
# 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).

This model was trained with `mean_noise_span_length=20`.

# Evaluation on Downstream Tasks (NER)

We evaluated the hmByT5 model on ICDAR Europeana dataset:

| Configuration                            | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg.         |
|------------------------------------------|-------|-------|-------|-------|-------|--------------|
| `wsFalse-bs4-e10-lr0.00015-poolingfirst` | 86.61 | 85.88 | 87.65 | 87.93 | 88.01 | 87.22 ± 0.83 |
| `wsFalse-bs8-e10-lr0.00015-poolingfirst` | 87.88 | 87.56 | 85.62 | 86.52 | 87.03 | 86.92 ± 0.8  |
| `wsFalse-bs4-e10-lr0.00016-poolingfirst` | 86.17 | 85.87 | 87.77 | 86.58 | 87.96 | 86.87 ± 0.85 |
| `wsFalse-bs8-e10-lr0.00016-poolingfirst` | 87.67 | 86.02 | 85.66 | 87    | 85.99 | 86.47 ± 0.75 |

The results show no performance improvement of the [model](https://huggingface.co/hmbyt5/byt5-small-historic-dutch)
trained with `mean_noise_span_length=3`, that achieved 87.90 ± 0.71.

# 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.


This model was trained with 'mean\_noise\_span\_length=20'.


Evaluation on Downstream Tasks (NER)
====================================


We evaluated the hmByT5 model on ICDAR Europeana dataset:



The results show no performance improvement of the model
trained with 'mean\_noise\_span\_length=3', that achieved 87.90 ± 0.71.


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 

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