Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-nl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-nl2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-nl2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-nl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-base-nl2: direct link, hf CLI and curl.
- Browser
- Download file 231 MB
-
https://huggingface.co/google/t5-efficient-base-nl2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-base-nl2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-base-nl2/resolve/main/flax_model.msgpack
231 MB
- Xet hash:
- 9732708df38f738896b97a12307deba5b023ca105a6fa1bdc049bbfcabad00d4
- Size of remote file:
- 231 MB
- SHA256:
- c76b2d9e109e99b99d2506e9cc5807e06267a2702538a6474def13b542e2ae44
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