How to process raw text files to create similar "PrettyBig" model?

[GenTxt]

Thanks for the repo. Have sampling working fine from your "PrettyBig" model.

I would like to generate my own dataset from 6 gigs of raw, header free Gutenberg text files
and I was wondering how this can be done using datasets/create_tfrecords.py

Using tar I've created "RS_2017-04-4_data.xz" from the raw text files and placed in "openwebtext/RS_2017-04-4_data.xz"

I've edited one of your .json files to include the paths in the required "files.json" (# This file should contain paths to all your RS_--_data. files)

run create_tfrecords.py and creates parse/RS_2017-04 folders

90 minutes later from the terminal

Parsing chunk 1 took 54.41039276123047 seconds
-- 0.0% of chunk 1's docs yielded text.
Saving chunk 1 took 1.6689300537109375e-06 seconds
Parsing chunk 2 took 49.19901156425476 seconds
-- 0.0% of chunk 2's docs yielded text.
Saving chunk 2 took 1.1920928955078125e-06 second

... parse/RS_2017-04 is still empty

Stopped at this point because I assume this is wrong. Any suggestions how I can prepare a similar model as "PrettyBig" using standard raw text files?

Cheers,

P.S Do you plan on releasing the 1.7 model?

[kizinfo]

Modify create_tfrecords.py at the top, filename and path declarations point to the text files. make sure the line:
files = glob.glob(os.path.join(base_dir, "*.txt"))
properly points to and indexes the source text files.
also need a copy of the existing model encoder.
and set files_per as the number of text files to use for each chunk of tfrecords. I already had them split into chunks with ~300 books each separated by <|EndOfText|>. the .py doesn't add an end text token so if you didn't already put them in your txt files and want them need to modify the code further.

have been able to train 4gig of text on colab TPU over a number of days to 60k iterations. Results arn't as good as finetuning existing models (yet). The biggest model that still fit colab was:

"n_head": 17,
"lr": 0.00025,
"warmup_steps": 2000,
"beta1": 0.0,
"decay_exponent": 0.8,
"opt_name": "adafactor",
"decay_type": "pow",
"train_batch_size": 8,
"max_steps": 200000,
"predict_batch_size": 1,
"eval_batch_size": 8,
"iterations": 100,
"n_embd": 1020,
"n_ctx": 1024,
 "n_layer": 34,

With such small batch sizes, I'm not sure it will ever work well and without bfloat16 working on inference can't get bigger models on colab. The author says he trained models on a pod (which with 'evalutaion' pricing would cost tens of thousands of dollars).

From the original GPT2 paper the Authors claimed that the variation in training text was important for bigger models so it might be that training a model from scratch on a domain specific 4gig corpus won't ever do as well as training on a general 40gig corpus and then finetuning on the domain.

Have been able to get some really good results from the original 345M GPT2 models by finetuning on domain specific content that maintains context well through multiple paragraphs.

[GenTxt]

Thanks for the information. A lot to test and as you say it's likely true that " ... a domain specific 4gig corpus won't ever do as well as training on a general 40gig corpus and then finetuning on the domain."

I'm having good results too with genre specific models based on the OpenAi 345M. Can only hope they decide to release their larger models within 6 months.

Closing this now

[ConnorJL]

I'm sorry the scripts are pretty poorly documented, I'm planning on making a better custom dataset setup when I get the time. You basically just want to use create_tfrecords.py as kizinfo said to generate the .tfrecords files from your txt files.

You do NOT have to add <|endoftext|> manually! If you use my bpe_text function (in inputs.py) as input, it automatically samples "stitch" amount of texts from your dataset, concatenates them with <|endoftext|> in between and then samples n_ctx amount of tokens from the final result. Make sure that "stitch" is set so that (your minimal length text * stitch) >= n_ctx.

I plan on releasing the 1.5B model, see my blogposts about it here and here.

[GenTxt]

Thanks. I got the basics working based on kizinfo's detailed reply but it didn't seem to actually reduce the error loss. I will check the scripts again as per your advice. Looking forward to testing your 1.5B model. Checking your blogs now. Cheers

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On Thu, Jun 6, 2019 at 10:25 AM ConnorJL ***@***.***> wrote: I'm sorry the scripts are pretty poorly documented, I'm planning on making a better custom dataset setup when I get the time. You basically just want to use create_tfrecords.py as kizinfo said to generate the .tfrecords files from your txt files. You do NOT have to add <|endoftext|> manually! If you use my bpe_text function (in inputs.py) as input, it automatically samples "stitch" amount of texts from your dataset, concatenates them with <|endoftext|> in between and then samples n_ctx amount of tokens from the final result. Make sure that "stitch" is set so that (your minimal length text * stitch) >= n_ctx. I plan on releasing the 1.5B model, see my blogposts about it here ***@***.***/gpt2-counting-consciousness-and-the-curious-hacker-323c6639a3a8> and here ***@***.***/replicating-gpt2-1-5b-86454a7f26af>. — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub <#2?email_source=notifications&email_token=AFMAWPNSEHUT25BDOHDNDETPZEM5LA5CNFSM4HUUW4FKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODXDALOI#issuecomment-499516857>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AFMAWPMKVACKEAQMWIA7FPLPZEM5LANCNFSM4HUUW4FA> .

[GenTxt]

Enjoyed reading your blogs and I'm in full agreement. To the points you raised I'm wondering if your models can be used for fine-tuning a custom corpus model the same as the current Open-Ai 345 version? I think this is a great part of OpenAi's fear, false as it may be, that prevents them from releasing the full code. I'm getting great results fine-tuning corpus files with nshepperd's repo and selected branches using the current OpenAi 345M For example:: https://github.com/mkturkcan/GPTune (provides pre-trained models to download) based on the finetuning code released by nshepperd The one drawback is waiting for the possible release of the larger OpenAi models to fully test these repos.. It would be fantastic if your version could be tweaked to offer the same ability. Cheers, and thanks again for the great work.

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On Thu, Jun 6, 2019 at 8:06 PM Aaron Allan ***@***.***> wrote: Thanks. I got the basics working based on kizinfo's detailed reply but it didn't seem to actually reduce the error loss. I will check the scripts again as per your advice. Looking forward to testing your 1.5B model. Checking your blogs now. Cheers On Thu, Jun 6, 2019 at 10:25 AM ConnorJL ***@***.***> wrote: > I'm sorry the scripts are pretty poorly documented, I'm planning on > making a better custom dataset setup when I get the time. You basically > just want to use create_tfrecords.py as kizinfo said to generate the > .tfrecords files from your txt files. > > You do NOT have to add <|endoftext|> manually! If you use my bpe_text > function (in inputs.py) as input, it automatically samples "stitch" amount > of texts from your dataset, concatenates them with <|endoftext|> in between > and then samples n_ctx amount of tokens from the final result. Make sure > that "stitch" is set so that (your minimal length text * stitch) >= n_ctx. > > I plan on releasing the 1.5B model, see my blogposts about it here > ***@***.***/gpt2-counting-consciousness-and-the-curious-hacker-323c6639a3a8> > and here > ***@***.***/replicating-gpt2-1-5b-86454a7f26af>. > > — > You are receiving this because you modified the open/close state. > Reply to this email directly, view it on GitHub > <#2?email_source=notifications&email_token=AFMAWPNSEHUT25BDOHDNDETPZEM5LA5CNFSM4HUUW4FKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODXDALOI#issuecomment-499516857>, > or mute the thread > <https://github.com/notifications/unsubscribe-auth/AFMAWPMKVACKEAQMWIA7FPLPZEM5LANCNFSM4HUUW4FA> > . >