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deberta-v3-base-financial-inc-dec-ner

Token Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
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Venkatesh4342 commited on
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End of training

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  1. README.md +23 -13
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,10 +22,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0416
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- - Precision: 0.9291
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  - Recall: 0.9704
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- - F1: 0.9493
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- - Accuracy: 0.9910
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  ## Model description
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@@ -44,24 +44,34 @@ 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: 2e-05
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- - train_batch_size: 4
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- - eval_batch_size: 4
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 6
 
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 184 | 0.0454 | 0.9154 | 0.8815 | 0.8981 | 0.9843 |
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- | No log | 2.0 | 368 | 0.0444 | 0.9220 | 0.9630 | 0.9420 | 0.9903 |
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- | 0.0654 | 3.0 | 552 | 0.0416 | 0.9291 | 0.9704 | 0.9493 | 0.9910 |
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- | 0.0654 | 4.0 | 736 | 0.0422 | 0.9489 | 0.9630 | 0.9559 | 0.9918 |
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- | 0.0654 | 5.0 | 920 | 0.0451 | 0.9416 | 0.9556 | 0.9485 | 0.9910 |
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- | 0.0064 | 6.0 | 1104 | 0.0461 | 0.9416 | 0.9556 | 0.9485 | 0.9910 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0416
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+ - Precision: 0.9632
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  - Recall: 0.9704
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+ - F1: 0.9668
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+ - Accuracy: 0.9933
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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: 1e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 15
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 92 | 0.1193 | 0.625 | 0.7407 | 0.6780 | 0.9588 |
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+ | No log | 2.0 | 184 | 0.0522 | 0.8643 | 0.8963 | 0.88 | 0.9798 |
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+ | No log | 3.0 | 276 | 0.0554 | 0.8897 | 0.8963 | 0.8930 | 0.9835 |
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+ | No log | 4.0 | 368 | 0.0362 | 0.9416 | 0.9556 | 0.9485 | 0.9910 |
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+ | No log | 5.0 | 460 | 0.0315 | 0.9286 | 0.9630 | 0.9455 | 0.9918 |
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+ | 0.1731 | 6.0 | 552 | 0.0416 | 0.9632 | 0.9704 | 0.9668 | 0.9933 |
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+ | 0.1731 | 7.0 | 644 | 0.0496 | 0.9420 | 0.9630 | 0.9524 | 0.9910 |
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+ | 0.1731 | 8.0 | 736 | 0.0527 | 0.9420 | 0.9630 | 0.9524 | 0.9910 |
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+ | 0.1731 | 9.0 | 828 | 0.0604 | 0.9348 | 0.9556 | 0.9451 | 0.9895 |
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+ | 0.1731 | 10.0 | 920 | 0.0564 | 0.9420 | 0.9630 | 0.9524 | 0.9910 |
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+ | 0.0028 | 11.0 | 1012 | 0.0571 | 0.9493 | 0.9704 | 0.9597 | 0.9918 |
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+ | 0.0028 | 12.0 | 1104 | 0.0570 | 0.9493 | 0.9704 | 0.9597 | 0.9918 |
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+ | 0.0028 | 13.0 | 1196 | 0.0559 | 0.9493 | 0.9704 | 0.9597 | 0.9918 |
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+ | 0.0028 | 14.0 | 1288 | 0.0574 | 0.9493 | 0.9704 | 0.9597 | 0.9918 |
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+ | 0.0028 | 15.0 | 1380 | 0.0576 | 0.9493 | 0.9704 | 0.9597 | 0.9918 |
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  ### Framework versions
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