metadata
base_model: google/t5-v1_1-base
tags:
- datadreamer
- datadreamer-0.28.0
- synthetic
- openai-community/gpt2
- openai-community/gpt2
- text2text-generation
widget:
- text: >-
Acknowledgments
Thank you to all our donors. Their input was invaluable, and many of them
have kept this program active. I really appreciate some privacy concerns
with these papers and the paper itself. However, thank you to my research
team for helping get the entire research protocol up and running since
2010. It's been absolutely stunning for me to be a part of such a small
organization, but when something like this happens, it is such a huge
deal. It means it's hard not to get involved.
You will also get a new Open Science Foundation letter if you donate and
support NLP. I know I am more than qualified to help you in any way you
get involved. Thank you in advance.
As an additional thanks-good-ness, at the risk of repeating some of a
large list, I will do an accompanying Google Hangout. The Hangout is where
you can send an email at nlp-doc@umass-edu. In my time as a speaker, we'll
do an ongoing Hangout video series and maybe even a live talk. The
original YouTube channel is hosted here.
If you have any questions or concerns or would like to talk to a team
member, write to my Open Science Committee through this website below or
send your comments directly to me. Thanks.
example_title: Example 1
- text: >-
Findings:
For those of you whose work relies on an annotation, it may be helpful to
check out my previous work by Fotus, Dandridgea, and Chudley on generative
models. All were very effective! If there could be the opportunity for
more work like that, it's great to have more!
This work was adapted verbatim into a textbook paper as a guest post for
Kornbirch's "I, The Computer Science Professorship": The Importance of an
Annotations " for an excellent article.
This paper provides three new tools built into each, namely an online
parser, two independent dictionaries, and a simple formulae for a variety
of information types.
It was suggested that one or two authors could contribute their work to
make the paper worthwhile by bringing a few new ideas or topics into the
field. After learning how to extract such things, we now need to make it
possible for authors to share what they want to talk about, even if this
entails writing less text.
In addition to publishing annotations, annotations represent a new tool. A
user's ability in their everyday life to learn with their own hands is
vital to make good annotation tools. Annotations have the additional
benefit of being simple to use and fast to produce. And these annotations
can, given the nature of their usage, provide the motivation needed to
improve, too.
With support for this paper from The University of Chicago, we now
consider creating my own annotated text corpus. After a cursory search on
the abstracts, a search of the articles in this article, one of my other
annotated texts on annotation, had already been selected. And as long as
this corpus was already available to help my other annotated articles in
progress, we can go ahead and create our own custom annotations of my own
work.
Notes
example_title: Example 2
- text: >-
This program will create some data, like this chart:
That's much nicer.
The author, I have the following project information (details, contact
info, etc)::
Name
Date posted last year
Title
Subject
Comments on Papers
Notes
Pseudoscience & Information
In my first presentation of a dataset, I said I had to send people out on
my own for three days/event hours (to "keep the record warm"). I took this
data, which you can buy and download on my web site, and found that the
participants had been involved (3 days to 2 days plus 4 hours in "special
situations" between 4, 5 and 8 a.m., etc). I was going to use "research"
as my main interest group; hence I wrote that research report in a simple
"research summary sheet" called Acknowledgements. This time, my focus was
on making this research report available for the public, because many
researchers will have seen it or read it and would be pleased to be able
to send out paper in their papers or give a grant for this project to try
to help others. I also want to provide people useful information that
could help them improve their abilities to participate in important
scientific research.
If you find your data useful, please share this information using the
mailing list and Twitter or any other means where you can email me:
[email protected]
Thank You.
Robert
J.D. Wimp
University of Chicago Libraries
Chicago
Email
Message To
To
To
Dear J.O.,
I would like to welcome each and every person of great interest within the
U.S. of B.S. B.S. to you and I will keep an e-mail list at
http://usb.beyond.ucla.ac.uk for this purpose. One last note about this
program. During my presentation I referenced this program, but I also
discussed the nature, length (days) and size of submissions. I would add
this program in a follow up question I did at that address to people who
think the use of these programs is wrong, i.e. they know of the lack of
research using "Research" for their "inform" (rather than "science")
(hereinafter referred to as "the web page").
I would like to clarify: I was only talking to people who were interested
and interested in improving their ability at their research work to send
out "research" for their peer-reviewed journals. That means I was talking
to some non-scientific professional, that did have a field interest, to
ask questions on this program as well. As of this writing, I can confirm
that there have been NO references to using "Research" or this data. Any
other question that needs to be addressed by researchers for the
"experience" of being a part of future research is welcome.
Sincerely...
J.E.W.
University of Wisconsin --
Michaela
Your research papers should not be used without an active or even active
peer (not in direct relation to the actual papers/events or "situations").
Research must be presented with open, serious scholarly discussions of the
main research findings.
You also shouldn't use "Tribality" as a proxy to describe the nature of
certain types of study, since this type doesn't offer that useful
understanding of many important issues of the study or of the subject. As
an example, I've seen that there are researchers in psychology, where
"tribality" refers to how they describe research from areas that could
provide useful benefits. However, to compare this research with
"tribality" you need to look at those cases with fewer "tribalities".
Because I'm dealing with this research, I'm not actually talking about
"dementia", here or elsewhere. Instead, I'm writing about the treatment
for "dementia" or the cause of people's problem when they don't interact
with the subject. There may be some "tribalities".
Because the people who do all this work are usually from marginalized
populations I've done research projects with, those people are more likely
to engage actively in research (whether by looking to create new or
expanded scientific papers, because this is not being done; that is
usually where resources are sparse), making it harder for others.
These are the people whose lives make them less productive (in terms Of
being physically active and experiencing life more often and feeling
healthier (I think); most of these studies use "tragic illness", in the
sense that people with these conditions are most productive and thus less
able to make improvements.
The people who take advantage of this have nothing to do with our culture
and can participate
example_title: Example 3
pipeline_tag: text2text-generation
Model Card
Example Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained('jeffbritts/abstracts_to_post_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('jeffbritts/abstracts_to_post_model', revision=None) # Load model
pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)
inputs = ["Acknowledgments\n\nThank you to all our donors. Their input was invaluable, and many of them have kept this program active. I really appreciate some privacy concerns with these papers and the paper itself. However, thank you to my research team for helping get the entire research protocol up and running since 2010. It's been absolutely stunning for me to be a part of such a small organization, but when something like this happens, it is such a huge deal. It means it's hard not to get involved.\n\nYou will also get a new Open Science Foundation letter if you donate and support NLP. I know I am more than qualified to help you in any way you get involved. Thank you in advance.\n\nAs an additional thanks-good-ness, at the risk of repeating some of a large list, I will do an accompanying Google Hangout. The Hangout is where you can send an email at nlp-doc@umass-edu. In my time as a speaker, we'll do an ongoing Hangout video series and maybe even a live talk. The original YouTube channel is hosted here.\n\nIf you have any questions or concerns or would like to talk to a team member, write to my Open Science Committee through this website below or send your comments directly to me. Thanks."]
print(pipe(inputs, max_length=512, do_sample=False))
This model was trained with a synthetic dataset with DataDreamer 🤖💤. The synthetic dataset card and model card can be found here. The training arguments can be found here.