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djsull/ner_insurence_roberta

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  1. README.md +12 -12
  2. model.safetensors +1 -1
  3. runs/May31_05-33-00_129-146-79-76/.nfs00000000000007010000003b +0 -0
  4. runs/May31_06-02-13_129-146-79-76/.nfs000000000000070e0000003c +0 -0
  5. runs/May31_06-04-20_129-146-79-76/events.out.tfevents.1717135460.129-146-79-76.3801064.3 +3 -0
  6. runs/May31_06-04-20_129-146-79-76/events.out.tfevents.1717136335.129-146-79-76.3801064.4 +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -19,10 +19,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0013
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- - Precision: 0.9517
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- - Recall: 0.9554
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- - F1: 0.9536
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  - Accuracy: 0.9997
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  ## Model description
@@ -54,14 +54,14 @@ The following hyperparameters were used during training:
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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 | 495 | 0.0026 | 0.8654 | 0.8663 | 0.8659 | 0.9990 |
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- | 0.0113 | 2.0 | 990 | 0.0013 | 0.9220 | 0.9273 | 0.9246 | 0.9996 |
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- | 0.002 | 3.0 | 1485 | 0.0016 | 0.9003 | 0.9012 | 0.9007 | 0.9994 |
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- | 0.0012 | 4.0 | 1980 | 0.0011 | 0.9477 | 0.9486 | 0.9482 | 0.9997 |
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- | 0.0008 | 5.0 | 2475 | 0.0012 | 0.9373 | 0.9419 | 0.9396 | 0.9997 |
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- | 0.0007 | 6.0 | 2970 | 0.0012 | 0.9392 | 0.9428 | 0.9410 | 0.9997 |
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- | 0.0005 | 7.0 | 3465 | 0.0013 | 0.9517 | 0.9554 | 0.9536 | 0.9997 |
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- | 0.0003 | 8.0 | 3960 | 0.0013 | 0.9517 | 0.9554 | 0.9536 | 0.9997 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0012
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+ - Precision: 0.9483
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+ - Recall: 0.9473
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+ - F1: 0.9478
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  - Accuracy: 0.9997
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0101 | 1.0 | 505 | 0.0015 | 0.9051 | 0.9156 | 0.9103 | 0.9995 |
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+ | 0.0017 | 2.0 | 1010 | 0.0014 | 0.9284 | 0.9304 | 0.9294 | 0.9995 |
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+ | 0.001 | 3.0 | 1515 | 0.0010 | 0.9380 | 0.9409 | 0.9394 | 0.9997 |
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+ | 0.0007 | 4.0 | 2020 | 0.0010 | 0.9347 | 0.9357 | 0.9352 | 0.9997 |
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+ | 0.0005 | 5.0 | 2525 | 0.0017 | 0.9197 | 0.9188 | 0.9193 | 0.9995 |
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+ | 0.0004 | 6.0 | 3030 | 0.0010 | 0.9453 | 0.9483 | 0.9468 | 0.9997 |
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+ | 0.0002 | 7.0 | 3535 | 0.0012 | 0.9463 | 0.9473 | 0.9468 | 0.9997 |
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+ | 0.0001 | 8.0 | 4040 | 0.0012 | 0.9483 | 0.9473 | 0.9478 | 0.9997 |
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  ### Framework versions
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