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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-large-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-large-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
+
+
+
+### 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-large-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-large-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 and Belgian newspapers. This last dataset is oversampled to bias the model
+towards descriptions of events in the Netherlands and Belgium.
+
+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-large-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-large-dutch
+* Amount of fine-tune training steps: 77600
+* 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: 45.1
+
+### 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/)
+

added_tokens.json

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config.gin

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+from __gin__ import dynamic_registration
+import __main__ as train_script
+import seqio
+import t5.data.mixtures
+from t5x import adafactor
+from t5x.examples.t5 import network
+from t5x import gin_utils
+from t5x import models
+from t5x import partitioning
+from t5x import trainer
+from t5x import utils
+import tasks.nedd_tasks
+import tasks.ul2_tasks as tasks2
+
+# Macros:
+# ==============================================================================
+BATCH_SIZE = 64
+DROPOUT_RATE = 0.0
+LABEL_SMOOTHING = 0.0
+LOSS_NORMALIZING_FACTOR = None
+MIXTURE_OR_TASK_MODULE = None
+MIXTURE_OR_TASK_NAME = 'ul2_mc4_nedd_wiki_news_mix_1'
+MODEL = @models.EncoderDecoderModel()
+MODEL_DIR = 'ul2_large_mc4_nedd_wiki_news_nl'
+OPTIMIZER = @adafactor.Adafactor()
+RANDOM_SEED = None
+SHUFFLE_TRAIN_EXAMPLES = True
+TASK_FEATURE_LENGTHS = {'inputs': 512, 'targets': 512}
+TRAIN_STEPS = 1000000
+USE_CACHED_TASKS = False
+USE_HARDWARE_RNG = False
+VOCABULARY = @seqio.SentencePieceVocabulary()
+Z_LOSS = 0.0001
+
+# Parameters for adafactor.Adafactor:
+# ==============================================================================
+adafactor.Adafactor.decay_rate = 0.8
+adafactor.Adafactor.logical_factor_rules = \
+    @adafactor.standard_logical_factor_rules()
+adafactor.Adafactor.step_offset = 0
+
+# Parameters for utils.CheckpointConfig:
+# ==============================================================================
+utils.CheckpointConfig.restore = @utils.RestoreCheckpointConfig()
+utils.CheckpointConfig.save = @utils.SaveCheckpointConfig()
+
+# Parameters for utils.create_learning_rate_scheduler:
+# ==============================================================================
+utils.create_learning_rate_scheduler.base_learning_rate = 1.0
+utils.create_learning_rate_scheduler.factors = 'constant * rsqrt_decay'
+utils.create_learning_rate_scheduler.warmup_steps = 10000
+
+# Parameters for train/utils.DatasetConfig:
+# ==============================================================================
+train/utils.DatasetConfig.batch_size = %BATCH_SIZE
+train/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
+train/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
+train/utils.DatasetConfig.pack = True
+train/utils.DatasetConfig.seed = None
+train/utils.DatasetConfig.shuffle = %SHUFFLE_TRAIN_EXAMPLES
+train/utils.DatasetConfig.split = 'train'
+train/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
+train/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
+
+# Parameters for train_eval/utils.DatasetConfig:
+# ==============================================================================
+train_eval/utils.DatasetConfig.batch_size = %BATCH_SIZE
+train_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
+train_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
+train_eval/utils.DatasetConfig.pack = True
+train_eval/utils.DatasetConfig.seed = 42
+train_eval/utils.DatasetConfig.shuffle = False
+train_eval/utils.DatasetConfig.split = 'validation'
+train_eval/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
+train_eval/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
+
+# Parameters for models.EncoderDecoderModel:
+# ==============================================================================
+models.EncoderDecoderModel.input_vocabulary = %VOCABULARY
