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الأفوكادو: استراتيجية تكييف المفردات إلى المجال المصب
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خلال مرحلة التوصيل الدقيقة للتعلم، لا يزال المفردات المسبدة مسبقا دون تغيير، في حين يتم تحديث المعلمات النموذجية.المفردات الناتجة بناء على البيانات المحددة مسبقا هي فرعية نفسية للبيانات المصب عند وجود تناقض المجال.نقترح النظر في المفردات كمعلمة قابلة للتحسين، مما يسمح لنا بتحديث المفردات من خلال توسيعها مع المفردات المحددة للمجال بناء على إحصاء التكتيف.علاوة على ذلك، نحافظ على تضيير الكلمات المضافة من التجاوز إلى البيانات المصب عن طريق الاستفادة من المعرفة المستفادة من نموذج لغة مسبق مع مصطلح التنظيم.حققت طريقتنا تحسينات أداء ثابتة حول مجالات متنوعة (أي، العلوم الطبية الحيوية، علوم الكمبيوتر، الأخبار، والمراجعات).
During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists. We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain specific vocabulary based on a tokenization statistic. Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term. Our method achieved consistent performance improvements on diverse domains (i.e., biomedical, computer science, news, and reviews).
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Since the seminal work of Richard Montague in the 1970s, mathematical and logic tools have successfully been used to model several aspects of the meaning of natural language. However, visually impaired people continue to face serious difficulties in
getting full access to this important instrument. Our paper aims to present a work in progress whose main goal is to provide blind students and researchers with an adequate method to deal with the different resources that are used in formal semantics. In particular, we intend to adapt the Portuguese Braille system in order to accommodate the most common symbols and formulas used in this kind of approach and to develop pedagogical procedures to facilitate its learnability. By making this formalization compatible with the Braille coding (either traditional and electronic), we hope to help blind people to learn and use this notation, essential to acquire a better understanding of a great number of semantic properties displayed by natural language.
This research was aimed to study a gross chemical composition of Avocado fruits
and seeds, and to known physicochemical characteristics of its oil, and its fatly acid
composition.
The data indicated that its fruits contained (12.4%) oil , while se
eds contained lower
amounts of oil which reached (1.92%) dry weight basis , and the moisture content of seeds
reached (%52.5) , and acid value reached ( 1.6and4.3 mg KOH/g) at fruit and seed oils
respectively , Avocado fruits oil contained (77.77%) unsaturated fatly acid , and (21.77%)
saturated , and iodine value obtained in our study ( 77-84 g I/100 g) of fruits oil and
( 45.78 g I/100 g) of seeds oil confirming the unsaturated nature of these oils.
Also Avocado fruits and seeds oil possessed a low peroxide value comparison others
(3.71 , 2.2 Meq/kg oil) respectively ,
The data also indicated that its seeds contained (52.5%) moisture , (7.45%) grude
fibers ,(8.53%) sugars , (4.69%) pure protein , (2.34%) ash , and high percentage of starch
(59.3%) dry weight basis .
The aim of vocabulary inventory prediction is to predict a learner's whole vocabulary based on a limited sample of query words. This paper approaches the problem starting from the 2-parameter Item Response Theory (IRT) model, giving each word in the
vocabulary a difficulty and discrimination parameter. The discrimination parameter is evaluated on the sub-problem of question item selection, familiar from the fields of Computerised Adaptive Testing (CAT) and active learning. Next, the effect of the discrimination parameter on prediction performance is examined, both in a binary classification setting, and in an information retrieval setting. Performance is compared with baselines based on word frequency. A number of different generalisation scenarios are examined, including generalising word difficulty and discrimination using word embeddings with a predictor network and testing on out-of-dataset data.
Adding the sifting meals of the Avocado seed to wheat flour mixtures caused
increasing in folowing percentages of components: (moisture , fibers , ash , starch and total
soluble sugars )
Also the (5,10%) percentages adding caused a slight reducing
in gluten quality ,
while a reducing in gluten quality was clearly at (15 , 20%) percentages.
Also the adding of tested meals caused improvement in baking quality (loaf weight
during baking and during cooling) , also improvement the sensory properties of produced
bread significantly , except (20%) percentage comparing with control sample.
Finally , 15 and 20 percentages , only of Avocado seed meals adding caused
reducing in taste property of produced bread.
We introduce BERTweetFR, the first large-scale pre-trained language model for French tweets. Our model is initialised using a general-domain French language model CamemBERT which follows the base architecture of BERT. Experiments show that BERTweetFR
outperforms all previous general-domain French language models on two downstream Twitter NLP tasks of offensiveness identification and named entity recognition. The dataset used in the offensiveness detection task is first created and annotated by our team, filling in the gap of such analytic datasets in French. We make our model publicly available in the transformers library with the aim of promoting future research in analytic tasks for French tweets.
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