Transformers
PyTorch
JAX
TensorBoard
Safetensors
English
t5
text2text-generation
text-generation-inference
Instructions to use hmbyt5/byt5-small-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hmbyt5/byt5-small-english with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hmbyt5/byt5-small-english") model = AutoModelForSeq2SeqLM.from_pretrained("hmbyt5/byt5-small-english") - Notebooks
- Google Colab
- Kaggle
hmByT5 - Preliminary Language Models
Preliminary Historic Multilingual and Monolingual ByT5 Models. Following languages are currently covered:
- English (British Library Corpus - Books)
More details can be found in our GitHub repository.
Pretraining
We use the official JAX/FLAX example in Hugging Face Transformers to pretrain a ByT5 model on a single v3-8 TPU. Details about the training can be found here.
Evaluation on Downstream Tasks (NER)
We evaluated the hmByT5 model on downstream tasks:
| Model | English AjMC | German AjMC | French AjMC | Finnish NewsEye | Swedish NewsEye | Dutch ICDAR | French ICDAR | Avg. |
|---|---|---|---|---|---|---|---|---|
hmbyt5/byt5-small-english |
85.65 ± 1.21 | 87.27 ± 0.50 | 84.44 ± 0.79 |
Acknowledgements
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). Many Thanks for providing access to the TPUs ❤️
- Downloads last month
- 7
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support