NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
Model trained for Czech POS Tagging and Lemmatization using Czech version of BERT model, RobeCzech. Model is trained on data from Prague Dependency Treebank 3.5. Model is a part of Czech NLP with Contextualized Embeddings master thesis and presented a state-of-the-art performance on the date of submission of the work.
Demo jupyter notebook is available on the project GitHub.
Experimental materials, data and R scripts used in the paper "Garden-path sentences and the diversity of their
(mis)representations" (Ceháková - Chromý, 2023).
RobeCzech is a monolingual RoBERTa language representation model trained on Czech data. RoBERTa is a robustly optimized Transformer-based pretraining approach. We show that RobeCzech considerably outperforms equally-sized multilingual and Czech-trained contextualized language representation models, surpasses current state of the art in all five evaluated NLP tasks and reaches state-of-theart results in four of them. The RobeCzech model is released publicly at https://hdl.handle.net/11234/1-3691 and https://huggingface.co/ufal/robeczech-base, both for PyTorch and TensorFlow.
Supplementary materials for the paper “Processing of explicit and implicit contrastive and temporal discourse relations in Czech” (submitted to Discourse Processes)
Sentiment analysis models for Czech language. Models are three Czech sentiment analysis datasets(http://liks.fav.zcu.cz/sentiment/): Mall, CSFD, Facebook, and joint data from all three datasets above, using Czech version of BERT model, RobeCzech.
We present the best model for every dataset. Mall and CSFD models are new state-of-the-art for respective data.
Demo jupyter notebook is available on the project GitHub.
These models are a part of Czech NLP with Contextualized Embeddings master thesis.
The presented data and metadata include answers to questions raised in the questionnaire focused on the experience of teaching practicums and their role in the practical preparation of English language teachers at the Faculty of Arts, Charles University, as well as a basic quantitative analysis of the answers.
The analysis of the questionnaires shows that trainees are, in most cases, prepared for their teaching practicum both professionally and in terms of pedagogy and psychology, and the use of reflective teaching methods seems very useful. The benefits of the teaching practicum include, in particular, getting to know the real situation of teaching in secondary schools and working with a larger group of pupils, getting to know oneself as a teacher, gaining self-confidence, and becoming aware of one's own limits and areas for improvement. The downsides of the current system of teaching practice include mainly the low time allocation, the lack of integration of the practice in the curriculum, and the lack of involvement of the trainee in the daily running of the school (administrative work, supervision, meetings) and the lack of quality feedback from the faculty teacher.