metadata
base_model: google/t5-v1_1-base
tags:
- datadreamer
- datadreamer-0.28.0
- synthetic
- gpt-4
- gpt-4
- text2text-generation
widget:
- text: >-
In this paper, we present a novel method for Natural Language Processing
(NLP) based on the introduction of deep learning techniques adapted to
linguistics. We demonstrate that by integrating syntactic and semantic
analysis in pre-processing stages, superior text understanding can be
facilitated. Initial processes involve tokenization, POS-tagging,
syntactic-semantic hinging for all corpus. To further the learning
precision, we introduce a framework powered by a hybrid of Transformer and
Recurrent Neural Networks architectures that manifest in increased
efficiency both theoretically and empirically. This paper shares
exhaustive results, detailing improvements in feature engineering,
promising a reduction in human-size semantic labor. We additionally
propose that integrating deep learning methods with traditional
linguistics dramatically improves contextual understanding and performance
on tasks such as language translation, sentiment analysis, and automated
thesaurus generation. The innovations reported here make significant
strides towards realizing viable, sophisticated machine-level NLP systems.
Additionally, the research represents groundwork for further exploration
and development promising higher degrees of culture-language contextuality
and robustness integral in future NLP applications.
example_title: Example 1
- text: >-
This paper proposes a novel approach to improve performance in Natural
Language Processing (NLP) tasks by harnessing the potential of deep
learning algorithms using multilingual transformer models. Our work
investigates the challenging problem of understanding and manipulating
sentences to Toucan dialogue language in context-dependent situations. We
present a comprehensive analysis of established NLP approaches, novel
methods based on transformer models, and thorough experiments that
demonstrate substantial advancements over the state-of-art performances.
Our primary contribution lies in the intelligent integration of thematic
role labeling with multilingual models to improve the comprehension of
sentence structure; for instance, recognizing grammatical relations
irrespective of a word’s syntactic position or morphological form. In
addition, our method progresses automatic predicate argument structure
analysis, giving significance and having potential applications in tasks
such as information extraction, summarization, and machine translation. We
provide task-specific models that reveal the comparative strength of our
architecture set over a cross-lingual task. Systematic evaluations
conducted on several linguistic databases have demonstrated robust
effectiveness in extracting and reconstructing meaningful entities from
unstructured language data. The empirical results show notable
enhancements in NLP task competence and thus stimulate further research
avenues for substantial developments in multimodal natural language
understanding and endow opportunities for practical applications.
example_title: Example 2
- text: >-
In recent years, natural language processing (NLP) has seen impressive
advancements because of the advent of deep learning technologies, like
transformer-based models such as BERT., However, there remain significant
challenges in obtaining human-level understanding, notably concerning
effectively extracting semantics from context, deeper discourse analysis
and anticipatory prediction during discourse development. In this research
paper, we propose a novel integrative NLP model named Contextualized
Anticipatory Semantic Humor Analysis (CASHA), which creates a
sophisticated blend of semantic context understanding, discourse reference
instantiation, and humorous setting anticipation. Inspired by human
cognitive processing, CASHA layers a sentence-level semantic extractor and
a transformer-based discourse modelling layer harboring informal semantics
to understand intricate discourse embeddings accurately. It subsequently
employs an adaptive humor anticipation layer based logically on previous
discourse understanding. For rigorous model evaluation, we performed
several experiments across diverse data sets encompassing assorted types
of humor. Results demonstrate significantly improved performance in both
humor detection and humor semantics understanding. They prompt profound
thinking about NLP applications regarding human-level understanding of
semantics from context. This work represents a potentially influential
step in advancing the transformative urban initiatives prioritized by
smart cities-examples abound about interfaces for ordinary citizens to
interact more creatively with city experiences and for cities authorities
to react empathetically to citizen-specific humor, metaphors, and cultural
dialects.
example_title: Example 3
pipeline_tag: text2text-generation
Model Card
Example Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained('CCB/abstracts_to_tweet_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('CCB/abstracts_to_tweet_model', revision=None) # Load model
pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)
inputs = ['In this paper, we present a novel method for Natural Language Processing (NLP) based on the introduction of deep learning techniques adapted to linguistics. We demonstrate that by integrating syntactic and semantic analysis in pre-processing stages, superior text understanding can be facilitated. Initial processes involve tokenization, POS-tagging, syntactic-semantic hinging for all corpus. To further the learning precision, we introduce a framework powered by a hybrid of Transformer and Recurrent Neural Networks architectures that manifest in increased efficiency both theoretically and empirically. This paper shares exhaustive results, detailing improvements in feature engineering, promising a reduction in human-size semantic labor. We additionally propose that integrating deep learning methods with traditional linguistics dramatically improves contextual understanding and performance on tasks such as language translation, sentiment analysis, and automated thesaurus generation. The innovations reported here make significant strides towards realizing viable, sophisticated machine-level NLP systems. Additionally, the research represents groundwork for further exploration and development promising higher degrees of culture-language contextuality and robustness integral in future NLP applications.']
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.