README.md

---
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\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."
    example_title: "Example 1"
  - text: "Findings:\n\nFor 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!\n\nThis 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.\n\nThis 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.\n\nIt 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.\n\nIn 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.\n\nWith 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.\n\nNotes"
    example_title: "Example 2"
  - text: "This program will create some data, like this chart:\n\n\nThat's much nicer.\n\n\nThe author, I have the following project information (details, contact info, etc)::\n\nName\n\nDate posted last year\n\nTitle\n\nSubject\n\nComments on Papers\n\nNotes\n\nPseudoscience & Information\n\nIn 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.\n\n\nIf 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]\n\n\nThank You.\n\nRobert\n\nJ.D. Wimp\n\nUniversity of Chicago Libraries\n\nChicago\n\nEmail\n\nMessage To\n\nTo\n\nTo\n\n\nDear J.O.,\n\nI 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\").\n\n\nI 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.\n\n\nSincerely...\n\nJ.E.W.\n\nUniversity of Wisconsin --\n\nMichaela\n\n\nYour 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.\n\nYou 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\".\n\n\nBecause 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.\n\n\nThese 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.\n\n\nThe 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

[Add more information here](https://huggingface.co/templates/model-card-example)

## Example Usage

```python3
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 🤖💤](https://datadreamer.dev). The synthetic dataset card and model card can be found [here](datadreamer.json). The training arguments can be found [here](training_args.json).