mT5: Multilingual T5
Multilingual T5 (mT5) is a massively multilingual pretrained text-to-text transformer model, trained following a similar recipe as T5. This repo can be used to reproduce the experiments in the mT5 paper.
Table of Contents
Languages covered
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
Results
mT5 achieves state-of-the-art performance on many cross-lingual NLP tasks, as of November 2020. For example, on XTREME zero-shot classification, structured prediction and QA tasks (showing F1 scores):
| Model | XNLI | PAWS-X | WikiAnn-NER | XQuAD | MLQA | TyDiQA-GoldP |
|---|---|---|---|---|---|---|
| mBERT | 65.4 | 81.9 | 62.2 | 64.5 | 61.4 | 59.7 |
| XLM | 69.1 | 80.9 | 61.2 | 59.8 | 48.5 | 43.6 |
| InfoXLM | 81.4 | - | - | - | 73.6 | - |
| X-STILTs | 80.4 | 87.7 | 64.7 | 77.2 | 72.3 | 76.0 |
| XLM-R | 79.2 | 86.4 | 65.4 | 76.6 | 71.6 | 65.1 |
| VECO | 79.9 | 88.7 | 65.7 | 77.3 | 71.7 | 67.6 |
| RemBERT | 80.8 | 87.5 | 70.1 | 79.6 | 73.1 | 77.0 |
| mT5-Small | 67.5 | 82.4 | 50.5 | 58.1 | 54.6 | 36.4 |
| mT5-Base | 75.4 | 86.4 | 55.7 | 67.0 | 64.6 | 59.1 |
| mT5-Large | 81.1 | 88.9 | 58.5 | 77.8 | 71.2 | 68.4 |
| mT5-XL | 82.9 | 89.6 | 65.5 | 79.5 | 73.5 | 77.8 |
| mT5-XXL | 85.0 | 90.0 | 69.2 | 82.5 | 76.0 | 82.0 |
Usage
Training
To run this code, you need to install the t5
library. General instructions for training,
fine-tuning, evaluation, and exporting models for inference can be found in the
t5
repo. In
order to use the additional mT5 tasks provided in this library with the
t5_mesh_transformer command, run from this directory and add the flag
--module_import="multilingual_t5.tasks". There is also support for mT5 in
HuggingFace; see
instructions in the T5 repo
here.
To train an mT5-Large model on the
mc4
task from scratch as described in the paper:
export PROJECT=yourproject
export ZONE=yourzone
export BUCKET=yourbucket
export TPU=yourtpu
ctpu up --name=$TPU --project=$PROJECT --zone=$ZONE --tpu-size=v3-256 --tpu-only --noconf
TASK=mc4
MODEL_DIR="${BUCKET}${TASK}"
python -m t5.models.mesh_transformer_main \
--tpu="${TPU}" \
--gcp_project="${PROJECT}" \
--tpu_zone="${ZONE}" \
--model_dir="${MODEL_DIR}" \
--gin_file="models/t5.1.1.large.gin" \
--gin_param="MIXTURE_NAME = '${TASK}'" \
--gin_param="utils.run.sequence_length = {'inputs': 1024, 'targets': 256}" \
--gin_param="utils.run.batch_size = ('tokens_per_batch', 1048576)" \
--gin_param="utils.run.learning_rate_schedule=@learning_rate_schedules.rsqrt_no_ramp_down" \
--gin_param="run.train_steps = 1000000" \
--gin_param="utils.tpu_mesh_shape.model_parallelism = 1" \
--gin_param="utils.tpu_mesh_shape.tpu_topology = 'v3-256'" \
--eval_mode="perplexity_eval" \
--eval_gin_param="mesh_eval_dataset_fn.num_eval_examples = 10000" \
--t5_tfds_data_dir="${BUCKET}/t5-tfds" \
--module_import="multilingual_t5.tasks"
Fine-Tuning
The example below shows how to finetune the mT5-Large model on the XNLI
zeroshot task. See finetune_mt5_tasks.sh for hyperparameter settings for
other tasks.
export PROJECT=yourproject
export ZONE=yourzone
export BUCKET=yourbucket
export TPU=yourtpu
ctpu up --name=$TPU --project=$PROJECT --zone=$ZONE --tpu-size=v3-256 --tpu-only --noconf
TASK=mt5_xnli_zeroshot
SEQUENCE_LENGTH_GIN=xnli
PRETRAINED_DIR=gs://t5-data/pretrained_models/mt5/large
PRETRAINED_STEPS=1000000
FINETUNE_STEPS=20000
MODEL_DIR="${BUCKET}${TASK}"
# Run fine-tuning
python -m t5.models.mesh_transformer_main \
--tpu="${TPU}" \
--gcp_project="${PROJECT}" \
--tpu_zone="${ZONE}" \
--model_dir="${MODEL_DIR}" \
--gin_file="${PRETRAINED_DIR}/operative_config.gin" \
--gin_file="sequence_lengths/${SEQUENCE_LENGTH_GIN}.gin" \
--gin_param="utils.tpu_mesh_shape.tpu_topology = 'v3-256'" \
--gin_param="MIXTURE_NAME = '${TASK}'" \
--gin_param="utils.run.train_steps=$((PRETRAINED_STEPS+FINETUNE_STEPS))" \
--gin_param="utils.run.init_checkpoint='${PRETRAINED_DIR}/model.ckpt-${PRETRAINED_STEPS}'" \
--t5_tfds_data_dir="${BUCKET}/t5-tfds" \
--module_import="multilingual_t5.tasks" \
--gin_param="utils.run.batch_size = ('tokens_per_batch', 1048576)" \
--gin_location_prefix="multilingual_t5/gin/"
The remaining experiments are shown in the tasks.py file.
Released Model Checkpoints
We have released the following checkpoints for pre-trained models described in our paper:
- mT5-Small (300 million parameters): gs://t5-data/pretrained_models/mt5/small
- mT5-Base (580 million parameters): gs://t5-data/pretrained_models/mt5/base
- mT5-Large (1.2 billion parameters): gs://t5-data/pretrained_models/mt5/large
- mT5-XL (3.7 billion parameters): gs://t5-data/pretrained_models/mt5/xl
- mT5-XXL (13 billion parameters): gs://t5-data/pretrained_models/mt5/xxl
How to Cite
If you extend or use this work, please cite the paper where it was introduced:
@inproceedings{xue-etal-2021-mt5,
title = "m{T}5: A Massively Multilingual Pre-trained Text-to-Text Transformer",
author = "Xue, Linting and
Constant, Noah and
Roberts, Adam and
Kale, Mihir and
Al-Rfou, Rami and
Siddhant, Aditya and
Barua, Aditya and
Raffel, Colin",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.41",
doi = "10.18653/v1/2021.naacl-main.41",
pages = "483--498"
}