Hugging Face's logo

yhavinga
/
t5-eff-xl-8l-dutch-english-cased

Text2Text Generation
Transformers
JAX
Dutch
English
t5
seq2seq
text-generation-inference
Model card Files Community
Yeb Havinga commited on
Commit
b0dadc9
•
1 Parent(s): ee4d034

Autoupdate README.md

Browse files
Files changed (1) hide show
  1. README.md +155 -0
README.md ADDED
@@ -0,0 +1,155 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - nl
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-eff-xl-8l-dutch-english-cased
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 🇳🇱🇧🇪 mC4and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned).
18
+
19
+
20
+ This **t5 eff** model has **1240M** parameters.
21
+ It was pre-trained on the dataset
22
+ `mc4_nl_cleaned` config `large_en_nl` for **1** epoch(s) and a duration of **4d 19h**,
23
+ with a sequence length of **512**, batch size **64** and **538k/1703705** total steps.
24
+ Pre-training evaluation loss and accuracy are **1,3019** and **0,71**.
25
+
26
+
27
+ * 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.
28
+ * For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
29
+ the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!
30
+
31
+ Please refer to the original T5 papers and Scale Efficiently papers for more information about the T5 architecture
32
+ and configs, though it must be noted that this model (t5-eff-xl-8l-dutch-english-cased) is unrelated to these projects and not an 'official' checkpoint.
33
+ * **[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*.
34
+ * **[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*.
35
+
36
+
37
+ ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
38
+
39
+
40
+ ## Tokenizer
41
+
42
+ The model uses a cased SentencePiece tokenizer configured with the `Nmt, NFKC, Replace multi-space to single-space` normalizers
43
+ and has 32003 tokens.
44
+ 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).
45
+ See [./raw/main/tokenizer.json](tokenizer.json) for details.
46
+
47
+ ## Dataset
48
+
49
+ All models listed below are trained on
50
+ [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
51
+ which is the original mC4, except
52
+
53
+ * 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
54
+ * Sentences with less than 3 words are removed
55
+ * Sentences with a word of more than 1000 characters are removed
56
+ * Documents with less than 5 sentences are removed
57
+ * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
58
+ "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
59
+
60
+ The Dutch and English models are trained on a 50/50% mix of Dutch mC4 and English C4.
61
+
62
+ ## Models
63
+
64
+ Three types of models have been trained. `t5-base-dutch` is the only model with an original T5 config.
65
+ The other model types t5-v1.1 and t5-eff have `gated-relu` instead of `relu` as activation function,
66
+ and trained with a drop-out of `0.0` unless training would diverge (`t5-v1.1-large-dutch-cased`).
67
+ The T5-eff models are models with mostly different numbers of layers. The table will list
68
+ the several dimensions of these models. Note that `efficient` is a misnomer for models with few layers,
69
+ e.g. `t5-xl-4L-dutch-english-cased`, that is not efficient and one of the worst models on downstream summarization.
70
+
71
+ | | 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 |
72
+ |:------------------|:----------------|:-----------------------------|:---------------------------|:----------------------------|:-----------------------------------|:----------------------------------------|:-----------------------------|:-------------------------------|:----------------------------------|:-----------------------------------|:--------------------------------------|
73
+ | 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 |
74
+ | d_model | 768 | 768 | 768 | 1024 | 768 | 768 | 512 | 2048 | 768 | 1024 | 1024 |
75
+ | d_ff | 3072 | 2048 | 2048 | 2816 | 2048 | 2048 | 1920 | 5120 | 2560 | 16384 | 4096 |
76
+ | num_heads | 12 | 12 | 12 | 16 | 12 | 12 | 8 | 32 | 12 | 32 | 16 |
77
+ | d_kv | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 128 | 64 |
78
+ | num_layers | 12 | 12 | 12 | 24 | 12 | 12 | 24 | 4 | 36 | 8 | 8 |
79
+ | num parameters | 223M | 248M | 248M | 783M | 248M | 248M | 250M | 585M | 729M | 1241M | 335M |
80
+ | feed_forward_proj | relu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu |
81
+ | dropout | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 |
82
+ | 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 |
83
+ | tr. seq len | 512 | 1024 | 1024 | 512 | 512 | 1024 | 512 | 512 | 512 | 512 | 512 |
84
+ | batch size | 128 | 64 | 64 | 64 | 128 | 64 | 128 | 512 | 512 | 64 | 128 |
85
+ | total steps | 527500 | 1014525 | 1210154 | 2427498 | 2839630 | 1520k/3397024 | 851852 | 212963 | 212963 | 538k/1703705 | 851850 |
86
+ | epochs | 1 | 2 | 2 | 2 | 10 | 4 | 1 | 1 | 1 | 1 | 1 |
87
+ | duration | 2d9h | 5d5h | 6d6h | 8d13h | 11d18h | 9d1h | 4d10h | 6d1h | 17d15h | 4d 19h | 3d 23h |
88
+ | optimizer | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor |
