The training accuracy of a classification model is much less important than how well that model works when it's given new, unseen data. After all, you train models so that they can be used on new data that you find in the real world. So, after you've trained a classification ...
I have never touched image classification before in any class or personal project, so I had to start from scratch in terms of learning how this is done. Additionally, this is my first time using Keras pre-trained models for transfer learning, and I am incredibly impressed at how simple the...
Users can specify different DCM subtypes, define prior distributions, and estimate the model using the rstan or cmdstanr interface to Stan. You can then easily examine model parameters, calculate model fit metrics, compare competing models, and evaluate the reliability of the attributes. Installation...
Recently, large scale pre-trained language models such as BERT and models with lattice structure that consisting of character-level and word-level information have achieved state-of-the-art performance in most downstream natural language processing (NLP)
In our previous work, by combining BERT with other models, a feature-enhanced Chinese short text classification model was proposed based on a non-equilibrium bidirectional Long Short-Term Memory network2. However, the pre-training model has limitations in terms of the length of the input sample....
This way, it can be difficult to find resources for other languages, such as pre-trained language models in different languages, corpora, etc. Related to this, is the few amounts of works exploring multi-lingual classification (around 1%). Moreover, the oriental languages are very different ...
Unlike traditional deep learning models that improve classification accuracy by increasing depth, the proposed model in this paper not only improves classification accuracy, but also achieves the light weight of the model. The work in this paper is organized as follows: “Multi-modal PQD classificatio...
Required Arguments for Evaluation# The following arguments are required for evaluation: pretrained_model: pretrained Token Classification model fromlist_available_models()or path to a.nemofile. For example,ner_en_bertoryour_model.nemo model.dataset.data_dir: path to the directory that containesmodel...
models.py ├── README.md ├── requirements.txt ├── saved │ ├── diff │ ├── log │ └── models ├── text_classification ├── trainer │ ├── cnews_trainer.py │ ├── __init__.py │ ├── medical_question_trainer.py │ └── weibo_trainer.py ├── ...
CNN models take much less time to train than feed forward networks. The accuracy of the CNN models is not as good as the feed forward networks. Regularization by adding dropout does not always prevent overfitting.The Segmentation ModelRegression model...