Instructions to use hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax") model = AutoModelForSeq2SeqLM.from_pretrained("hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
language:
- en
- de
- fr
- fi
- sv
- nl
---
# hmByT5 - Preliminary Language Models
Preliminary Historic Multilingual and Monolingual ByT5 Models. Following languages are currently covered:
* English (British Library Corpus - Books)
* German (Europeana Newspaper)
* French (Europeana Newspaper)
* Finnish (Europeana Newspaper)
* Swedish (Europeana Newspaper)
* Dutch (Delpher Corpus)
More details can be found in [our GitHub repository](https://github.com/stefan-it/hmByT5).
# 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](https://github.com/stefan-it/hmByT5/tree/main/hmbyt5-flax).
This model was trained with `mean_noise_span_length=20` for one epoch.
# Evaluation on Downstream Tasks (NER)
See detailed results at [hmLeaderboard](https://huggingface.co/spaces/stefan-it/hmLeaderboard).
# Acknowledgements
Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
Many Thanks for providing access to the TPUs ❤️
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