89
+ | lr | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.009 | 0.005 | 0.005 |
90
+ | warmup | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 5000.0 | 20000.0 | 2500.0 | 1000.0 | 1500.0 | 1500.0 |
91
+ | eval loss | 1,38 | 1,20 | 0,96 | 1,07 | 1,11 | 1,13 | 1,18 | 1,27 | 1,05 | 1,3019 | 1,15 |
92
+ | eval acc | 0,70 | 0,73 | 0,78 | 0,76 | 0,75 | 0,74 | 0,74 | 0,72 | 0,76 | 0,71 | 0,74 |
93
+
94
+ ## Evaluation on summarization
95
+
96
+ The models below have been evaluated on the summarization downstream task on 50K samples from the CNN Dailymail dataset.
97
+ All models were fine-tuned with the AdamW optimizer with a batch size of 128 and constant learning rate of 1e-3 after a
98
+ warmup of 64 steps, with a label smoothing factor of 0.05.
99
+ Article and summary token lengths were set to 1024 and 142.
100
+
101
+ | | 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 |
102
+ |:-------------------|:----------------|:-----------------------------|:---------------------------|:-----------------------------------|:----------------------------------------|:-----------------------------|:-------------------------------|:----------------------------------|:--------------------------------------|:-----------|
103
+ | rouge1 | 33.0313 | 33.8432 | 34.0906 | 33.1116 | 34.6465 | 34.376 | 30.8983 | 35.0931 | 33.9293 | 33.6466 |
104
+ | rouge2 | 12.9452 | 13.7706 | 13.6203 | 13.275 | 13.8525 | 13.8939 | 11.6005 | 14.3823 | 13.6274 | 13.1085 |
105
+ | rougeL | 23.7204 | 24.5642 | 24.7304 | 24.3561 | 24.721 | 25.2496 | 22.6536 | 25.3213 | 24.5595 | 23.909 |
106
+ | rougeLsum | 29.842 | 30.7783 | 31.1438 | 30.0548 | 31.6104 | 31.3838 | 27.8467 | 32.3526 | 30.952 | 30.5054 |
107
+ | gen_len | 90.488 | 91.832 | 92.122 | 89.583 | 98.333 | 90.442 | 92.342 | 96.832 | 95.057 | 96.312 |
108
+ | num parameters | 223M | 248M | 248M | 248M | 248M | 250M | 585M | 729M | 335M | 582M |
109
+ | samples_per_second | 3.195 | 3.039 | 3.0 | 3.216 | 2.974 | 1.594 | 2.47 | 0.623 | 3.087 | 1.201 |
110
+
111
+ ## Translation models
112
+
113
+ The small 24L and base 36L models have been fine-tuned for translation on the CCMatrix dataset.
114
+ The models named *-`multi` support both directions of translation. The models are trained on CCMatrix only. As this is
115
+ a really large dataset with over 100M Dutch-English sentence pairs, the models are trained on a fraction of it,
116
+ refer to the table below for how long. Evaluation is performed on a CCMatrix section not trained on, but also
117
+ on Tatoeba and Opus Books. The `_bp` columns list the *brevity penalty*. The `avg_bleu` score is the bleu score
118
+ averaged over all three evaluation datasets.
119
+
120
+ The translation metrics are listed in the table below:
121
+
122
+ | | 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 |
123
+ |:-----------------------|:-----------------------------|:-----------------------------|:-----------------------------|:------------------------------|:------------------------------|
124
+ | id | 0 | 14 | 15 | 16 | 20 |
125
+ | source_lang | en | en | nl | en | nl |
126
+ | target_lang | nl | nl | en | nl | en |
127
+ | source_prefix | translate English to Dutch: | translate English to Dutch: | translate Dutch to English: | translate English to Dutch: | translate Dutch to English: |
128
+ | tatoeba_bp | 0.9897614370103832 | 0.9736173618072754 | 0.943521164106552 | 0.9760983304454847 | 0.9406676405486575 |
129
+ | ccmatrix_bp | 0.9590750786190209 | 0.9536276245543676 | 0.9635673583308255 | 0.9517934939463099 | 0.9585648049711814 |
130
+ | opus_books_bp | 0.7478011343203491 | 0.7950194726093107 | 0.9362852511299413 | 0.770498474692027 | 0.8870675076932444 |
131
+ | tatoeba_score | 50.63006965176505 | 46.580601850286214 | 52.82030981131822 | 46.419809813946046 | 51.67887417355214 |
132
+ | ccmatrix_score | 60.33227938980884 | 56.81297258845844 | 62.836646082246254 | 57.404319674892406 | 63.08633155239932 |
133
+ | opus_books_score | 10.405013868050663 | 13.477997378535864 | 24.93113308798125 | 12.927244801365507 | 23.418552148252047 |
134
+ | avg_bleu | 40.455787636541515 | 38.95719060576017 | 46.86269632718191 | 38.91712476340132 | 46.0612526247345 |
135
+ | total steps | 78125 | 390625 | 390625 | 390625 | 390625 |
136
+ | duration | 14h | 101h | 101h | 74h | 74h |
137
+ | num_parameters | 728928000 | 728928000 | 728928000 | 249991680 | 249991680 |
138
+ | label_smoothing_factor | 0.09 | 0.15 | 0.15 | 0.1 | 0.1 |
139
+ | learning_rate | 0.0001 | 5e-05 | 5e-05 | 0.0005 | 0.0005 |
140
+
141
+ ## Acknowledgements
142
+
143
+ This project would not have been possible without compute generously provided by Google through the
144
+ [TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem and was also
145
+ instrumental all parts of the training. Logging metrics to Weights & Biases made it possible to keep track of many
146
+ models and orchestrate hyper-parameter sweeps with insightful visualizations. I cannot imagine how I would
147
+ have completed this project 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
+