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README.md ADDED
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+
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+ ---
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+ language:
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+ - nl
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+ license: apache-2.0
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+ tags:
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+ - dutch
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+ - t5
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+ - t5x
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+ - ul2
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+ - seq2seq
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+ datasets:
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+ - yhavinga/mc4_nl_cleaned
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+ - yhavinga/nedd_wiki_news
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+ inference: false
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+ ---
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+
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+ # ul2-small-dutch for Dutch
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+
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+ Pretrained T5 model on Dutch using a UL2 (Mixture-of-Denoisers) objective.
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+ The T5 model was introduced in
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+ [this paper](https://arxiv.org/abs/1910.10683)
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+ and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
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+ The UL2 objective was introduced in
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+ [this paper](https://arxiv.org/abs/2205.05131)
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+ and first released at [this page](https://github.com/google-research/google-research/tree/master/ul2).
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+
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+ **Note:** The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on
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+ a specific downstream task to be useful in practice.
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+
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+ ## Model description
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+
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+ T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format.
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+ `ul2-small-dutch` T5 is a transformers model pretrained on a very large corpus of
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+ Dutch data in a self-supervised fashion.
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+ This means it was pretrained on the raw texts only, with no humans labelling them in any way
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+ (which is why it can use lots of publicly available data) with an automatic process to generate
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+ inputs and outputs from those texts.
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+
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+
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+ This model used the [T5 v1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) improvements compared to the original T5 model during the pretraining:
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+ - GEGLU activation in the feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202)
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+ - Dropout was turned off during pre-training. Dropout should be re-enabled during fine-tuning
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+ - Pre-trained on self-supervised objective only without mixing in the downstream tasks
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+ - No parameter sharing between embedding and classifier layer
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+
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+
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+
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+ ### UL2 pretraining objective
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+
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+ This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training
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+ paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where
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+ the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers
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+ that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of
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+ three denoising tasks:
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+
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+ 1. R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective;
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+ 2. X-denoising (or extreme span corruption); and
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+ 3. S-denoising (or sequential PrefixLM).
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+
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+ During pre-training, we sample from the available denoising tasks based on user-specified ratios.
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+ UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training
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+ denoising task. During the pre-training, a paradigm token is inserted to the input
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+ (`[NLU]` for R-denoising, `[NLG]` for X-denoising, or `[S2S]` for S-denoising) indicating the denoising task at hand.
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+ Then, during fine-tuning the same input token should be inserted to get the best performance for different downstream
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+ fine-tuning tasks.
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+
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+ ## Intended uses & limitations
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+
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+ This model was only pretrained in a self-supervised way excluding any supervised training.
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+ Therefore, this model has to be fine-tuned before it is usable on a downstream task,
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+ like text classification, unlike the Google's original T5 model.
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+
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+ **Note:** You most likely need to fine-tune these T5/UL2 models without mixed precision
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+ so fine-tune them with full fp32 precision. Fine-tuning with Flax in bf16 - `model.to_bf16()` - is possible
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+ if you set the mask correctly to exclude layernorm and embedding layers. Also note that the T5x pre-training
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+ and fine-tuning configs set `z_loss` to 1e-4, which is used to keep the loss scale from underflowing.
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+ You can also find more fine-tuning tips from [here](https://discuss.huggingface.co/t/t5-finetuning-tips), for example.
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+
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+ **Note**: For fine-tuning, most likely you can get better results if you insert a prefix token
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+ of `[NLU]`, `[NLG]`, or `[S2S]` to your input texts.
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+ For general language understanding fine-tuning tasks, you could use the `[NLU]` token.
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+ For GPT-style causal language generation, you could use the `[S2S]` token.
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+ The token `[NLG]` of the X-denoising pretrain task is somewhat mix between the language understanding and causal language
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+ generation so the token `[NLG]` could maybe be used for language generation fine-tuning too.
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+
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+ ### How to use
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+
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+ Here is how to use this model in PyTorch:
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+
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+ ```python
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+ from transformers import T5Tokenizer, T5ForConditionalGeneration
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+
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+ tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-small-dutch", use_fast=False)
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+ model = T5ForConditionalGeneration.from_pretrained("yhavinga/ul2-small-dutch")
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+ ```
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+
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+ and in Flax:
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+
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+ ```python
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+ from transformers import T5Tokenizer, FlaxT5ForConditionalGeneration
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+
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+ tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-small-dutch", use_fast=False)
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+ model = FlaxT5ForConditionalGeneration.from_pretrained("yhavinga/ul2-small-dutch")
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+ ```
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+
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+
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+ ### Limitations and bias
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+
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+ The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral.
