Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Package for Learning on Tabular and Text Data with Transformers
حزمة للتعلم على البيانات الجداول والنصوص مع المحولات
355
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
أدت التقدم المحرز الأخير في معالجة اللغات الطبيعية إلى أن تصبح هياكل المحولات النموذجية السائدة المستخدمة لمهام اللغة الطبيعية.ومع ذلك، في العديد من مجموعات البيانات في العالم، يتم تضمين طرائق إضافية التي لا يستوفي المحول مباشرة.نقدم مجموعة أدوات متعددة الوسائط، حزمة بيثون مفتوحة المصدر لتضمين بيانات النص والمجدول (القاطع والرقمي) مع المحولات لتطبيقات المصب.تدمج مجموعة أدواتنا جيدا مع تعانق واجهة برمجة التطبيقات الموجودة في وجه المعانقة مثل التوت والمركز النموذجي الذي يتيح تنزيل سهلة من مختلف النماذج المدربة مسبقا.
Recent progress in natural language processing has led to Transformer architectures becoming the predominant model used for natural language tasks. However, in many real- world datasets, additional modalities are included which the Transformer does not directly leverage. We present Multimodal- Toolkit, an open-source Python package to incorporate text and tabular (categorical and numerical) data with Transformers for downstream applications. Our toolkit integrates well with Hugging Face's existing API such as tokenization and the model hub which allows easy download of different pre-trained models.
References used
https://aclanthology.org/
rate research
Read More
QuranTree.jl is an open-source package for working with the Quranic Arabic Corpus (Dukes and Habash, 2010). It aims to provide Julia APIs as an alternative to the Java APIs of JQuranTree. QuranTree.jl currently offers functionalities for intuitive in
dexing of chapters, verses, words and parts of words of the Qur'an; for creating custom transliteration; for character dediacritization and normalization; and, for handling the morphological features. Lastly, it can work well with Julia's TextAnalysis.jl and Python's CAMeL Tools.
Offensive language detection (OLD) has received increasing attention due to its societal impact. Recent work shows that bidirectional transformer based methods obtain impressive performance on OLD. However, such methods usually rely on large-scale we
ll-labeled OLD datasets for model training. To address the issue of data/label scarcity in OLD, in this paper, we propose a simple yet effective domain adaptation approach to train bidirectional transformers. Our approach introduces domain adaptation (DA) training procedures to ALBERT, such that it can effectively exploit auxiliary data from source domains to improve the OLD performance in a target domain. Experimental results on benchmark datasets show that our approach, ALBERT (DA), obtains the state-of-the-art performance in most cases. Particularly, our approach significantly benefits underrepresented and under-performing classes, with a significant improvement over ALBERT.
In recent years, time-critical processing or real-time processing and analytics of bid data have received a significant amount of attentions. There are many areas/domains where real-time processing of data and making timely decision can save thousand
s of human lives, minimizing the risks of human lives and resources, enhance the quality of human lives, enhance the chance of profitability, efficient resources management etc. This paper has presented such type of real-time big data analytic applications and a classification of those applications. In addition, it presents the time requirements of each type of these applications along with its significant benefits. Also, a general overview of big data to describe a background knowledge on this scope.
Abstract Recently, multimodal transformer models have gained popularity because their performance on downstream tasks suggests they learn rich visual-linguistic representations. Focusing on zero-shot image retrieval tasks, we study three important fa
ctors that can impact the quality of learned representations: pretraining data, the attention mechanism, and loss functions. By pretraining models on six datasets, we observe that dataset noise and language similarity to our downstream task are important indicators of model performance. Through architectural analysis, we learn that models with a multimodal attention mechanism can outperform deeper models with modality-specific attention mechanisms. Finally, we show that successful contrastive losses used in the self-supervised learning literature do not yield similar performance gains when used in multimodal transformers.
Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augme
nting generative Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialog modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge required for knowledgeable but engaging dialog from Wikipedia, images, and human-written dialog utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
