metadata
base_model: google/t5-v1_1-base
tags:
- datadreamer
- datadreamer-0.46.0
- synthetic
- mistral-tiny
- mistral-tiny
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: >-
In this paper, we introduce a novel learning framework for addressing
inconsistencies and incompleteness inEffects of Pre-training Task
Structure on Cross-lingual Transferof large-scale, multilingual machine
reading comprehension (MRC) models. Our proposed method, termed
Structured-MRC, employs a new task structure that strategically balances
knowledge transfer and specialized information acquisition across
languages. Rather than using one universal pre-training task,
Structured-MRC synchronizes task-wise pre-training across related language
pairs. This technique allows our models to effectively learn and transfer
recurring patterns while avoiding overgeneralization. Comprehensive
experiments are carried out on eight diverse languages from the XNLI,
XNLG, MARC, and WikiMRC datasets, demonstrating that the Structured-MRC
framework significantly outperforms state-of-the-art approaches in terms
of consistency, comprehensibility, and generality. The insights gained
from this study highlight the importance of structuring learning tasks for
cross-lingual transfer in MRC, with implications for various NLP
applications.
example_title: Example 1
- text: >-
Title: Unsupervised Multilingual Contextual Word Embeddings: Incorporating
Morphological and Syntactic Features
In this paper, we propose an unsupervised approach for developing
multilingual contextual word embeddings that capture both morphological
and syntactic properties. The model framework, dubbed 'UniMorph Syn',
targets 100 low-resource languages while accounting for diverse inflection
patterns and structurally complex sentences. By employing morphological
analyzers, POS taggers, and dependency parsers as pre-processing steps, we
enhance the quality and comprehensiveness of contextual representations.
We analyze the model's performance through cross-linguistic and cross-task
evaluations using several datasets and benchmarks, obtaining promising
results and outperforming relevant baselines. Our work showcases the
potential of unsupervised, large-scale, both morphologically and
syntactically-aware models for low-resource languages within a
multilingual context.
example_title: Example 2
- text: >-
In this work, we present an innovative deep learning model for Natural
Language Processing (NLP) tasks that leverages the transformer
architecture supplemented with a robust pre-training strategy on vast
unstructured data. Our model, Scored-Efficient Transformer (SET), excels
in balancing efficiency with quality, achieving competitive and in some
cases better performance than existing models while being significantly
more computationally efficient.
SET's unique feature is the introduction of a novel dynamic attention
mechanism, which selectively accord focus to important contextual
features, steadfastly reducing computational requirements without
compromising the semantic understanding or performance. Furthermore, we
explore a novel pre-training schema, named PseudoCorpus, which entails the
creation of pseudo corpora from task-specific data, effectively tailoring
the model to cater to a diverse range of NLP tasks with minimal need for
task-specific fine-tuning.
Experimental evaluations on benchmark datasets including GLUE, SQuAD, and
STS-B demonstrate not only signs of consistent improvements in various NLP
tasks for SET, but also highlight its remarkable generalization ability
across various tasks. In summary, SET revolutionizes the NLP paradigm with
a critical balance of efficiency, productivity, and efficacy, pushing the
boundaries of potential applications in real-world scenarios.
example_title: Example 3
Model Card
Example Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained('andersoncliffb/abstracts_to_tweet_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('andersoncliffb/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 introduce a novel learning framework for addressing inconsistencies and incompleteness inEffects of Pre-training Task Structure on Cross-lingual Transferof large-scale, multilingual machine reading comprehension (MRC) models. Our proposed method, termed Structured-MRC, employs a new task structure that strategically balances knowledge transfer and specialized information acquisition across languages. Rather than using one universal pre-training task, Structured-MRC synchronizes task-wise pre-training across related language pairs. This technique allows our models to effectively learn and transfer recurring patterns while avoiding overgeneralization. Comprehensive experiments are carried out on eight diverse languages from the XNLI, XNLG, MARC, and WikiMRC datasets, demonstrating that the Structured-MRC framework significantly outperforms state-of-the-art approaches in terms of consistency, comprehensibility, and generality. The insights gained from this study highlight the importance of structuring learning tasks for cross-lingual transfer in MRC, with implications for various 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.