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Token Classification
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Safetensors
bert
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End of training

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  1. README.md +28 -9
  2. model.safetensors +1 -1
  3. runs/Mar25_17-17-44_supermicro/events.out.tfevents.1711358266.supermicro.3476667.0 +3 -0
  4. runs/Mar25_17-19-08_supermicro/events.out.tfevents.1711358351.supermicro.3477082.0 +3 -0
  5. runs/Mar25_17-21-29_supermicro/events.out.tfevents.1711358491.supermicro.3477633.0 +3 -0
  6. runs/Mar25_17-23-20_supermicro/events.out.tfevents.1711358602.supermicro.3477976.0 +3 -0
  7. runs/Mar25_17-47-44_supermicro/events.out.tfevents.1711360076.supermicro.3481966.0 +3 -0
  8. runs/Mar25_20-07-49_supermicro/events.out.tfevents.1711368471.supermicro.3520570.0 +3 -0
  9. training_args.bin +1 -1
README.md CHANGED
@@ -23,13 +23,13 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.6153846153498767
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  - name: Recall
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  type: recall
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- value: 0.804091266599492
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  - name: F1
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  type: f1
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- value: 0.6971945083321585
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -39,10 +39,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the generator dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1990
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- - Precision: 0.6154
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- - Recall: 0.8041
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- - F1: 0.6972
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  ## Model description
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@@ -67,13 +67,32 @@ The following hyperparameters were used during training:
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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: 1
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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- | 0.2157 | 1.0 | 416 | 0.1990 | 0.6154 | 0.8041 | 0.6972 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.901610712050607
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  - name: Recall
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  type: recall
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+ value: 0.8982985303950894
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  - name: F1
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  type: f1
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+ value: 0.8999515736949341
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the generator dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5195
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+ - Precision: 0.9016
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+ - Recall: 0.8983
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+ - F1: 0.9000
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  ## Model description
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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: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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+ | 0.2099 | 1.0 | 416 | 0.1940 | 0.8281 | 0.8152 | 0.8216 |
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+ | 0.1658 | 2.0 | 832 | 0.1799 | 0.8464 | 0.8590 | 0.8527 |
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+ | 0.1276 | 3.0 | 1248 | 0.1821 | 0.8795 | 0.8639 | 0.8716 |
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+ | 0.1076 | 4.0 | 1664 | 0.1961 | 0.8903 | 0.8788 | 0.8845 |
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+ | 0.0792 | 5.0 | 2080 | 0.2277 | 0.8787 | 0.8869 | 0.8828 |
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+ | 0.054 | 6.0 | 2496 | 0.2395 | 0.9084 | 0.8701 | 0.8888 |
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+ | 0.0433 | 7.0 | 2912 | 0.2991 | 0.8999 | 0.8915 | 0.8957 |
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+ | 0.0288 | 8.0 | 3328 | 0.3374 | 0.8919 | 0.8935 | 0.8927 |
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+ | 0.022 | 9.0 | 3744 | 0.3752 | 0.9054 | 0.8921 | 0.8987 |
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+ | 0.0211 | 10.0 | 4160 | 0.4105 | 0.8952 | 0.8985 | 0.8968 |
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+ | 0.0147 | 11.0 | 4576 | 0.4084 | 0.9013 | 0.9004 | 0.9009 |
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+ | 0.0095 | 12.0 | 4992 | 0.4542 | 0.9047 | 0.8952 | 0.8999 |
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+ | 0.01 | 13.0 | 5408 | 0.4516 | 0.9086 | 0.8896 | 0.8990 |
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+ | 0.0087 | 14.0 | 5824 | 0.4521 | 0.9025 | 0.8935 | 0.8980 |
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+ | 0.0069 | 15.0 | 6240 | 0.4878 | 0.9034 | 0.9022 | 0.9028 |
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+ | 0.0042 | 16.0 | 6656 | 0.5097 | 0.9021 | 0.8997 | 0.9009 |
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+ | 0.006 | 17.0 | 7072 | 0.5195 | 0.9054 | 0.9008 | 0.9031 |
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+ | 0.0043 | 18.0 | 7488 | 0.5032 | 0.9009 | 0.8977 | 0.8993 |
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+ | 0.0029 | 19.0 | 7904 | 0.5155 | 0.9003 | 0.8962 | 0.8983 |
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+ | 0.0034 | 20.0 | 8320 | 0.5195 | 0.9016 | 0.8983 | 0.9000 |
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  ### Framework versions
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