
# 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` for one epoch.

# Mean Noise Span Length

The previously pretrained hmByT5 models "accidentally" use a mean noise span length of 3, because this value is the
default one for T5. But the ByT5 paper mentions, that using a length of 3 would make pretraining tasks too easy, and
recommend a value of 20. Thus, we pretrained this model with `mean_noise_span_length=20` and fine-tuned it on English
AjMC dataset:

| Configuration                            | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg.         |
|------------------------------------------|-------|-------|-------|-------|-------|--------------|
| `wsFalse-bs4-e10-lr0.00015-poolingfirst` | 85.48 | 84.6  | 85.65 | 86.83 | 86.53 | 85.82 ± 0.79 |
| `wsFalse-bs4-e10-lr0.00016-poolingfirst` | 85.35 | 84.5  | 86.05 | 85.1  | 85.18 | 85.24 ± 0.5  |
| `wsFalse-bs8-e10-lr0.00016-poolingfirst` | 84.14 | 83.45 | 84.4  | 84.9  | 85.82 | 84.54 ± 0.79 |
| `wsFalse-bs8-e10-lr0.00015-poolingfirst` | 85.27 | 85.3  | 83.33 | 85.25 | 81.7  | 84.17 ± 1.45 |

For comparison the model using a length of 3 achieved 85.65 ± 1.21.

# 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' for one epoch.


Mean Noise Span Length
======================


The previously pretrained hmByT5 models "accidentally" use a mean noise span length of 3, because this value is the
default one for T5. But the ByT5 paper mentions, that using a length of 3 would make pretraining tasks too easy, and
recommend a value of 20. Thus, we pretrained this model with 'mean\_noise\_span\_length=20' and fine-tuned it on English
AjMC dataset:



For comparison the model using a length of 3 achieved 85.65 ± 1.21.


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 
