Autoupdate README.md

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	---
	language:
	- nl
	- en
	- multilingual
	license: apache-2.0
	tags:
	- dutch
	- english
	- t5
	- t5x
	- ul2
	- seq2seq
	- translation
	datasets:
	- yhavinga/mc4_nl_cleaned
	- yhavinga/nedd_wiki_news
	pipeline_tag: translation
	widget:
	- text: >-
	Redistricting and West Virginia’s shrinking population forced the state’s
	Republican Legislature to pit Mr. McKinley, a six-term Republican with a
	pragmatic bent, against Mr. Mooney, who has served four terms marked more
	by conservative rhetoric than legislative achievements.
	- text: >-
	It is a painful and tragic spectacle that rises before me: I have drawn
	back the curtain from the rottenness of man. This word, in my mouth, is at
	least free from one suspicion: that it involves a moral accusation against
	humanity.
	- text: >-
	Young Wehling was hunched in his chair, his head in his hand. He was so
	rumpled, so still and colorless as to be virtually invisible. His
	camouflage was perfect, since the waiting room had a disorderly and
	demoralized air, too. Chairs and ashtrays had been moved away from the
	walls. The floor was paved with spattered dropcloths.
	---

	# ul2-base-nl36-en-nl for English to Dutch translation

	Fine-tuned T5 model on English to Dutch translation that was pretrained on Dutch using a UL2 (Mixture-of-Denoisers) objective.
	The T5 model was introduced in
	[this paper](https://arxiv.org/abs/1910.10683)
	and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
	The UL2 objective was introduced in
	[this paper](https://arxiv.org/abs/2205.05131)
	and first released at [this page](https://github.com/google-research/google-research/tree/master/ul2).



	## Model description

	T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format.

	`ul2-base-nl36-en-nl` T5 is a transformers model fine-tuned on parallel sentence and paragraph pairs
	sampled from books.

	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:
	- GEGLU activation in the feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202)
	- Dropout was turned off during pre-training. Dropout should be re-enabled during fine-tuning
	- Pre-trained on self-supervised objective only without mixing in the downstream tasks
	- No parameter sharing between embedding and classifier layer

	The "efficient" T5 architecture findings presented in [this paper](https://arxiv.org/abs/2109.10686) were also applied,
	which suggests that a Deep-Narrow model architecture is favorable for downstream performance compared to other model
	architectures of similar parameter count. Specifically, the model depth is defined as the number of transformer blocks
	that are stacked sequentially.
	This model uses the [t5-efficient-base-nl36](https://huggingface.co/google/t5-efficient-base-nl36) architecture's
	layer depth, which means both the encoder and the decoder have 36 transformer layers compared to the original T5 "base"
	model's architecture of 12 transformer layers.

	### UL2 pretraining objective

	This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training
	paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where
	the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers
	that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of
	three denoising tasks:

	1. R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective;
	2. X-denoising (or extreme span corruption); and
	3. S-denoising (or sequential PrefixLM).

	During pre-training, we sample from the available denoising tasks based on user-specified ratios.
	UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training
	denoising task. During the pre-training, a paradigm token is inserted to the input
	(`[NLU]` for R-denoising, `[NLG]` for X-denoising, or `[S2S]` for S-denoising) indicating the denoising task at hand.
	Then, during fine-tuning the same input token should be inserted to get the best performance for different downstream
	fine-tuning tasks.

	## Intended uses & limitations

	This model was fine-tuned on parallel sentence and paragraph pairs and can be used
	for machine translation.

	### How to use

	Here is how to use this model in PyTorch:

	```python
	model_name = "yhavinga/ul2-base-nl36-en-nl"
	from transformers import AutoTokenizer
	from transformers import AutoModelForSeq2SeqLM
	from transformers import pipeline
	import torch
	device_num = 0 if torch.cuda.is_available() else -1
	device = "cpu" if device_num < 0 else f"cuda:{device_num}"

	tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
	model = AutoModelForSeq2SeqLM.from_pretrained(model_name, use_auth_token=True).to(
	device
	)
	params = {"max_length": 370, "num_beams": 4, "early_stopping": True}
	translator = pipeline("translation", tokenizer=tokenizer, model=model, device=device_num)
	print(translator("Young Wehling was hunched in his chair, his head in his hand. He was so rumpled, so still and colorless as to be virtually invisible.",
	**params)[0]['translation_text'])
	```


	### Limitations and bias

	The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral.
	Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.

