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t5-v1.1-base-dutch-uncased

Text2Text Generation
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JAX
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Dutch
t5
seq2seq
text-generation-inference
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@@ -4,29 +4,51 @@ language:
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  datasets:
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  - yhavinga/mc4_nl_cleaned
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  tags:
 
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  - seq2seq
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- - lm-head
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- license: apache-2.0
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  inference: false
 
11
  ---
12
 
13
- # T5-base pre-trained on cleaned Dutch mC4 🇳🇱
 
 
 
14
 
15
- A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) v1.1 base model pre-trained from scratch on [Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
16
 
17
  * Pre-trained T5 models need to be finetuned before they can be used for downstream tasks, therefore the inference widget on the right has been turned off.
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- * T5 paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
 
 
 
 
 
 
 
19
 
20
  ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
21
 
22
- ## Tokenizer
23
 
24
- * SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
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- Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
 
 
 
 
 
 
 
26
 
 
 
 
 
 
 
27
  ## Dataset
28
 
29
- All models listed below are trained on of the `full` configuration (39B tokens) of
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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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@@ -37,45 +59,97 @@ which is the original mC4, except
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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.
39
 
40
- ## Models
41
-
42
- TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model.
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-
44
- * `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021
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- Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
46
- * The two T5 v1.1 base models are an uncased and cased version of `t5-v1.1-base`, again pre-trained from scratch on Dutch,
47
- with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the
48
- base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1.
49
- * The large cased model is a pre-trained Dutch version of `t5-v1.1-large`. Training of t5-v1.1-large proved difficult.
50
- Without dropout regularization, the training would diverge at a certain point. With dropout training went better,
51
- be it much slower than training the t5-model. At some point convergance was too slow to warrant further training.
52
- The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased
53
- base model is probably the better choice.
54
 
55
- | | model | train seq len | acc | loss | batch size | epochs | steps | dropout | optim | lr | duration |
56
- |---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------|
57
- | [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | T5 | 512 | 0,70 | 1,38 | 128 | 1 | 528481 | 0.1 | adafactor | 5e-3 | 2d 9h |
58
- | [yhavinga/t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | t5-v1.1 | 1024 | 0,73 | 1,20 | 64 | 2 | 1014525 | 0.0 | adafactor | 5e-3 | 5d 5h |
59
- | [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | t5-v1.1 | 1024 | **0,78** | **0,96** | 64 | 2 | 1210000 | 0.0 | adafactor | 5e-3 | 6d 6h |
60
- | [yhavinga/t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) | t5-v1.1 | 512 | 0,76 | 1,07 | 64 | 1 | 1120000 | 0.1 | adafactor | 5e-3 | 86 13h |
61
-
62
- The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.
63
-
64
- | | model | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration |
65
- |-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------|
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- | [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,8 | 13,6 | 25,2 | 32,1 | 79 | 6 | 64 | 26916 | 2h 40m |
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- | [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,4 | 13,6 | 25,3 | 31,7 | 81 | 5 | 16 | 89720 | 11h |
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69
 
70
  ## Acknowledgements
71
 
72
  This project would not have been possible without compute generously provided by Google through the
73
- [TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
74
- instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM,
75
- and training the models:
 
 
76
 
77
  * [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
78
- * [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
79
  * [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
80
 
81
- Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
 
 
4
  datasets:
5
  - yhavinga/mc4_nl_cleaned
6
  tags:
7
+ - t5
8
  - seq2seq
9
+
 
10
  inference: false
11
+ license: apache-2.0
12
  ---
13
 
14
+ # t5-v1.1-base-dutch-uncased
15
+
16
+ A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
17
+ pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 ${and_english}](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
18
 
 
19
 
20
  * Pre-trained T5 models need to be finetuned before they can be used for downstream tasks, therefore the inference widget on the right has been turned off.
21
+ * For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
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+ the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!
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+
24
+ Please refer to the original T5 papers and Scale Efficiently papers for more information about the T5 architecture
25
+ and configs, though it must be noted that this model (${rec["name"]}) is unrelated to these projects and not an 'official' checkpoint.
26
+ * **[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)** by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*.
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+ * **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
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+
29
 
30
  ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
31
 
 
32
 
33
+ This **t5-v1.1** model has **247M** parameters.
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+ It was pre-trained on the dataset
35
+ `mc4_nl_cleaned` config `full` for **2** epoch(s) and a duration of **5d5h**,
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+ with a sequence length of **1024**, batch size **64** and **1014525** total steps.
37
+ Pre-training evaluation loss and accuracy are **1,20** and **0,73**.
38
+ After fine-tuning on 25K samples of Dutch CNN summarization, the Rouge1 score is **33.8**
39
+ (note: this evaluation model was not saved).
40
+
41
+ ## Tokenizer
42
 
43
+ The model uses an uncased SentencePiece tokenizer configured with the `Nmt, NFKC, Replace multi-space to single-space, Lowercase` normalizers
44
+ and has 32003 tokens.
45
+ It was trained on Dutch
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+ with scripts from the Huggingface Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
47
+ See [https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased/raw/main/tokenizer.json](tokenizer.json) for details.
48
+
49
  ## Dataset
50
 
51
+ All models listed below are trained on
52
  [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
53
  which is the original mC4, except
54
 
 
59
  * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
60
  "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
61
 
62
+ The Dutch and English models are trained on a 50/50% mix of Dutch mC4 and English C4.
 
