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Text Classification
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text-embeddings-inference
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End of training

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  1. README.md +63 -29
  2. runs/Dec24_16-50-51_86f59ffbd25a/events.out.tfevents.1703436653.86f59ffbd25a.1423.153 +2 -2
  3. runs/Dec24_16-50-51_86f59ffbd25a/events.out.tfevents.1703436653.86f59ffbd25a.1423.154 +2 -2
  4. runs/Dec24_16-50-51_86f59ffbd25a/events.out.tfevents.1703436720.86f59ffbd25a.1423.155 +3 -0
  5. runs/Dec24_16-50-51_86f59ffbd25a/events.out.tfevents.1703436720.86f59ffbd25a.1423.156 +3 -0
README.md CHANGED
@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Anwaarma/Merged-Server-praj](https://huggingface.co/Anwaarma/Merged-Server-praj) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7888
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- - Accuracy: 0.31
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- - F1: 0.4733
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  ## Model description
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@@ -38,7 +38,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -50,31 +50,65 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | No log | 0.0 | 50 | 0.5805 | 0.6 | 0.5992 |
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- | No log | 0.01 | 100 | 0.5687 | 0.65 | 0.6505 |
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- | No log | 0.01 | 150 | 0.5711 | 0.64 | 0.6404 |
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- | No log | 0.01 | 200 | 0.5643 | 0.67 | 0.6678 |
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- | No log | 0.02 | 250 | 0.5757 | 0.64 | 0.6393 |
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- | No log | 0.02 | 300 | 0.5855 | 0.66 | 0.6517 |
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- | No log | 0.02 | 350 | 0.5529 | 0.63 | 0.6260 |
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- | No log | 0.03 | 400 | 0.5646 | 0.62 | 0.6105 |
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- | No log | 0.03 | 450 | 0.5814 | 0.61 | 0.6087 |
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- | 0.5986 | 0.03 | 500 | 0.5702 | 0.62 | 0.6129 |
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- | 0.5986 | 0.04 | 550 | 0.5618 | 0.62 | 0.6205 |
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- | 0.5986 | 0.04 | 600 | 0.5517 | 0.63 | 0.6306 |
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- | 0.5986 | 0.04 | 650 | 0.5522 | 0.62 | 0.6192 |
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- | 0.5986 | 0.05 | 700 | 0.5524 | 0.63 | 0.6288 |
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- | 0.5986 | 0.05 | 750 | 0.5619 | 0.64 | 0.6404 |
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- | 0.5986 | 0.05 | 800 | 0.5638 | 0.64 | 0.64 |
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- | 0.5986 | 0.06 | 850 | 0.5481 | 0.66 | 0.6605 |
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- | 0.5986 | 0.06 | 900 | 0.5562 | 0.64 | 0.64 |
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- | 0.5986 | 0.06 | 950 | 0.5502 | 0.64 | 0.6354 |
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- | 0.5834 | 0.07 | 1000 | 0.5970 | 0.68 | 0.6606 |
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- | 0.5834 | 0.07 | 1050 | 0.5369 | 0.67 | 0.6702 |
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- | 0.5834 | 0.07 | 1100 | 0.5970 | 0.6 | 0.5966 |
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- | 0.5834 | 0.08 | 1150 | 0.5770 | 0.62 | 0.6192 |
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- | 0.5834 | 0.08 | 1200 | 0.5582 | 0.63 | 0.6306 |
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- | 0.5834 | 0.09 | 1250 | 0.5505 | 0.66 | 0.66 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [Anwaarma/Merged-Server-praj](https://huggingface.co/Anwaarma/Merged-Server-praj) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
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+ - Loss: 0.5643
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+ - Accuracy: 0.82
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+ - F1: 0.9011
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 4e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 0.0 | 50 | 0.5790 | 0.6 | 0.5992 |
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+ | No log | 0.01 | 100 | 0.5691 | 0.65 | 0.6505 |
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+ | No log | 0.01 | 150 | 0.5678 | 0.65 | 0.6505 |
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+ | No log | 0.01 | 200 | 0.5621 | 0.68 | 0.6773 |
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+ | No log | 0.02 | 250 | 0.5666 | 0.63 | 0.6303 |
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+ | No log | 0.02 | 300 | 0.5721 | 0.65 | 0.6463 |
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+ | No log | 0.02 | 350 | 0.5533 | 0.63 | 0.6260 |
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+ | No log | 0.03 | 400 | 0.5614 | 0.62 | 0.6105 |
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+ | No log | 0.03 | 450 | 0.5756 | 0.62 | 0.6181 |
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+ | 0.5985 | 0.03 | 500 | 0.5666 | 0.6 | 0.5947 |
