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t5-base-36L-ccmatrix-multi

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+ ---
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+ language:
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+ - nl
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+ datasets:
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+ - yhavinga/mc4_nl_cleaned
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+ - yhavinga/ccmatrix
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+ tags:
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+ - t5
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+ - translation
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+ - seq2seq
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+
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+ pipeline_tag: translation
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+ widget:
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+ - 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. It is used--and I wish to emphasize the fact again--without any moral significance: and this is so far true that the rottenness I speak of is most apparent to me precisely in those quarters where there has been most aspiration, hitherto, toward 'virtue' and 'godliness.'"
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+ - text: "For once Fletcher’s sedate features showed a certain lightness. 'I believe I will linger awhile longer.' He indicated a holoscreen which was displaying the image from an external camera. Cloud-splattered landscape was rolling past, pastel greens, browns, and blues illuminated by Duke’s radiance. 'It is not often a mortal man is permitted to view a world over the shoulder of angels.'"
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+
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+ license: apache-2.0
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+ ---
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+
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+ # t5-base-36L-ccmatrix-multi
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+
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+ A [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) model finetuned on Dutch to English and English to Dutch translation with the CCMatrix dataset.
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+ Evaluation metrics of this model are listed in the **Translation models** section below.
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+
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+ This **t5 eff** model has **728M** parameters.
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+ It was pre-trained on the dataset
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+ `mc4_nl_cleaned` config `large_en_nl` for **1** epoch(s) and a duration of **17d15h**,
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+ with a sequence length of **512**, batch size **512** and **212963** total steps.
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+ Pre-training evaluation loss and accuracy are **1,05** and **0,76**.
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+
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+
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+
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+
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+ ## Tokenizer
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+
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+ The model uses a cased SentencePiece tokenizer configured with the `Nmt, NFKC, Replace multi-space to single-space` normalizers
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+ and has 32003 tokens.
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+ It was trained on Dutch and English with scripts from the Huggingface Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
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+ See [./raw/main/tokenizer.json](tokenizer.json) for details.
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+
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+ ## Dataset
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+
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+ All models listed below are trained on
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+ [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
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+ which is the original mC4, except
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+
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+ * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
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+ * Sentences with less than 3 words are removed
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+ * Sentences with a word of more than 1000 characters are removed
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+ * Documents with less than 5 sentences are removed
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+ * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
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+ "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
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+
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+ The Dutch and English models are trained on a 50/50% mix of Dutch mC4 and English C4.
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+
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+ ## Models
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+
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+ Three types of models have been trained. `t5-base-dutch` is the only model with an original T5 config.
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+ The other model types t5-v1.1 and t5-eff have `gated-relu` instead of `relu` as activation function,
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+ and trained with a drop-out of `0.0` unless training would diverge (`t5-v1.1-large-dutch-cased`).
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+ The T5-eff models are models with mostly different numbers of layers. The table will list
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+ the several dimensions of these models. Note that `efficient` is a misnomer for models with few layers,
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+ e.g. `t5-xl-4L-dutch-english-cased`, that is not efficient and one of the worst models on downstream summarization.