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+ Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
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+
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+ ## Training data
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+
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+ The `ul2-small-dutch` T5 model was pre-trained simultaneously on a combination of several datasets,
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+ including the full version of the "mc4_nl_cleaned" dataset, which is a cleaned version of Common Crawl's web
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+ crawl corpus, Dutch books, the Dutch subset of Wikipedia (2022-03-20), and a subset of "mc4_nl_cleaned"
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+ containing only texts from Dutch and Belgian newspapers. This last dataset is oversampled to bias the model
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+ towards descriptions of events in the Netherlands and Belgium.
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+
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+
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+
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+ ## Training procedure
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+
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+ ### Preprocessing
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+
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+ The ul2-small-dutch T5 model uses a SentencePiece unigram tokenizer with a vocabulary of 32,000 tokens.
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+ The tokenizer includes the special tokens `<pad>`, `</s>`, `<unk>`, known from the original T5 paper,
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+ `[NLU]`, `[NLG]` and `[S2S]` for the MoD pre-training, and `<n>` for newline.
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+ During pre-training with the UL2 objective, input and output sequences consist of 512 consecutive tokens.
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+ The tokenizer does not lowercase texts and is therefore case-sensitive; it distinguises
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+ between `dutch` and `Dutch`.
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+ Additionally, 100+28 extra tokens were added for pre-training tasks, resulting in a total of 32,128 tokens.
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+
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+ ### Pretraining
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+ The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/),
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+ for 957300 steps with a batch size of 128
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+ (in total 62 B tokens).
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+ The optimizer used was AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2,
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+ and then an inverse square root decay (exponential decay) of the learning rate after.
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+ The model was trained with Google's Jax/Flax based [t5x framework](https://github.com/google-research/t5x) with help
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+ from [Stephenn Fernandes](https://huggingface.co/StephennFernandes) to get started writing task definitions that wrap
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+ HF datasets.
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+
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+ The UL2 training objective code used with the [t5x framework](https://github.com/google-research/t5x) was copied and
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+ slightly modified from the [UL2 paper](https://arxiv.org/pdf/2205.05131.pdf) appendix chapter 9.2 by the authors
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+ of the Finnish ul2 models. Used UL2 objective code is available in the repository
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+ [Finnish-NLP/ul2-base-nl36-finnish](https://huggingface.co/Finnish-NLP/ul2-base-nl36-finnish) in the files `ul2_objective.py` and `tasks.py`.
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+ UL2's mixture-of-denoisers configuration was otherwise equal to the UL2 paper
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+ but for the rate of mixing denoisers, 20% for S-denoising was used (suggested at the paper chapter 4.5)
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+ and the rest was divided equally between the R-denoising and X-denoising (i.e. 40% for both).
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+ ### Model list
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+
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+ Models in this series:
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+ | | ul2-base-dutch | ul2-base-nl36-dutch | ul2-large-dutch | ul2-small-dutch |
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+ |:---------------------|:---------------------|:----------------------|:---------------------|:---------------------|
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+ | model_type | t5 | t5 | t5 | t5 |
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+ | _pipeline_tag | text2text-generation | text2text-generation | text2text-generation | text2text-generation |
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+ | d_model | 768 | 768 | 1024 | 512 |
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+ | d_ff | 2048 | 3072 | 2816 | 1024 |
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+ | num_heads | 12 | 12 | 16 | 6 |
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+ | d_kv | 64 | 64 | 64 | 64 |
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+ | num_layers | 12 | 36 | 24 | 8 |
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+ | num_decoder_layers | 12 | 36 | 24 | 8 |
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+ | feed_forward_proj | gated-gelu | gated-gelu | gated-gelu | gated-gelu |
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+ | dense_act_fn | gelu_new | gelu_new | gelu_new | gelu_new |
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+ | vocab_size | 32128 | 32128 | 32128 | 32128 |
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+ | tie_word_embeddings | 0 | 0 | 0 | 0 |
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+ | torch_dtype | float32 | float32 | float32 | float32 |
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+ | _gin_batch_size | 128 | 64 | 64 | 128 |
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+ | _gin_z_loss | 0.0001 | 0.0001 | 0.0001 | 0.0001 |
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+ | _gin_t5_config_dtype | 'bfloat16' | 'bfloat16' | 'bfloat16' | 'bfloat16' |
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+
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+
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+
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+ ## Evaluation results
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+
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+ See the evaluation section in the interactive [Pre-training Dutch T5 Models](https://huggingface.co/spaces/yhavinga/pre-training-dutch-t5-models) blog.
