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djsull/ner_insurence_roberta

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README.md CHANGED
@@ -19,11 +19,11 @@ 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: 0.0012
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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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  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
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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: 8
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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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- | 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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  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: 0.0014
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+ - Precision: 0.9275
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+ - Recall: 0.9314
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+ - F1: 0.9295
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+ - Accuracy: 0.9996
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  ## Model description
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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: 64
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+ - eval_batch_size: 64
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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: 5
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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 | 127 | 0.0032 | 0.8493 | 0.8618 | 0.8555 | 0.9990 |
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+ | No log | 2.0 | 254 | 0.0020 | 0.8841 | 0.9008 | 0.8924 | 0.9994 |
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+ | No log | 3.0 | 381 | 0.0014 | 0.9152 | 0.9219 | 0.9185 | 0.9996 |
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+ | 0.0088 | 4.0 | 508 | 0.0014 | 0.9275 | 0.9314 | 0.9295 | 0.9996 |
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+ | 0.0088 | 5.0 | 635 | 0.0013 | 0.9225 | 0.9293 | 0.9259 | 0.9996 |
 
 
 
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  ### Framework versions
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