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+
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+ | | t5-base-dutch | t5-v1.1-base-dutch-uncased | t5-v1.1-base-dutch-cased | t5-v1.1-large-dutch-cased | t5-v1_1-base-dutch-english-cased | t5-v1_1-base-dutch-english-cased-1024 | t5-small-24L-dutch-english | t5-xl-4L-dutch-english-cased | t5-base-36L-dutch-english-cased | t5-eff-xl-8l-dutch-english-cased | t5-eff-large-8l-dutch-english-cased |
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+ |:------------------|:----------------|:-----------------------------|:---------------------------|:----------------------------|:-----------------------------------|:----------------------------------------|:-----------------------------|:-------------------------------|:----------------------------------|:-----------------------------------|:--------------------------------------|
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+ | type | t5 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5 eff | t5 eff | t5 eff | t5 eff | t5 eff |
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+ | d_model | 768 | 768 | 768 | 1024 | 768 | 768 | 512 | 2048 | 768 | 1024 | 1024 |
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+ | d_ff | 3072 | 2048 | 2048 | 2816 | 2048 | 2048 | 1920 | 5120 | 2560 | 16384 | 4096 |
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+ | num_heads | 12 | 12 | 12 | 16 | 12 | 12 | 8 | 32 | 12 | 32 | 16 |
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+ | d_kv | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 128 | 64 |
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+ | num_layers | 12 | 12 | 12 | 24 | 12 | 12 | 24 | 4 | 36 | 8 | 8 |
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+ | num parameters | 223M | 248M | 248M | 783M | 248M | 248M | 250M | 585M | 729M | 1241M | 335M |
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+ | feed_forward_proj | relu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu |
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+ | dropout | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 |
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+ | dataset | mc4_nl_cleaned | mc4_nl_cleaned full | mc4_nl_cleaned full | mc4_nl_cleaned | mc4_nl_cleaned small_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl |
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+ | tr. seq len | 512 | 1024 | 1024 | 512 | 512 | 1024 | 512 | 512 | 512 | 512 | 512 |
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+ | batch size | 128 | 64 | 64 | 64 | 128 | 64 | 128 | 512 | 512 | 64 | 128 |
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+ | total steps | 527500 | 1014525 | 1210154 | 2427498 | 2839630 | 1520k/3397024 | 851852 | 212963 | 212963 | 538k/1703705 | 851850 |
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+ | epochs | 1 | 2 | 2 | 2 | 10 | 4 | 1 | 1 | 1 | 1 | 1 |
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+ | duration | 2d9h | 5d5h | 6d6h | 8d13h | 11d18h | 9d1h | 4d10h | 6d1h | 17d15h | 4d 19h | 3d 23h |
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+ | optimizer | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor |
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+ | lr | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.009 | 0.005 | 0.005 |
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+ | warmup | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 5000.0 | 20000.0 | 2500.0 | 1000.0 | 1500.0 | 1500.0 |
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+ | eval loss | 1,38 | 1,20 | 0,96 | 1,07 | 1,11 | 1,13 | 1,18 | 1,27 | 1,05 | 1,3019 | 1,15 |
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+ | eval acc | 0,70 | 0,73 | 0,78 | 0,76 | 0,75 | 0,74 | 0,74 | 0,72 | 0,76 | 0,71 | 0,74 |
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+
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+ ## Evaluation on summarization
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+
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+ The models below have been evaluated on the summarization downstream task on 50K samples from the CNN Dailymail dataset.
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+ All models were fine-tuned with the AdamW optimizer with a batch size of 128 and constant learning rate of 1e-3 after a
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+ warmup of 64 steps, with a label smoothing factor of 0.05.
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+ Article and summary token lengths were set to 1024 and 142.
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+
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+ | | t5-base-dutch | t5-v1.1-base-dutch-uncased | t5-v1.1-base-dutch-cased | t5-v1_1-base-dutch-english-cased | t5-v1_1-base-dutch-english-cased-1024 | t5-small-24L-dutch-english | t5-xl-4L-dutch-english-cased | t5-base-36L-dutch-english-cased | t5-eff-large-8l-dutch-english-cased | mt5-base |
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+ |:-------------------|:----------------|:-----------------------------|:---------------------------|:-----------------------------------|:----------------------------------------|:-----------------------------|:-------------------------------|:----------------------------------|:--------------------------------------|:-----------|
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+ | rouge1 | 33.0313 | 33.8432 | 34.0906 | 33.1116 | 34.6465 | 34.376 | 30.8983 | 35.0931 | 33.9293 | 33.6466 |
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+ | rouge2 | 12.9452 | 13.7706 | 13.6203 | 13.275 | 13.8525 | 13.8939 | 11.6005 | 14.3823 | 13.6274 | 13.1085 |
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+ | rougeL | 23.7204 | 24.5642 | 24.7304 | 24.3561 | 24.721 | 25.2496 | 22.6536 | 25.3213 | 24.5595 | 23.909 |
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+ | rougeLsum | 29.842 | 30.7783 | 31.1438 | 30.0548 | 31.6104 | 31.3838 | 27.8467 | 32.3526 | 30.952 | 30.5054 |
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+ | gen_len | 90.488 | 91.832 | 92.122 | 89.583 | 98.333 | 90.442 | 92.342 | 96.832 | 95.057 | 96.312 |
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+ | num parameters | 223M | 248M | 248M | 248M | 248M | 250M | 585M | 729M | 335M | 582M |
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+ | samples_per_second | 3.195 | 3.039 | 3.0 | 3.216 | 2.974 | 1.594 | 2.47 | 0.623 | 3.087 | 1.201 |
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+
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+ ## Translation models
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+
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+ The small 24L and base 36L models have been fine-tuned for translation on the CCMatrix dataset.