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+ | 0.5985 | 0.04 | 550 | 0.5613 | 0.64 | 0.6406 |
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+ | 0.5985 | 0.04 | 600 | 0.5541 | 0.63 | 0.6306 |
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+ | 0.5985 | 0.04 | 650 | 0.5571 | 0.62 | 0.6192 |
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+ | 0.5985 | 0.05 | 700 | 0.5536 | 0.62 | 0.6192 |
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+ | 0.5985 | 0.05 | 750 | 0.5614 | 0.63 | 0.6306 |
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+ | 0.5985 | 0.05 | 800 | 0.5667 | 0.63 | 0.6297 |
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+ | 0.5985 | 0.06 | 850 | 0.5466 | 0.66 | 0.6600 |
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+ | 0.5985 | 0.06 | 900 | 0.5532 | 0.66 | 0.6593 |
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+ | 0.5985 | 0.06 | 950 | 0.5482 | 0.67 | 0.6630 |
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+ | 0.5855 | 0.07 | 1000 | 0.5837 | 0.63 | 0.6220 |
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+ | 0.5855 | 0.07 | 1050 | 0.5368 | 0.67 | 0.6705 |
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+ | 0.5855 | 0.07 | 1100 | 0.5793 | 0.62 | 0.6167 |
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+ | 0.5855 | 0.08 | 1150 | 0.5694 | 0.63 | 0.6276 |
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+ | 0.5855 | 0.08 | 1200 | 0.5520 | 0.63 | 0.6306 |
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+ | 0.5855 | 0.09 | 1250 | 0.5572 | 0.66 | 0.6593 |
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+ | 0.5855 | 0.09 | 1300 | 0.5706 | 0.62 | 0.6150 |
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+ | 0.5855 | 0.09 | 1350 | 0.5694 | 0.66 | 0.6593 |
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+ | 0.5855 | 0.1 | 1400 | 0.5559 | 0.65 | 0.6497 |
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+ | 0.5855 | 0.1 | 1450 | 0.5515 | 0.67 | 0.6705 |
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+ | 0.5777 | 0.1 | 1500 | 0.5447 | 0.64 | 0.6393 |
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+ | 0.5777 | 0.11 | 1550 | 0.5453 | 0.65 | 0.6502 |
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+ | 0.5777 | 0.11 | 1600 | 0.5575 | 0.64 | 0.6400 |
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+ | 0.5777 | 0.11 | 1650 | 0.5498 | 0.66 | 0.6584 |
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+ | 0.5777 | 0.12 | 1700 | 0.5620 | 0.66 | 0.6604 |
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+ | 0.5777 | 0.12 | 1750 | 0.5734 | 0.67 | 0.6702 |
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+ | 0.5777 | 0.12 | 1800 | 0.5561 | 0.66 | 0.6593 |
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+ | 0.5777 | 0.13 | 1850 | 0.5376 | 0.67 | 0.6649 |
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+ | 0.5777 | 0.13 | 1900 | 0.5652 | 0.65 | 0.6505 |
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+ | 0.5777 | 0.13 | 1950 | 0.5414 | 0.67 | 0.6689 |
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+ | 0.575 | 0.14 | 2000 | 0.5340 | 0.67 | 0.6665 |
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+ | 0.575 | 0.14 | 2050 | 0.5393 | 0.68 | 0.6794 |
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+ | 0.575 | 0.14 | 2100 | 0.5253 | 0.7 | 0.6994 |
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+ | 0.575 | 0.15 | 2150 | 0.5334 | 0.69 | 0.6834 |
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+ | 0.575 | 0.15 | 2200 | 0.5395 | 0.68 | 0.6773 |
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+ | 0.575 | 0.15 | 2250 | 0.5426 | 0.65 | 0.6446 |
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+ | 0.575 | 0.16 | 2300 | 0.5523 | 0.64 | 0.6370 |
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+ | 0.575 | 0.16 | 2350 | 0.5378 | 0.68 | 0.6804 |
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+ | 0.575 | 0.16 | 2400 | 0.5375 | 0.67 | 0.6649 |
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+ | 0.575 | 0.17 | 2450 | 0.5378 | 0.68 | 0.6742 |
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+ | 0.556 | 0.17 | 2500 | 0.5491 | 0.69 | 0.6867 |
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+ | 0.556 | 0.17 | 2550 | 0.5347 | 0.66 | 0.6517 |
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+ | 0.556 | 0.18 | 2600 | 0.5325 | 0.69 | 0.6852 |
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+ | 0.556 | 0.18 | 2650 | 0.5490 | 0.68 | 0.6794 |
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+ | 0.556 | 0.18 | 2700 | 0.5313 | 0.7 | 0.7005 |
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+ | 0.556 | 0.19 | 2750 | 0.5451 | 0.65 | 0.6314 |
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+ | 0.556 | 0.19 | 2800 | 0.5506 | 0.64 | 0.6312 |
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+ | 0.556 | 0.19 | 2850 | 0.5539 | 0.65 | 0.6497 |
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+ | 0.556 | 0.2 | 2900 | 0.5601 | 0.66 | 0.6604 |
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+ | 0.556 | 0.2 | 2950 | 0.5530 | 0.67 | 0.6705 |
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  ### Framework versions
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