 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-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google-t5/t5-base: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/google-t5/t5-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google-t5/t5-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-t5/t5-base/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 2f67ba177372f8bc54ad6a65a6ef0f0259df8f616216d8a7c1d1e7117a6b6227
- Size of remote file:
- 892 MB
- SHA256:
- d96ab4b2e2ac1743c32e80669ec37905151c78d8136ff0ce4ba6566bde6e932f
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