
# hmByT5 - Preliminary Language Models

Preliminary Historic Multilingual and Monolingual ByT5 Models. Following languages are currently covered:

* 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).

# Evaluation on Downstream Tasks (NER)

We evaluated the hmByT5 model on ICDAR Europeana dataset:

| Configuration                            | Run 1 | Avg.        |
|------------------------------------------|-------|-------------|
| `wsFalse-bs4-e10-lr0.00015-poolingfirst` | 87.63 | 87.63 ± 0.0 |
| `wsFalse-bs8-e10-lr0.00016-poolingfirst` | 87.35 | 87.35 ± 0.0 |
| `wsFalse-bs8-e10-lr0.00015-poolingfirst` | 87.26 | 87.26 ± 0.0 |
| `wsFalse-bs4-e10-lr0.00016-poolingfirst` | 86.31 | 86.31 ± 0.0 |

We only performed fine-tuning for one epoch. Unfortunately, this ByT5 Base model shows no improvement over ByT5 Small architecture.

# 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 ❤️

{"language": ["nl"], "license": "mit"}
transformers pytorch jax tensorboard t5 text2text-generation nl license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #nl #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

hmByT5 - Preliminary Language Models
====================================


Preliminary Historic Multilingual and Monolingual ByT5 Models. Following languages are currently covered:


* Dutch (Delpher Corpus)


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 ICDAR Europeana dataset:


Configuration: 'wsFalse-bs4-e10-lr0.00015-poolingfirst', Run 1: 87.63, Avg.: 87.63 ± 0.0
Configuration: 'wsFalse-bs8-e10-lr0.00016-poolingfirst', Run 1: 87.35, Avg.: 87.35 ± 0.0
Configuration: 'wsFalse-bs8-e10-lr0.00015-poolingfirst', Run 1: 87.26, Avg.: 87.26 ± 0.0
Configuration: 'wsFalse-bs4-e10-lr0.00016-poolingfirst', Run 1: 86.31, Avg.: 86.31 ± 0.0


We only performed fine-tuning for one epoch. Unfortunately, this ByT5 Base model shows no improvement over ByT5 Small architecture.


Acknowledgements
================


Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC).
Many Thanks for providing access to the TPUs ️



TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #nl #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
