metadata
base_model: google/t5-v1_1-base
tags:
- datadreamer
- datadreamer-0.18.0
- synthetic
- openai-community/gpt2
- openai-community/gpt2
- text2text-generation
widget:
- text: >-
Note that not all scientists will apply, but there may be a handful.
The abstract can be downloaded from the papers cited in the paper for use
within your project. We also recommend posting the results of the
experiment, using our mailing list format, on these pages.
For other papers, see How to obtain the data from your source publication
in NLP.
This project was last reported with NLP 3.10.6. The journal publishes NLP
3.10.6 once every seven years.
example_title: Example 1
- text: >-
No supporting documents.
The URL is http://csjn.acscentral.org/article/9780-1222-116600-3,
arxiv.org/abs/12220153. Inline citations accepted.
See http://sciencebook.org/reviews/2013/11/27/is-math-theory or
https://www.npr.org/content/early/2012/5/17/209732.full
Read more.
Related articles and papers by Jonathan Blumberg.
Books
Gottfried Wernick (2013), The Mathematics of Arithmetic and Mathematical
Environments. Journal of Linear Science, 1:1. ISBN 97803544-01-1 CrossRef
Full Text
Mikayla Sotjianis and Peter Kudzimbe (2008), Mathematical Applications of
Arxiv: Arithmetic in the Riemann–Kosmogorov Puzzle: Results from A
Simulation. Riemann–Kosmogorov Puzzle, 1:1. ISBN 978-1-415-4589-6 Google
Scholar
Thomas M. Leeson, Benjamin Gagnon, Paul E. Sowardson, Mark J. Alder,
Robert F. Blanchard, Alan K. O'Brien, and Alan B. Caffey (2013),
Statistical Analysis for Probabilistic Complexity. J. Prodd. Math, 6:3157.
Google Scholar Crossref, ISI
Schlott and Gee (2013), Theory of Differential Order and Complexity:
Exploring the Complexness and Complexness of the Efficient and Operative
Eigenvalues, 5th ed. Berkeley, CA: Google Scholar
Cafu K. Nixen (1990), Computational Statistics with RISC for the
Riemann–Kosmogorov Puzzle. L.Citation: 16352909
Konrad, A. M., F. Gomes, J. J. Fortunini, and M. Mascariel (2011), The LSE
and Kratz scale of polynomials (LSE = n polynomials). Environ., 36:3109.
Google Scholar SAGE Journals, ISI
Friesberg, P. A., E. R. Hirsch, F. M. Schubert, R. Oskarbrunner, L.
Eckermeyer Cen. G. Ziemann, P. W. Ziemann (2015), Mathematical
Mathematical Formulae. Proc. ICLS, 67, 471–482. doi: 10.1023/jpj.1516085
PubMed Abstract | CrossRef Full Text | Google Scholar
McNally, R. P., Gagnon, D. G. Trenberth, M. S., and E. P. Hildebrandt
(2010), Analysis of the Arithmetic of K(−4)\. J. Probabil. Exp. Prob.
Prod., 59:738–749. doi: 10.1308/JPM-C4S1020-0815.55509864 PubMed Abstract
| CrossRef Full Text | Google Scholar
example_title: Example 2
- text: >-
You will get:
A short overview of NLP research paper A review of all scientific articles
related to the subject (in alphabetical order). You will understand why
authors of journals using NLP paper are using NLP papers. Authorly search
for: A list of the papers cited. To add citations, include all of your
abstracts on top. Review and publish published papers of NLP and arXiv
papers in the subject for this NLP paper, as well as for all other papers
submitted for publication.
example_title: Example 3
pipeline_tag: text2text-generation
Model Card
Example Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained('cornelliusyudhawijaya/abstracts_to_post_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('cornelliusyudhawijaya/abstracts_to_post_model', revision=None) # Load model
pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)
inputs = ['Note that not all scientists will apply, but there may be a handful.\n\nThe abstract can be downloaded from the papers cited in the paper for use within your project. We also recommend posting the results of the experiment, using our mailing list format, on these pages.\n\nFor other papers, see How to obtain the data from your source publication in NLP.\n\nThis project was last reported with NLP 3.10.6. The journal publishes NLP 3.10.6 once every seven years.']
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.