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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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  2. model.safetensors +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ 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: 0.0277
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- - Precision: 0.9353
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- - Recall: 0.9630
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- - F1: 0.9489
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- - Accuracy: 0.9910
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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: 2e-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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- - num_epochs: 4
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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 | 0.0351 | 0.9302 | 0.8889 | 0.9091 | 0.9858 |
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- | No log | 2.0 | 184 | 0.0277 | 0.9353 | 0.9630 | 0.9489 | 0.9910 |
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- | No log | 3.0 | 276 | 0.0391 | 0.9353 | 0.9630 | 0.9489 | 0.9910 |
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- | No log | 4.0 | 368 | 0.0362 | 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: 0.0351
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+ - Precision: 0.9051
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+ - Recall: 0.9185
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+ - F1: 0.9118
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+ - Accuracy: 0.9873
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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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+ - 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 | 92 | 0.0589 | 0.8370 | 0.8370 | 0.8370 | 0.9813 |
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+ | No log | 2.0 | 184 | 0.0351 | 0.9051 | 0.9185 | 0.9118 | 0.9873 |
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+ | No log | 3.0 | 276 | 0.0626 | 0.8849 | 0.9111 | 0.8978 | 0.9828 |
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+ | No log | 4.0 | 368 | 0.0379 | 0.9357 | 0.9704 | 0.9527 | 0.9910 |
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+ | No log | 5.0 | 460 | 0.0384 | 0.9353 | 0.9630 | 0.9489 | 0.9903 |
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+ | 0.0721 | 6.0 | 552 | 0.0421 | 0.9353 | 0.9630 | 0.9489 | 0.9903 |
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  ### Framework versions
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