	## Training data

	The `ul2-base-nl36-en-nl` T5 model was pre-trained simultaneously on a combination of several datasets,
	including the `full` config of the "mc4_nl_cleaned" dataset, which is a cleaned version of Common Crawl's web
	crawl corpus, Dutch books, the Dutch subset of Wikipedia (2022-03-20), and a subset of "mc4_nl_cleaned"
	containing only texts from Dutch newspapers.

	After pre-training, the model was
	fine-tuned on a translation dataset containing 13 million sentence and paragraph pairs
	sampled from books.

	## Training procedure

	### Preprocessing

	The ul2-base-nl36-en-nl T5 model uses a SentencePiece unigram tokenizer with a vocabulary of 32,000 tokens.
	The tokenizer includes the special tokens `<pad>`, `</s>`, `<unk>`, known from the original T5 paper,
	`[NLU]`, `[NLG]` and `[S2S]` for the MoD pre-training, and `<n>` for newline.
	During pre-training with the UL2 objective, input and output sequences consist of 512 consecutive tokens.
	The tokenizer does not lowercase texts and is therefore case-sensitive; it distinguises
	between `dutch` and `Dutch`.
	Additionally, 100+28 extra tokens were added for pre-training tasks, resulting in a total of 32,128 tokens.

	### Fine-tuning

	This model was fine-tuned on a dataset containing 13M sentence and paragraph translation pairs sampled from books.

	* Pre-trained model used as starting point: yhavinga/ul2-base-nl36-dutch
	* Amount of fine-tune training steps: 43415
	* Batch size: 512 (gradient accumulation steps: 16)
	* Sequence length: 370 tokens
	* Model dtype: bfloat16
	* z_loss: 0.0001
	* Optimizer: adamw_hf beta1: 0.9 beta2: 0.9969 eps: 1e-08
	* Dropout rate: 0.01
	* Learning rate: 0.0009 with linear decay to 0 and warmup for 500 steps
	* Label smoothing factor: 0.11
	* Bleu score: 44.2

	### Model list

	Models in this series:


	\| \| ul2-base-en-nl \| ul2-base-nl36-en-nl \| ul2-large-en-nl \|
	\|:---------------------\|:-----------------\|:----------------------\|:------------------\|
	\| model_type \| t5 \| t5 \| t5 \|
	\| _pipeline_tag \| translation \| translation \| translation \|
	\| d_model \| 768 \| 768 \| 1024 \|
	\| d_ff \| 2048 \| 3072 \| 2816 \|
	\| num_heads \| 12 \| 12 \| 16 \|
	\| d_kv \| 64 \| 64 \| 64 \|
	\| num_layers \| 12 \| 36 \| 24 \|
	\| num_decoder_layers \| 12 \| 36 \| 24 \|
	\| feed_forward_proj \| gated-silu \| gated-silu \| gated-silu \|
	\| dense_act_fn \| silu \| silu \| silu \|
	\| vocab_size \| 32128 \| 32128 \| 32128 \|
	\| tie_word_embeddings \| 0 \| 0 \| 0 \|
	\| torch_dtype \| float32 \| float32 \| float32 \|
	\| _gin_batch_size \| 128 \| 64 \| 64 \|
	\| _gin_z_loss \| 0.0001 \| 0.0001 \| 0.0001 \|
	\| _gin_t5_config_dtype \| 'bfloat16' \| 'bfloat16' \| 'bfloat16' \|

	## Evaluation results

	See the evaluation section in the interactive [Pre-training Dutch T5 Models](https://huggingface.co/spaces/yhavinga/pre-training-dutch-t5-models) blog.

	## Acknowledgements

	This project would not have been possible without compute generously provided by Google through the
	[TPU Research Cloud](https://sites.research.google/trc/).
	Thanks to the [Finnish-NLP](https://huggingface.co/Finnish-NLP) authors for releasing their code for the UL2 objective and associated task definitions.
	Thanks to [Stephenn Fernandes](https://huggingface.co/StephennFernandes) for helping me get started with the t5x framework.

	Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)