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+ The models named *-`multi` support both directions of translation. The models are trained on CCMatrix only. As this is
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+ a really large dataset with over 100M Dutch-English sentence pairs, the models are trained on a fraction of it,
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+ refer to the table below for how long. Evaluation is performed on a CCMatrix section not trained on, but also
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+ on Tatoeba and Opus Books. The `_bp` columns list the *brevity penalty*. The `avg_bleu` score is the bleu score
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+ averaged over all three evaluation datasets.
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+
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+ The translation metrics are listed in the table below:
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+
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+ | | t5-base-36L-ccmatrix-en-nl | t5-base-36L-ccmatrix-multi | t5-base-36L-ccmatrix-multi | t5-small-24L-ccmatrix-multi | t5-small-24L-ccmatrix-multi |
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+ |:-----------------------|:-----------------------------|:-----------------------------|:-----------------------------|:------------------------------|:------------------------------|
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+ | id | 0 | 14 | 15 | 16 | 20 |
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+ | source_lang | en | en | nl | en | nl |
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+ | target_lang | nl | nl | en | nl | en |
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+ | source_prefix | translate English to Dutch: | translate English to Dutch: | translate Dutch to English: | translate English to Dutch: | translate Dutch to English: |
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+ | tatoeba_bp | 0.9897614370103832 | 0.9736173618072754 | 0.943521164106552 | 0.9760983304454847 | 0.9406676405486575 |
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+ | ccmatrix_bp | 0.9590750786190209 | 0.9536276245543676 | 0.9635673583308255 | 0.9517934939463099 | 0.9585648049711814 |
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+ | opus_books_bp | 0.7478011343203491 | 0.7950194726093107 | 0.9362852511299413 | 0.770498474692027 | 0.8870675076932444 |
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+ | tatoeba_score | 50.63006965176505 | 46.580601850286214 | 52.82030981131822 | 46.419809813946046 | 51.67887417355214 |
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+ | ccmatrix_score | 60.33227938980884 | 56.81297258845844 | 62.836646082246254 | 57.404319674892406 | 63.08633155239932 |
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+ | opus_books_score | 10.405013868050663 | 13.477997378535864 | 24.93113308798125 | 12.927244801365507 | 23.418552148252047 |
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+ | avg_bleu | 40.455787636541515 | 38.95719060576017 | 46.86269632718191 | 38.91712476340132 | 46.0612526247345 |
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+ | total steps | 78125 | 390625 | 390625 | 390625 | 390625 |
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+ | duration | 14h | 101h | 101h | 74h | 74h |
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+ | num_parameters | 728928000 | 728928000 | 728928000 | 249991680 | 249991680 |
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+ | label_smoothing_factor | 0.09 | 0.15 | 0.15 | 0.1 | 0.1 |
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+ | learning_rate | 0.0001 | 5e-05 | 5e-05 | 0.0005 | 0.0005 |
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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/). The HuggingFace 🤗 ecosystem and was also
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+ instrumental all parts of the training. Logging metrics to Weights & Biases made it possible to keep track of many
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+ models and orchestrate hyper-parameter sweeps with insightful visualizations. I cannot imagine how I would
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+ have completed this project otherwise.
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+ The following repositories where helpful in setting up the TPU-VM,
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+ and getting an idea what sensible hyper-parameters are for training gpt2 from scratch.
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+
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+ * [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
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+ * [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
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+
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+ Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
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+