language:
- nl
datasets:
- yhavinga/mc4_nl_cleaned
- ml6team/cnn_dailymail_nl
tags:
- seq2seq
- lm-head
license: apache-2.0
inference: false
T5 v1.1 Large finetuned for CNN news summarization in Dutch 🇳🇱
This model is t5-v1.1-large-dutch-cased finetuned on CNN Dailymail NL
The inference widget on the right has been turned off. For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for the Netherformer 📰 example application!
Tokenizer
- Tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface Transformers Flax examples.
Dataset
All models listed below are trained on of the full configuration (39B tokens) of
cleaned Dutch mC4,
which is the original mC4, except
- Documents that contained words from a selection of the Dutch and English List of Dirty Naught Obscene and Otherwise Bad Words are removed
- Sentences with less than 3 words are removed
- Sentences with a word of more than 1000 characters are removed
- Documents with less than 5 sentences are removed
- Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies", "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
Models
- The first model,
t5-base-dutchis a re-training of the Dutch T5 base v1.0 model trained during the Flax/Jax community week. With training complete, accuracy was improved from 0,64 to 0,70. - The second two models are a uncased and cased version of
t5-v1.1-base, again pre-trained from scratch on Dutch, with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1. - The large cased model is a pre-trained Dutch version of
t5-v1.1-large. Training of t5-v1.1-large proved difficult. Without dropout regularization, the training would diverge at a certain point. With dropout training went better, be it much slower than training the t5-model. At some point convergance was too slow to warrant further training. The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased base model is probably the better choice.
| model | train seq len | acc | loss | batch size | epochs | steps | dropout | optim | lr | duration | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| t5-base-dutch | T5 | 512 | 0,70 | 1,38 | 128 | 1 | 528481 | 0.1 | adafactor | 5e-3 | 2d 9h |
| t5-v1.1-base-dutch-uncased | t5-v1.1 | 1024 | 0,73 | 1,20 | 64 | 2 | 1014525 | 0.0 | adafactor | 5e-3 | 5d 5h |
| t5-v1.1-base-dutch-cased | t5-v1.1 | 1024 | 0,78 | 0,96 | 64 | 2 | 1210000 | 0.0 | adafactor | 5e-3 | 6d 6h |
| t5-v1.1-large-dutch-cased | t5-v1.1 | 512 | 0,76 | 1,07 | 64 | 1 | 1120000 | 0.1 | adafactor | 5e-3 | 86 13h |
The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.
| model | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
| 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 |
Acknowledgements
This project would not have been possible without compute generously provided by Google through the TPU Research Cloud. The HuggingFace 🤗 ecosystem was also instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM, and getting an idea what sensible hyper-parameters are for training gpt2 from scratch.
Created by Yeb Havinga