 
 
 
 
 
 
 
 
 
 
 
 
63
 
64
+ ## Models
 
 
 
 
 
 
 
 
 
 
 
 
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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`).
69
+ The T5-eff models are models with mostly different numbers of layers. The table will list
70
+ the several dimensions of these models. Note that the `efficient` is a misnomer for models with few layers,
71
+ 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 |
85
+ | 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 |
90
+ | optimizer | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor |
91
+ | 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 |
93
+ | eval loss | 1,38 | 1,20 | 0,96 | 1,07 | 1,11 | 1,13 | 1,18 | 1,27 | 1,05 | 1,3019 | 1,15 |
94
+ | eval acc | 0,70 | 0,73 | 0,78 | 0,76 | 0,75 | 0,74 | 0,74 | 0,72 | 0,76 | 0,71 | 0,74 |
95
+
96
+ ## Evaluation on summarization
97
+
98
+ The models below have been evaluated on the summarization downstream task on 50K samples from the CNN Dailymail dataset.
99
+ All models were fine-tuned with the AdamW optimizer with a batch size of 128 and constant learning rate of 1e-3 after a
100
+ warmup of 64 steps, with a label smoothing factor of 0.05.
101
+ Article and summary token lengths were set to 1024 and 142.
102
+
103
+ | | 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 |
104
+ |:-------------------|:----------------|:-----------------------------|:---------------------------|:-----------------------------------|:----------------------------------------|:-----------------------------|:-------------------------------|:----------------------------------|:--------------------------------------|:-----------|
105
+ | 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 |
109
+ | gen_len | 90.488 | 91.832 | 92.122 | 89.583 | 98.333 | 90.442 | 92.342 | 96.832 | 95.057 | 96.312 |
110
+ | num parameters | 223M | 248M | 248M | 248M | 248M | 250M | 585M | 729M | 335M | 582M |
111
+ | samples_per_second | 3.195 | 3.039 | 3.0 | 3.216 | 2.974 | 1.594 | 2.47 | 0.623 | 3.087 | 1.201 |
112
+
113
+ ## Translation models
114
+
115
+ The small 24L and base 36L models have been fine-tuned for translation on the CCMatrix dataset.
116
+ The models with `multi` support two directions of translation. The models are trained on CCMatrix only. As this is
117
+ a really large dataset with over 100M Dutch-English sentence pairs, the models are trained on a fraction of it,
118
+ refer to the table below for how long. Evaluation is performed on a CCMatrix section not trained on, but also
119
+ on Tatoeba and Opus Books.
120
+
121
+ The translation metrics are listed in the table below:
122
+
123
+ | | 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 |
124
+ |:-----------------------|:-----------------------------|:-----------------------------|:-----------------------------|:------------------------------|:------------------------------|
125
+ | id | 0 | 14 | 15 | 16 | 20 |
126
+ | source_lang | en | en | nl | en | nl |
127
+ | target_lang | nl | nl | en | nl | en |
128
+ | 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 |
130
+ | ccmatrix_bp | 0.9590750786190209 | 0.9536276245543676 | 0.9635673583308255 | 0.9517934939463099 | 0.9585648049711814 |
131
+ | opus_books_bp | 0.7478011343203491 | 0.7950194726093107 | 0.9362852511299413 | 0.770498474692027 | 0.8870675076932444 |
132
+ | tatoeba_score | 50.63006965176505 | 46.580601850286214 | 52.82030981131822 | 46.419809813946046 | 51.67887417355214 |
133
+ | ccmatrix_score | 60.33227938980884 | 56.81297258845844 | 62.836646082246254 | 57.404319674892406 | 63.08633155239932 |
134
+ | opus_books_score | 10.405013868050663 | 13.477997378535864 | 24.93113308798125 | 12.927244801365507 | 23.418552148252047 |
135
+ | avg_bleu | 40.455787636541515 | 38.95719060576017 | 46.86269632718191 | 38.91712476340132 | 46.0612526247345 |
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+ | total steps | 78125 | 390625 | 390625 | 390625 | 390625 |
137
+ | duration | 14h | 101h | 101h | 74h | 74h |
138
+ | num_parameters | 728928000 | 728928000 | 728928000 | 249991680 | 249991680 |
139
+ | label_smoothing_factor | 0.09 | 0.15 | 0.15 | 0.1 | 0.1 |
140
+ | learning_rate | 0.0001 | 5e-05 | 5e-05 | 0.0005 | 0.0005 |
141
 
142
  ## Acknowledgements
143
 
144
  This project would not have been possible without compute generously provided by Google through the
145
+ [TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem and was also
146
+ instrumental all parts of the training. Logging metrics to Weights & Biases made it possible to keep track of many
147
+ models and also some hyper-paramater sweeps, I could not imagine how I would have completed this work otherwise.
148
+ The following repositories where helpful in setting up the TPU-VM,
149
+ and getting an idea what sensible hyper-parameters are for training gpt2 from scratch.
150
 
151
  * [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
 
152
  * [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
153
 
154
+ Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
155
+