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bert-LoRA-reminder

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  1. README.md +33 -33
  2. adapter_model.safetensors +1 -1
  3. runs/Apr26_12-34-07_fc5a5e47cb22/events.out.tfevents.1714134848.fc5a5e47cb22.18181.0 +2 -2
README.md CHANGED
@@ -3,7 +3,7 @@ license: mit
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  library_name: peft
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  tags:
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  - generated_from_trainer
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- base_model: dbmdz/bert-base-italian-cased
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  metrics:
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  - accuracy
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  model-index:
@@ -16,9 +16,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # bert-LoRA-reminder
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- This model is a fine-tuned version of [dbmdz/bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5063
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  - Accuracy: 0.9545
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  ## Model description
@@ -50,36 +50,36 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.7021 | 1.0 | 22 | 0.7031 | 0.5682 |
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- | 0.678 | 2.0 | 44 | 0.6824 | 0.5909 |
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- | 0.6913 | 3.0 | 66 | 0.6752 | 0.6364 |
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- | 0.6676 | 4.0 | 88 | 0.6663 | 0.7273 |
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- | 0.6871 | 5.0 | 110 | 0.6597 | 0.7273 |
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- | 0.6645 | 6.0 | 132 | 0.6512 | 0.7727 |
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- | 0.6521 | 7.0 | 154 | 0.6464 | 0.7955 |
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- | 0.6508 | 8.0 | 176 | 0.6402 | 0.7955 |
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- | 0.6516 | 9.0 | 198 | 0.6342 | 0.7955 |
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- | 0.6702 | 10.0 | 220 | 0.6244 | 0.7727 |
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- | 0.6328 | 11.0 | 242 | 0.6190 | 0.7955 |
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- | 0.6285 | 12.0 | 264 | 0.6126 | 0.7955 |
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- | 0.6257 | 13.0 | 286 | 0.6048 | 0.8182 |
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- | 0.6451 | 14.0 | 308 | 0.5966 | 0.8182 |
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- | 0.6313 | 15.0 | 330 | 0.5890 | 0.8182 |
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- | 0.6088 | 16.0 | 352 | 0.5813 | 0.8864 |
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- | 0.6063 | 17.0 | 374 | 0.5730 | 0.8864 |
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- | 0.6089 | 18.0 | 396 | 0.5646 | 0.8864 |
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- | 0.5829 | 19.0 | 418 | 0.5544 | 0.9318 |
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- | 0.6054 | 20.0 | 440 | 0.5487 | 0.9318 |
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- | 0.5813 | 21.0 | 462 | 0.5431 | 0.9545 |
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- | 0.5908 | 22.0 | 484 | 0.5365 | 0.9545 |
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- | 0.561 | 23.0 | 506 | 0.5308 | 0.9545 |
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- | 0.5636 | 24.0 | 528 | 0.5248 | 0.9545 |
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- | 0.5573 | 25.0 | 550 | 0.5195 | 0.9545 |
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- | 0.5585 | 26.0 | 572 | 0.5146 | 0.9545 |
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- | 0.5649 | 27.0 | 594 | 0.5106 | 0.9545 |
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- | 0.5649 | 28.0 | 616 | 0.5081 | 0.9545 |
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- | 0.5532 | 29.0 | 638 | 0.5068 | 0.9545 |
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- | 0.5776 | 30.0 | 660 | 0.5063 | 0.9545 |
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  ### Framework versions
 
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  library_name: peft
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  tags:
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  - generated_from_trainer
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+ base_model: dbmdz/bert-base-italian-xxl-cased
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  metrics:
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  - accuracy
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  model-index:
 
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  # bert-LoRA-reminder
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+ This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2044
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  - Accuracy: 0.9545
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6763 | 1.0 | 22 | 0.6823 | 0.5455 |
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+ | 0.6657 | 2.0 | 44 | 0.6662 | 0.5682 |
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+ | 0.6704 | 3.0 | 66 | 0.6538 | 0.6136 |
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+ | 0.6667 | 4.0 | 88 | 0.6362 | 0.6818 |
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+ | 0.6594 | 5.0 | 110 | 0.6168 | 0.8182 |
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+ | 0.6213 | 6.0 | 132 | 0.5956 | 0.8864 |
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+ | 0.6057 | 7.0 | 154 | 0.5703 | 0.8864 |
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+ | 0.5977 | 8.0 | 176 | 0.5545 | 0.8864 |
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+ | 0.5932 | 9.0 | 198 | 0.5368 | 0.8636 |
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+ | 0.5647 | 10.0 | 220 | 0.5074 | 0.8864 |
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+ | 0.5479 | 11.0 | 242 | 0.4885 | 0.8864 |
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+ | 0.5465 | 12.0 | 264 | 0.4595 | 0.8864 |
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+ | 0.5026 | 13.0 | 286 | 0.4309 | 0.9091 |
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+ | 0.4548 | 14.0 | 308 | 0.4011 | 0.9091 |
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+ | 0.4372 | 15.0 | 330 | 0.3721 | 0.9318 |
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+ | 0.4509 | 16.0 | 352 | 0.3521 | 0.9318 |
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+ | 0.4398 | 17.0 | 374 | 0.3327 | 0.9318 |
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+ | 0.4186 | 18.0 | 396 | 0.3113 | 0.9545 |
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+ | 0.3781 | 19.0 | 418 | 0.2953 | 0.9545 |
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+ | 0.3638 | 20.0 | 440 | 0.2847 | 0.9318 |
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+ | 0.3604 | 21.0 | 462 | 0.2631 | 0.9545 |
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+ | 0.4001 | 22.0 | 484 | 0.2504 | 0.9545 |
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+ | 0.3346 | 23.0 | 506 | 0.2417 | 0.9545 |
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+ | 0.3041 | 24.0 | 528 | 0.2303 | 0.9545 |
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+ | 0.3504 | 25.0 | 550 | 0.2218 | 0.9545 |
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+ | 0.286 | 26.0 | 572 | 0.2152 | 0.9545 |
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+ | 0.3526 | 27.0 | 594 | 0.2102 | 0.9545 |
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+ | 0.2657 | 28.0 | 616 | 0.2066 | 0.9545 |
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+ | 0.3019 | 29.0 | 638 | 0.2052 | 0.9545 |
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+ | 0.307 | 30.0 | 660 | 0.2044 | 0.9545 |
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  ### Framework versions
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