Sentence examples for use a model trained from inspiring English sources

If you are using the Neural Network dependency parser and want to get the original SD relations, see the CoreNLP FAQ on how to use a model trained on Stanford Dependencies.

If you are selling to customers that allow you to pool anonymized data or use a model trained on their data with other customers, the AI product will perform better "out of the box" with each subsequent customer.

Prediction accuracy was evaluated using a leave-one-out procedure which estimated the scores of each individual using a model trained with the rest of the data.

We conducted external predictions for the ligands of the PDB entries 3MVJ (L A ), 3DNE (L B ), and 3DND (L C ) using a model trained on MUV548.

We also show that our recogniser that uses a model trained on the CHEMDNER corpus is suitable for recognising names in a wide range of corpora, consistently outperforming two popular chemical NER tools.

In Section 3, we discuss MDT imputation under the framework of ML decoding and why it becomes difficult when using a model trained with decorrelated features such as cepstral features or features generated by, e.g., linear discriminant analysis (LDA) [29].

Weekly ILI values were estimated using a model trained on the roughly 1 million influenza-related tweets from the second data set (October 1, 2009 through May 20, 2010) that were unambiguously tagged with US locations, using CDC-reported ILI values across the entire United States as the objective.

The above classification analyses assessed the ability to construct and classify concepts using a model trained and tested on MEG responses averaged over participants.

In this work we have used our model to create two sets of stability values for the human proteome – one using a model trained on all our data (P1), and one using a model trained on data cleansed from secreted and membrane proteins (P2).

We directly assessed the predictive performance of the regression using leave-one-out (LOO) error estimation, predicting the PMI of each sample using a model trained on the remaining samples.

where is a model trained using both and.