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update model card README.md

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  ---
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- license: apache-2.0
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  tags:
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  - generated_from_trainer
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  model-index:
@@ -12,9 +12,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # mbti-career
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- This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3400
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  ## Model description
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@@ -40,17 +40,37 @@ The following hyperparameters were used during training:
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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: 300
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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- | 0.6344 | 0.59 | 100 | 0.5630 |
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- | 0.5653 | 1.18 | 200 | 0.4788 |
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- | 0.4784 | 1.76 | 300 | 0.4241 |
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- | 0.3999 | 2.35 | 400 | 0.3505 |
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- | 0.3595 | 2.94 | 500 | 0.3400 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
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+ license: mit
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  tags:
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  - generated_from_trainer
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  model-index:
 
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  # mbti-career
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3516
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  ## Model description
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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: 300
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+ - num_epochs: 15
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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+ | 0.6547 | 0.59 | 100 | 0.6169 |
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+ | 0.5967 | 1.18 | 200 | 0.5943 |
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+ | 0.5872 | 1.76 | 300 | 0.5696 |
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+ | 0.554 | 2.35 | 400 | 0.5287 |
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+ | 0.5041 | 2.94 | 500 | 0.4890 |
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+ | 0.4773 | 3.53 | 600 | 0.4895 |
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+ | 0.4691 | 4.12 | 700 | 0.4840 |
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+ | 0.4253 | 4.71 | 800 | 0.4573 |
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+ | 0.4002 | 5.29 | 900 | 0.4240 |
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+ | 0.3813 | 5.88 | 1000 | 0.4031 |
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+ | 0.3561 | 6.47 | 1100 | 0.3943 |
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+ | 0.3359 | 7.06 | 1200 | 0.3864 |
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+ | 0.3126 | 7.65 | 1300 | 0.3889 |
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+ | 0.2948 | 8.24 | 1400 | 0.3869 |
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+ | 0.2816 | 8.82 | 1500 | 0.3788 |
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+ | 0.2522 | 9.41 | 1600 | 0.3891 |
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+ | 0.2451 | 10.0 | 1700 | 0.3849 |
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+ | 0.2148 | 10.59 | 1800 | 0.3784 |
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+ | 0.2132 | 11.18 | 1900 | 0.3716 |
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+ | 0.1882 | 11.76 | 2000 | 0.3659 |
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+ | 0.1754 | 12.35 | 2100 | 0.3737 |
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+ | 0.169 | 12.94 | 2200 | 0.3711 |
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+ | 0.1559 | 13.53 | 2300 | 0.3672 |
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+ | 0.1537 | 14.12 | 2400 | 0.3391 |
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+ | 0.1427 | 14.71 | 2500 | 0.3516 |
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  ### Framework versions