Comments (5)
Regarding fastText, see piskvorky/gensim-data#26 (comment).
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@marco-c I just tried using 2-gram features for the TfIdfVectorizer
, here is the gist of the results:
Before:
X: (11007, 21990), y: (11007,)
Cross Validation scores:
Accuracy: f0.951746383784194 (+/- 0.0027559934918468167)
Precision: f0.9821515475460835 (+/- 0.0025632765014633823)
Recall: f0.9621308627678055 (+/- 0.002984157203642199)
After:
X: (11007, 95124), y: (11007,)
Cross Validation scores:
Accuracy: f0.9525540057386749 (+/- 0.004604483751288196)
Precision: f0.9830817914064187 (+/- 0.0035752238919424186)
Recall: f0.9621308627678055 (+/- 0.0035581451061348556)
Unfortunately the terminal crashes with terminate called after throwing an instance of 'std::bad_alloc'
in the second case.
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Results for which model?
To avoid OOMs, you can try to reduce the number of ngrams considered.
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These are the results for the bug model. Should I try it out for other models too? To generalize, won't it be better to have a feature of passing the value of n
from the terminal? (something like python run.py --train --model=bug --ngrams=2
)
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These are the results for the bug model. Should I try it out for other models too?
Yes, let's try the tracking, regression and component models too.
To generalize, won't it be better to have a feature of passing the value of n from the terminal? (something like python run.py --train --model=bug --ngrams=2)
Yes, it would be nice, but not so easy.
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Related Issues (20)
- Refactor the logic in the `bug_to_types` function and extract it from the `bugtype` model file
- [model:performance] Experiment with including bugs with their performance impact is `none` in the training set only
- Integrate with Weights & Biases
- Create a pipeline to retrieve crash reports from BigQuery and make them available to the model training tasks
- Create a model to predict actionable crashes
- [model:stepstoreproduce] Training task exit with status code 137
- [model:regressor] AttributeError: 'IsotonicRegressionCalibrator' object has no attribute 'n_features_in_' HOT 2
- Refactor the way that we apply `CalibratedClassifierCV` to the models
- [model:performance] Experiment with including bugs from performance components in the training set only
- Add more detailed guidance and recommendations to CONTRIBUTING.md
- Remove duplicate imports for `dateutil.parser`
- [model:bugtype] The `Keywords` feature accepts keywords but is given keyword prefixes
- [model:performance] Add the model to `http_service` and `data_pipeline`
- [model:accessibility] Add the model to `http_service` and `data_pipeline`
- UnboundLocalError: local variable 'median_fix_time_text' referenced before assignment when generating quality report
- [model:bugtype] ValueError: y should be a 1d array, got an array of shape (198635, 5) instead.
- [model:bugtype] ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() HOT 1
- Show the number of not classified instances for multi-label models HOT 1
- Include certain comment fields which will help the model to tag spam comments
- OOM Error when running data pipeline tasks to train `accessibility` and `performance` bug models
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