metadata
base_model: google/t5-v1_1-base
tags:
- datadreamer
- datadreamer-0.1.0
- synthetic
- gpt-4
- gpt-4
- text2text-generation
widget:
- text: >-
An important paradigm of natural language processing consists of
large-scale pre-training on general domain data and adaptation to
particular tasks or domains. As we pre-train larger models, full
fine-tuning, which retrains all model parameters, becomes less feasible.
Using GPT-3 175B as an example -- deploying independent instances of
fine-tuned models, each with 175B parameters, is prohibitively expensive.
We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained
model weights and injects trainable rank decomposition matrices into each
layer of the Transformer architecture, greatly reducing the number of
trainable parameters for downstream tasks. Compared to GPT-3 175B
fine-tuned with Adam, LoRA can reduce the number of trainable parameters
by 10,000 times and the GPU memory requirement by 3 times. LoRA performs
on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa,
GPT-2, and GPT-3, despite having fewer trainable parameters, a higher
training throughput, and, unlike adapters, no additional inference
latency. We also provide an empirical investigation into rank-deficiency
in language model adaptation, which sheds light on the efficacy of LoRA.
We release a package that facilitates the integration of LoRA with PyTorch
models and provide our implementations and model checkpoints for RoBERTa,
DeBERTa, and GPT-2 at this https URL.
example_title: LoRA Abstract
- text: >-
In this research paper, we propose a novel approach to Natural Language
Processing (NLP) that addresses several limitations of existing methods.
By integrating deep learning architectures with traditional NLP
techniques, we have developed a model that shows significant improvements
in performance across several NLP tasks including sentiment analysis, text
summarization, and machine translation. We treat language processing not
as a linear task but rather an interconnected web of sub-tasks, each
benefiting from mutual feedback. The conceptual breakthrough of this
approach is the shared representation of linguistic features across these
sub-tasks that allow for robust understanding and language inference. We
demonstrated the effectiveness of our model in extensive empirical
evaluations on several benchmark datasets, where our method consistently
outperforms state-of-the-art solutions. We also discuss the theoretical
justification of our model. Overall, this paper extends the frontiers of
NLP by broadening the commonly used methods and setting BPM (Benchmarks
Per Minute) records in five major tasks. We hope this work encourages
future researchers to adopt an integrated perspective when building NLP
models.
example_title: Example 2
- text: >-
In recent years, we have seen a significative progression in Natural
Language Processing (NLP) capabilities, primarily driven by advancements
in deep learning. However, creating accurate models capable of
understanding context, tone, and semantic meanings remains a significant
challenge. Several models struggle to maintain stable performance when
presented with different kinds of texts. In this paper, we address the
problem of language-context detection in diversely written text. We
introduce new approaches utilising transformer-based models combined with
Domain-Adaptive Fine Tuning, a technique that allows capturing various
linguistic details for enhanced comprehension of text. Extensive
experiments on several datasets reveal that it is not just the large
scales of these models that matter, but a proper, task-specific tuning,
can significantly bring reductions in model complexity, resource demands,
and increase the prediction performance, challenging the commonly held
belief in "bigger is better". We further suggest that our innovations will
directly lead to significant improvements in performance and the wide
adoption of the NLP models within real-world scenarios. AI model's ability
to scale will see a vital performance curve particularly under low-data
regime conditions which are prevalent in the commercial sector.
example_title: Example 3
pipeline_tag: text2text-generation
datasets:
- datadreamer-dev/abstracts_and_tweets
Model Card
This is an "Abstract to Tweet" model that crafts a tweet summarizing a research paper abstract trained on a synthetic dataset of arXiv abstracts and tweets. It is used as a demonstration of the DataDreamer 🤖💤 library.
Example Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained('datadreamer-dev/abstracts_to_tweet_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('datadreamer-dev/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 delve into advanced techniques and methods in Natural Language Processing (NLP), innovatively incorporating Transformer architectures and self-supervised learning methods. We aim to reiterate the current understanding of Transformer-based models in executing various language tasks by dissecting their versatility and expandability on broad language systems.\n\nMoreover, stabilization measures, tokenization assortment, and interpreting latent spaces provide an in-depth novelty to our pipeline, overcoming long-known obstacles. We explore meta-architectural modifications focusing on enhancing prompt language models' efficiency, allowing flexible adaptations to the core Transformer technique's abundance in BERT, GPT-like systems.\n\nTo implement these adaptations, several experiments were conducted on varied benchmark datasets to evaluate core metrics such as Bleu, Rouge, and Warp-CTC metrics in translation and transcription tasks. We carried out significant analysis focusing on module interpretability, additional error inspection, task-specific regulatory mechanisms, execution speed, and computational considerations.\n\nOur experimental results bring in distraction from widespread but sub-optimal benchmarks and offer evidence underpinning the contrary yet potent issues yet to be addressed methodically. We invite the community to reflect on these novel insights, develop and refine our proposed techniques, speeding technical progress, avoiding prototypical retrodiction in the Natural Language Understanding ecosystem to respect inclusive, diverse, and correctly perceived expressive content."]
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.