
# 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 ❤️

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

* 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.

# 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.

# Evaluation on Downstream Tasks (NER)

See detailed results at hmLeaderboard.

# Acknowledgements

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

# 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. # 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. # Evaluation on Downstream Tasks (NER)

See detailed results at hmLeaderboard. # 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 #de #fr #fi #sv #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:

* 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. # 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. # Evaluation on Downstream Tasks (NER)

See detailed results at hmLeaderboard. # 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 #de #fr #fi #sv #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:

* 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.# 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.# Evaluation on Downstream Tasks (NER)

See detailed results at hmLeaderboard.# Acknowledgements

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