README.md

---
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](https://huggingface.co/datasets/datadreamer-dev/abstracts_and_tweets/). It is used as a demonstration of the [DataDreamer 🤖💤 library](https://datadreamer.dev/docs/latest/).

## Example Usage

```python3
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 🤖💤](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).