
# 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](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 Base model on English AjMC dataset:

| Configuration                            | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg.         |
|------------------------------------------|-------|-------|-------|-------|-------|--------------|
| `wsFalse-bs8-e10-lr0.00015-poolingfirst` | 86.51 | 87.2  | 86.22 | 85.78 | 86.46 | 86.43 ± 0.46 |
| `wsFalse-bs4-e10-lr0.00016-poolingfirst` | 86.12 | 87.04 | 87.01 | 85.25 | 86.74 | 86.43 ± 0.68 |
| `wsFalse-bs8-e10-lr0.00016-poolingfirst` | 86.49 | 85.27 | 86.12 | 86.29 | 85.61 | 85.96 ± 0.45 |
| `wsFalse-bs4-e10-lr0.00015-poolingfirst` | 86.33 | 86.05 | 84.48 | 85.68 | 86.16 | 85.74 ± 0.67 |

The ByT5 Small [model](https://huggingface.co/hmbyt5/byt5-small-english) achieves 85.65 ± 1.21 on this dataset.

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


* 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.


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


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


We evaluated the hmByT5 Base model on English AjMC dataset:



The ByT5 Small model achieves 85.65 ± 1.21 on this dataset.


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 #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