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+
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+ ## Acknowledgements
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+
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+ This project would not have been possible without compute generously provided by Google through the
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+ [TPU Research Cloud](https://sites.research.google/trc/).
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+ Thanks to the [Finnish-NLP](https://huggingface.co/Finnish-NLP) authors for releasing their code for the UL2 objective and associated task definitions.
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+ Thanks to [Stephenn Fernandes](https://huggingface.co/StephennFernandes) for helping me get started with the t5x framework.
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+
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+ Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
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+
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config.gin ADDED
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+ from __gin__ import dynamic_registration
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+ import __main__ as train_script
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+ import seqio
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+ import t5.data.mixtures
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+ from t5x import adafactor
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+ from t5x.examples.t5 import network
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+ from t5x import gin_utils
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+ from t5x import models
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+ from t5x import partitioning
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+ from t5x import trainer
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+ from t5x import utils
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+ import tasks.nedd_tasks
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+ import tasks.ul2_tasks as tasks2
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+
15
+ # Macros:
16
+ # ==============================================================================
17
+ BATCH_SIZE = 128
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+ DROPOUT_RATE = 0.0
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+ LABEL_SMOOTHING = 0.0
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+ LOSS_NORMALIZING_FACTOR = None
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+ MIXTURE_OR_TASK_MODULE = None
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+ MIXTURE_OR_TASK_NAME = 'ul2_mc4_nedd_wiki_news_mix_1'
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+ MODEL = @models.EncoderDecoderModel()
24
+ MODEL_DIR = 'ul2_small_mc4_nedd_wiki_news_nl'
25
+ OPTIMIZER = @adafactor.Adafactor()
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+ RANDOM_SEED = None
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+ SHUFFLE_TRAIN_EXAMPLES = True
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+ TASK_FEATURE_LENGTHS = {'inputs': 512, 'targets': 512}
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+ TRAIN_STEPS = 1000000
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+ USE_CACHED_TASKS = False
31
+ USE_HARDWARE_RNG = False
32
+ VOCABULARY = @seqio.SentencePieceVocabulary()
33
+ Z_LOSS = 0.0001
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+
35
+ # Parameters for adafactor.Adafactor:
36
+ # ==============================================================================
37
+ adafactor.Adafactor.decay_rate = 0.8
38
+ adafactor.Adafactor.logical_factor_rules = \
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+ @adafactor.standard_logical_factor_rules()
40
+ adafactor.Adafactor.step_offset = 0
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+
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+ # Parameters for utils.CheckpointConfig:
43
+ # ==============================================================================
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+ utils.CheckpointConfig.restore = @utils.RestoreCheckpointConfig()
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+ utils.CheckpointConfig.save = @utils.SaveCheckpointConfig()
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+
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+ # Parameters for utils.create_learning_rate_scheduler:
48
+ # ==============================================================================
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+ utils.create_learning_rate_scheduler.base_learning_rate = 1.0
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+ utils.create_learning_rate_scheduler.factors = 'constant * rsqrt_decay'
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+ utils.create_learning_rate_scheduler.warmup_steps = 10000
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+
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+ # Parameters for train/utils.DatasetConfig:
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+ # ==============================================================================
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+ train/utils.DatasetConfig.batch_size = %BATCH_SIZE
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+ train/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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+ train/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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+ train/utils.DatasetConfig.pack = True
59
+ train/utils.DatasetConfig.seed = None
60
+ train/utils.DatasetConfig.shuffle = %SHUFFLE_TRAIN_EXAMPLES
61
+ train/utils.DatasetConfig.split = 'train'
62
+ train/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
63