+models.EncoderDecoderModel.label_smoothing = %LABEL_SMOOTHING
+models.EncoderDecoderModel.loss_normalizing_factor = %LOSS_NORMALIZING_FACTOR
+models.EncoderDecoderModel.module = @network.Transformer()
+models.EncoderDecoderModel.optimizer_def = %OPTIMIZER
+models.EncoderDecoderModel.output_vocabulary = %VOCABULARY
+models.EncoderDecoderModel.z_loss = %Z_LOSS
+
+# Parameters for partitioning.PjitPartitioner:
+# ==============================================================================
+partitioning.PjitPartitioner.logical_axis_rules = \
+    @partitioning.standard_logical_axis_rules()
+partitioning.PjitPartitioner.model_parallel_submesh = None
+partitioning.PjitPartitioner.num_partitions = 1
+
+# Parameters for utils.RestoreCheckpointConfig:
+# ==============================================================================
+utils.RestoreCheckpointConfig.path = []
+
+# Parameters for utils.SaveCheckpointConfig:
+# ==============================================================================
+utils.SaveCheckpointConfig.dtype = 'float32'
+utils.SaveCheckpointConfig.keep = 4
+utils.SaveCheckpointConfig.period = 50000
+utils.SaveCheckpointConfig.save_dataset = False
+utils.SaveCheckpointConfig.use_gda = False
+
+# Parameters for seqio.SentencePieceVocabulary:
+# ==============================================================================
+seqio.SentencePieceVocabulary.sentencepiece_model_file = \
+    'gs://t5-dutch-english/vocabs/nedd.32000.128extra/spiece.model'
+
+# Parameters for network.T5Config:
+# ==============================================================================
+network.T5Config.dropout_rate = %DROPOUT_RATE
+network.T5Config.dtype = 'bfloat16'
+network.T5Config.emb_dim = 1024
+network.T5Config.head_dim = 64
+network.T5Config.logits_via_embedding = False
+network.T5Config.mlp_activations = ('gelu', 'linear')
+network.T5Config.mlp_dim = 2816
+network.T5Config.num_decoder_layers = 24
+network.T5Config.num_encoder_layers = 24
+network.T5Config.num_heads = 16
+network.T5Config.vocab_size = 32128
+
+# Parameters for train_script.train:
+# ==============================================================================
+train_script.train.checkpoint_cfg = @utils.CheckpointConfig()
+train_script.train.eval_period = 2000
+train_script.train.eval_steps = 20
+train_script.train.infer_eval_dataset_cfg = None
+train_script.train.model = %MODEL
+train_script.train.model_dir = %MODEL_DIR
+train_script.train.partitioner = @partitioning.PjitPartitioner()
+train_script.train.random_seed = %RANDOM_SEED
+train_script.train.stats_period = 100
+train_script.train.summarize_config_fn = @gin_utils.summarize_gin_config
+train_script.train.total_steps = %TRAIN_STEPS
+train_script.train.train_dataset_cfg = @train/utils.DatasetConfig()
+train_script.train.train_eval_dataset_cfg = @train_eval/utils.DatasetConfig()
+train_script.train.trainer_cls = @trainer.Trainer
+train_script.train.use_hardware_rng = %USE_HARDWARE_RNG
+
+# Parameters for trainer.Trainer:
+# ==============================================================================
+trainer.Trainer.learning_rate_fn = @utils.create_learning_rate_scheduler()
+trainer.Trainer.num_microbatches = None
+
+# Parameters for network.Transformer:
+# ==============================================================================
+network.Transformer.config = @network.T5Config()

config.json

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+{
+  "_name_or_path": "yhavinga/ul2-large-en-nl",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2816,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "silu",
+  "dropout_rate": 0.01,
+  "eos_token_id": 1,
+  "early_stopping": true,
+  "feed_forward_proj": "gated-silu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "max_length": 370,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_beams": 4,
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.23.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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special_tokens_map.json

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+}

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test_results.json

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tokenizer_config.json

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+}

training_state.json

@@ -0,0 +1 @@
+{"step": 1241629}