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
| 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 ❤️ | |