
# umT5 Small

The UMT5 model was proposed in [UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi)
by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.

The abstract from the paper is the following:

*Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance
between different languages. However previous work has not systematically evaluated the efficacy of different
pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax,
that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly
capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a
range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax
outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our
contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters
across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.*

# Integration into Transformers

Overview of umT5 model integration:

* Transformers Integration is on-going, see this awesome [PR](https://github.com/huggingface/transformers/pull/22626) by @agemagician!
* Conversion script (umT5X checkpoints to FLAX) is [here](https://gist.github.com/stefan-it/5d6a4ec89e7ad97181983881434cb4eb).
{"license": "mit"}
transformers pytorch jax safetensors umt5 text2text-generation license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #safetensors #umt5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# umT5 Small

The UMT5 model was proposed in UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining
by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.

The abstract from the paper is the following:

*Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance
between different languages. However previous work has not systematically evaluated the efficacy of different
pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax,
that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly
capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a
range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax
outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our
contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters
across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.*

# Integration into Transformers

Overview of umT5 model integration:

* Transformers Integration is on-going, see this awesome PR by @agemagician!
* Conversion script (umT5X checkpoints to FLAX) is here.
# umT5 Small

The UMT5 model was proposed in UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining
by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.

The abstract from the paper is the following:

*Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance
between different languages. However previous work has not systematically evaluated the efficacy of different
pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax,
that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly
capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a
range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax
outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our
contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters
across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.* # Integration into Transformers

Overview of umT5 model integration:

* Transformers Integration is on-going, see this awesome PR by @agemagician!
* Conversion script (umT5X checkpoints to FLAX) is here.
TAGS
#transformers #pytorch #jax #safetensors #umt5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # umT5 Small

The UMT5 model was proposed in UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining
by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.

The abstract from the paper is the following:

*Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance
between different languages. However previous work has not systematically evaluated the efficacy of different
pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax,
that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly
capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a
range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax
outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our
contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters
across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.* # Integration into Transformers

Overview of umT5 model integration:

* Transformers Integration is on-going, see this awesome PR by @agemagician!
* Conversion script (umT5X checkpoints to FLAX) is here.
TAGS
#transformers #pytorch #jax #safetensors #umt5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# umT5 Small

The UMT5 model was proposed in UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining
by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.

The abstract from the paper is the following:

*Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance
between different languages. However previous work has not systematically evaluated the efficacy of different
pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax,
that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly
capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a
range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax
outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our
contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters
across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.*# Integration into Transformers

Overview of umT5 model integration:

* Transformers Integration is on-going, see this awesome PR by @agemagician!
* Conversion script (umT5X checkpoints to FLAX) is here.