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_testlr370.out
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_testlr370.out
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Interactive jobs timeout is 24hrs
==========================================
SLURM_JOB_ID = 370
SLURM_NODELIST = virya3
==========================================
Tue Feb 20 13:06:51 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.08 Driver Version: 545.23.08 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA A100-SXM4-40GB Off | 00000000:81:00.0 Off | On |
| N/A 27C P0 65W / 400W | 9357MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+--------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+================================+===========+=======================|
| 0 2 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+--------------------------------+-----------+-----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm1b_t33_650M_UR50S and are newly initialized: ['esm.pooler.dense.bias', 'esm.pooler.dense.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Model saved to final_lr_modelIT_MP.joblib
--- Train Dataset Evaluation ---
Classification Report:
precision recall f1-score support
0 1.00 1.00 1.00 8904
1 1.00 1.00 1.00 781
accuracy 1.00 9685
macro avg 1.00 1.00 1.00 9685
weighted avg 1.00 1.00 1.00 9685
Confusion Matrix:
[[8904 0]
[ 1 780]]
--- Test Dataset Evaluation ---
Classification Report:
precision recall f1-score support
0 0.99 0.99 0.99 2226
1 0.93 0.89 0.91 196
accuracy 0.99 2422
macro avg 0.96 0.94 0.95 2422
weighted avg 0.99 0.99 0.99 2422
Confusion Matrix:
[[2213 13]
[ 22 174]]
Model loaded successfully
Prediction: Non-ionic Membrane Protein, Probabilities: [9.99090898e-01 9.09101578e-04]