Upload folder using huggingface_hub
Browse files- README.md +152 -0
- config.json +52 -0
- confusion_matrix.png +0 -0
- febskxmodel_hug_0.pkl +3 -0
README.md
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---
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library_name: sklearn
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license: mit
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tags:
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- sklearn
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- skops
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- tabular-classification
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model_format: pickle
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model_file: febskxmodel_hug_0.pkl
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widget:
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- structuredData:
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backlog_minutes:
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- 793051
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- 474385
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- 785116
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backlog_num_jobs:
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- 302
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- 193
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- 302
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max_minutes:
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- 18
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- 360
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- 18
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nnodes:
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- 1
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- 1
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- 1
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running_minutes:
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- 1934034
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- 1934094
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- 1934034
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running_num_jobs:
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- 6827
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- 6828
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- 6827
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---
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# Model description
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[More Information Needed]
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## Intended uses & limitations
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[More Information Needed]
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## Training Procedure
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[More Information Needed]
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### Hyperparameters
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<details>
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<summary> Click to expand </summary>
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| Hyperparameter | Value |
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|----------------------------|----------------------------------------------------------------------------------------------------|
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| memory | |
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| steps | [('scale', StandardScaler()), ('hgbc', HistGradientBoostingClassifier(max_depth=9, max_iter=600))] |
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| verbose | False |
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| scale | StandardScaler() |
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| hgbc | HistGradientBoostingClassifier(max_depth=9, max_iter=600) |
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| scale__copy | True |
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| scale__with_mean | True |
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| scale__with_std | True |
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| hgbc__categorical_features | |
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| hgbc__class_weight | |
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| hgbc__early_stopping | auto |
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| hgbc__interaction_cst | |
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| hgbc__l2_regularization | 0.0 |
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| hgbc__learning_rate | 0.1 |
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| hgbc__loss | log_loss |
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| hgbc__max_bins | 255 |
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| hgbc__max_depth | 9 |
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| hgbc__max_iter | 600 |
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| hgbc__max_leaf_nodes | 31 |
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| hgbc__min_samples_leaf | 20 |
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| hgbc__monotonic_cst | |
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| hgbc__n_iter_no_change | 10 |
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| hgbc__random_state | |
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| hgbc__scoring | loss |
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| hgbc__tol | 1e-07 |
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| hgbc__validation_fraction | 0.1 |
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| hgbc__verbose | 0 |
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| hgbc__warm_start | False |
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</details>
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### Model Plot
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<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('scale', StandardScaler()),('hgbc',HistGradientBoostingClassifier(max_depth=9, max_iter=600))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('scale', StandardScaler()),('hgbc',HistGradientBoostingClassifier(max_depth=9, max_iter=600))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier(max_depth=9, max_iter=600)</pre></div></div></div></div></div></div></div>
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## Evaluation Results
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| Metric | Value |
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|-----------------------|-------------------|
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| accuracy | 0.946168166304685 |
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| classification report | precision recall f1-score support<br /><br /> 0 0.97 0.98 0.98 5075<br /> 1 0.74 0.57 0.64 218<br /> 2 0.70 0.59 0.64 108<br /> 3 0.67 0.55 0.60 86<br /> 4 0.89 0.92 0.90 959<br /><br /> accuracy 0.95 6446<br /> macro avg 0.79 0.72 0.75 6446<br />weighted avg 0.94 0.95 0.94 6446 |
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# How to Get Started with the Model
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[More Information Needed]
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# Model Card Authors
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This model card is written by following authors:
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[More Information Needed]
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# Model Card Contact
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You can contact the model card authors through following channels:
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[More Information Needed]
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# Citation
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Below you can find information related to citation.
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**BibTeX:**
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```
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[More Information Needed]
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```
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# citation_bibtex
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bibtex
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@inproceedings{...,year={2024}}
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# get_started_code
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import pickle
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with open(dtc_pkl_filename, 'rb') as file:
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clf = pickle.load(file)
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# model_card_authors
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Smruti Padhy
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# limitations
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This model is ready to be used in production.
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# model_description
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This is a Histogram-based Gradient Boosting Classification Tree model trained on HPC history jobs between 1Feb-1Aug 2022, window number0
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# eval_method
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The model is evaluated using test split, on accuracy and F1 score with macro average.
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# confusion_matrix
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config.json
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{
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"sklearn": {
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"columns": [
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"nnodes",
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"max_minutes",
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"backlog_minutes",
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"backlog_num_jobs",
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"running_num_jobs",
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"running_minutes"
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],
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"environment": [
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"scikit-learn=1.2.2"
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],
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"example_input": {
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"backlog_minutes": [
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793051,
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474385,
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785116
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],
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"backlog_num_jobs": [
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302,
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193,
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302
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],
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"max_minutes": [
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18,
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360,
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18
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],
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"nnodes": [
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1,
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1,
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1
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],
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"running_minutes": [
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1934034,
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1934094,
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1934034
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],
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"running_num_jobs": [
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6827,
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6828,
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6827
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]
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},
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"model": {
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"file": "febskxmodel_hug_0.pkl"
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},
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"model_format": "pickle",
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"task": "tabular-classification"
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}
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}
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confusion_matrix.png
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febskxmodel_hug_0.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:96bdbfc1ea264c191432cb89d3e3065dfaf8968a8122991db333297346ed9185
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size 2668195
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