seqeval
Metrics
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| seqeval | Metrics | |
|---|---|---|
| 1 | 2 | |
| 1,045 | 1,617 | |
| 1.4% | - | |
| 0.0 | 0.0 | |
| 3 days ago | over 1 year ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
seqeval
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Beginner questions about NER model evaluation.
. The standard way to evaluate NER (or any other sequence labelling problem) is to use the conlleval script (https://www.clips.uantwerpen.be/conll2000/chunking/output.html) or through the seqeval package in python (https://github.com/chakki-works/seqeval) . Either way, you need a list of predicted labels and a list of gold labels (see the code example in the link, it should be trivial to converse your output to the same data format).
Metrics
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Model evaluation - MAP@K
Starting with Python we’re going to code the functions from scratch using the values determined from the linear regression model. First we’re going to write a function to calculate the Average Precision at K. It will take in three values, the value from the test set, and value from the model prediction, and finally the value for K. This code can be found in the Github for the ml_metrics Python Library.
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How to Judge your Recommendation System Model ?
These metrics are straightforward to implement, also can be obtained from here. Happy Learning !
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