Update README.md

README.md

@@ -80,64 +80,4 @@ and getting an idea what sensible hyper-parameters are for training gpt2 from sc
 * [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
 * [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
 
-Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
-## Tokenizer
-
-* SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
-  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
-
-## Dataset
-
-All models listed below are trained on of the `full` configuration (39B tokens) of
-[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
-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](https://github.com/LDNOOBW/List-of-Dirty-Naughty-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
-
-TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model.
-
-* `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021
-  Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
-* 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,
-  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 |
-|---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------|
-| [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    |
-| [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    |
-| [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    |
-| [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   |
-
-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 |
-|-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------|
-| [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   |
-| [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      |
-
-
-## Acknowledgements
-
-This project would not have been possible without compute generously provided by Google through the
-[TPU Research Cloud](https://sites.research.google/trc/). 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 training the models:
-
-* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
-* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
-* [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
-
 Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)