+ train/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
64
+
65
+ # Parameters for train_eval/utils.DatasetConfig:
66
+ # ==============================================================================
67
+ train_eval/utils.DatasetConfig.batch_size = %BATCH_SIZE
68
+ train_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
69
+ train_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
70
+ train_eval/utils.DatasetConfig.pack = True
71
+ train_eval/utils.DatasetConfig.seed = 42
72
+ train_eval/utils.DatasetConfig.shuffle = False
73
+ train_eval/utils.DatasetConfig.split = 'validation'
74
+ train_eval/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
75
+ train_eval/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
76
+
77
+ # Parameters for models.EncoderDecoderModel:
78
+ # ==============================================================================
79
+ models.EncoderDecoderModel.input_vocabulary = %VOCABULARY
80
+ models.EncoderDecoderModel.label_smoothing = %LABEL_SMOOTHING
81
+ models.EncoderDecoderModel.loss_normalizing_factor = %LOSS_NORMALIZING_FACTOR
82
+ models.EncoderDecoderModel.module = @network.Transformer()
83
+ models.EncoderDecoderModel.optimizer_def = %OPTIMIZER
84
+ models.EncoderDecoderModel.output_vocabulary = %VOCABULARY
85
+ models.EncoderDecoderModel.z_loss = %Z_LOSS
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+
87
+ # Parameters for partitioning.PjitPartitioner:
88
+ # ==============================================================================
89
+ partitioning.PjitPartitioner.logical_axis_rules = \
90
+ @partitioning.standard_logical_axis_rules()
91
+ partitioning.PjitPartitioner.model_parallel_submesh = None
92
+ partitioning.PjitPartitioner.num_partitions = 1
93
+
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+ # Parameters for utils.RestoreCheckpointConfig:
95
+ # ==============================================================================
96
+ utils.RestoreCheckpointConfig.path = []
97
+
98
+ # Parameters for utils.SaveCheckpointConfig:
99
+ # ==============================================================================
100
+ utils.SaveCheckpointConfig.dtype = 'float32'
101
+ utils.SaveCheckpointConfig.keep = 4
102
+ utils.SaveCheckpointConfig.period = 50000
103
+ utils.SaveCheckpointConfig.save_dataset = False
104
+ utils.SaveCheckpointConfig.use_gda = False
105
+
106
+ # Parameters for seqio.SentencePieceVocabulary:
107
+ # ==============================================================================
108
+ seqio.SentencePieceVocabulary.sentencepiece_model_file = \
109
+ 'gs://t5-dutch-english/vocabs/nedd.32000.128extra/spiece.model'
110
+
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+ # Parameters for network.T5Config:
112
+ # ==============================================================================
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+ network.T5Config.dropout_rate = %DROPOUT_RATE
114
+ network.T5Config.dtype = 'bfloat16'
115
+ network.T5Config.emb_dim = 512
116
+ network.T5Config.head_dim = 64
117
+ network.T5Config.logits_via_embedding = False
118
+ network.T5Config.mlp_activations = ('gelu', 'linear')
119
+ network.T5Config.mlp_dim = 1024
120
+ network.T5Config.num_decoder_layers = 8
121
+ network.T5Config.num_encoder_layers = 8
122
+ network.T5Config.num_heads = 6
123
+ network.T5Config.vocab_size = 32128
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+
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+ # Parameters for train_script.train:
126
+ # ==============================================================================
127
+ train_script.train.checkpoint_cfg = @utils.CheckpointConfig()
128
+ train_script.train.eval_period = 2000
129
+ train_script.train.eval_steps = 20
130
+ train_script.train.infer_eval_dataset_cfg = None
131
+ train_script.train.model = %MODEL
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+ train_script.train.model_dir = %MODEL_DIR
133
+ train_script.train.partitioner = @partitioning.PjitPartitioner()
134
+ train_script.train.random_seed = %RANDOM_SEED
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+ train_script.train.stats_period = 100
136
+ train_script.train.summarize_config_fn = @gin_utils.summarize_gin_config
137
+ train_script.train.total_steps = %TRAIN_STEPS
138
+ train_script.train.train_dataset_cfg = @train/utils.DatasetConfig()
139
+ train_script.train.train_eval_dataset_cfg = @train_eval/utils.DatasetConfig()
140
+ train_script.train.trainer_cls = @trainer.Trainer
141
+ train_script.train.use_hardware_rng = %USE_HARDWARE_RNG
142
+
143
+ # Parameters for trainer.Trainer:
144
+ # ==============================================================================
145
+ trainer.Trainer.learning_rate_fn = @utils.create_learning_rate_scheduler()
146
+ trainer.Trainer.num_microbatches = None
147
+
148
+ # Parameters for network.Transformer:
149
+ # ==============================================================================
150
+ network.Transformer.config = @network.T5Config()
config.json ADDED
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+ {
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+ "_name_or_path": "./",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 1024,
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+ "d_kv": 64,
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+ "d_model": 512,
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+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "gelu_new",
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gated-gelu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "is_gated_act": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 8,
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+ "num_heads": 6,
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+ "num_layers": 8,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.24.0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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