ugiugi/twitter-t5-mlm

T5-like span-masked language modeling

In the following, we demonstrate how to train a T5 model using the span-masked language model objective as proposed in the Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. More specifically, we demonstrate how JAX/Flax can be leveraged to pre-train google/t5-v1_1-base in Norwegian on a single TPUv3-8 pod.

The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.

Let's start by creating a model repository to save the trained model and logs. Here we call the model "norwegian-t5-base", but you can change the model name as you like.

To setup all relevant files for training, let's create a directory.

cd ./norwegian-t5-base

Train tokenizer

In the first step, we train a tokenizer to efficiently process the text input for the model. We make use of the tokenizers library to train a sentencepiece unigram tokenizer as shown in t5_tokenizer_model.py which is heavily inspired from yandex-research/DeDLOC's tokenizer model .

The tokenizer is trained on the complete Norwegian dataset of OSCAR and consequently saved in the cloned model directory. This can take up to 120 minutes depending on your hardware ☕☕☕ .

import datasets

from t5_tokenizer_model import SentencePieceUnigramTokenizer


vocab_size = 32_000
input_sentence_size = None

# Initialize a dataset
dataset = datasets.load_dataset("oscar", name="unshuffled_deduplicated_no", split="train")

tokenizer = SentencePieceUnigramTokenizer(unk_token="<unk>", eos_token="</s>", pad_token="<pad>")


# Build an iterator over this dataset
def batch_iterator(input_sentence_size=None):
    if input_sentence_size is None:
        input_sentence_size = len(dataset)
    batch_length = 100
    for i in range(0, input_sentence_size, batch_length):
        yield dataset[i: i + batch_length]["text"]


# Train tokenizer
tokenizer.train_from_iterator(
    iterator=batch_iterator(input_sentence_size=input_sentence_size),
    vocab_size=vocab_size,
    show_progress=True,
)

# Save files to disk
tokenizer.save("./norwegian-t5-base/tokenizer.json")

Create configuration

Next, we create the model's configuration file. This is as simple as loading and storing **google/t5-v1_1-base** in the local model folder:

from transformers import T5Config

config = T5Config.from_pretrained("google/t5-v1_1-base", vocab_size=tokenizer.get_vocab_size())
config.save_pretrained("./norwegian-t5-base")

Great, we have set up our model repository. During training, we will automatically push the training logs and model weights to the repo.

Train model

Next we can run the example script to pretrain the model:

python run_t5_mlm_flax.py \
    --output_dir="./norwegian-t5-base" \
    --model_type="t5" \
    --config_name="./norwegian-t5-base" \
    --tokenizer_name="./norwegian-t5-base" \
    --dataset_name="oscar" \
    --dataset_config_name="unshuffled_deduplicated_no" \
    --max_seq_length="512" \
    --per_device_train_batch_size="32" \
    --per_device_eval_batch_size="32" \
    --adafactor \
    --learning_rate="0.005" \
    --weight_decay="0.001" \
    --warmup_steps="2000" \
    --overwrite_output_dir \
    --logging_steps="500" \
    --save_steps="10000" \
    --eval_steps="2500" \
    --push_to_hub

Training should converge at a loss and accuracy of 2.36 and 57.0 respectively after 3 epochs on a single TPUv3-8. This should take around 4.5 hours. Training statistics can be accessed on directly on the 🤗 hub