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cva-flow-weighted-classifier

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  1. README.md +17 -27
  2. model.safetensors +1 -1
  3. runs/Aug05_16-20-44_49d74c1f1623/events.out.tfevents.1722874845.49d74c1f1623.226.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3893
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- - Accuracy: 0.8416
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- - F1: 0.8571
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- - Precision: 0.9057
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- - Recall: 0.8136
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  ## Model description
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@@ -43,38 +43,28 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-06
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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: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.6798 | 1.0 | 7 | 0.6701 | 0.5941 | 0.6435 | 0.6607 | 0.6271 |
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- | 0.6318 | 2.0 | 14 | 0.6479 | 0.6337 | 0.7299 | 0.6410 | 0.8475 |
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- | 0.6002 | 3.0 | 21 | 0.6237 | 0.6139 | 0.7068 | 0.6351 | 0.7966 |
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- | 0.5562 | 4.0 | 28 | 0.5941 | 0.6634 | 0.7167 | 0.7049 | 0.7288 |
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- | 0.5005 | 5.0 | 35 | 0.5547 | 0.7129 | 0.7434 | 0.7778 | 0.7119 |
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- | 0.4412 | 6.0 | 42 | 0.5162 | 0.7525 | 0.7788 | 0.8148 | 0.7458 |
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- | 0.3961 | 7.0 | 49 | 0.4838 | 0.7723 | 0.7965 | 0.8333 | 0.7627 |
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- | 0.3571 | 8.0 | 56 | 0.4575 | 0.7822 | 0.8036 | 0.8491 | 0.7627 |
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- | 0.3317 | 9.0 | 63 | 0.4378 | 0.8020 | 0.8214 | 0.8679 | 0.7797 |
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- | 0.2796 | 10.0 | 70 | 0.4263 | 0.8119 | 0.8319 | 0.8704 | 0.7966 |
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- | 0.2554 | 11.0 | 77 | 0.4187 | 0.8218 | 0.8421 | 0.8727 | 0.8136 |
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- | 0.2429 | 12.0 | 84 | 0.4074 | 0.8218 | 0.8421 | 0.8727 | 0.8136 |
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- | 0.2222 | 13.0 | 91 | 0.3992 | 0.8416 | 0.8571 | 0.9057 | 0.8136 |
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- | 0.2089 | 14.0 | 98 | 0.3948 | 0.8515 | 0.8649 | 0.9231 | 0.8136 |
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- | 0.2055 | 15.0 | 105 | 0.3947 | 0.8515 | 0.8649 | 0.9231 | 0.8136 |
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- | 0.2121 | 16.0 | 112 | 0.3949 | 0.8515 | 0.8673 | 0.9074 | 0.8305 |
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- | 0.1739 | 17.0 | 119 | 0.3917 | 0.8416 | 0.8571 | 0.9057 | 0.8136 |
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- | 0.1768 | 18.0 | 126 | 0.3896 | 0.8416 | 0.8571 | 0.9057 | 0.8136 |
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- | 0.1778 | 19.0 | 133 | 0.3894 | 0.8416 | 0.8571 | 0.9057 | 0.8136 |
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- | 0.1659 | 20.0 | 140 | 0.3893 | 0.8416 | 0.8571 | 0.9057 | 0.8136 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2909
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+ - Accuracy: 0.88
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+ - F1: 0.8966
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+ - Precision: 0.9123
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+ - Recall: 0.8814
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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: 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: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.6654 | 1.0 | 7 | 0.6058 | 0.75 | 0.7899 | 0.7833 | 0.7966 |
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+ | 0.5859 | 2.0 | 14 | 0.5028 | 0.8 | 0.8182 | 0.8824 | 0.7627 |
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+ | 0.4887 | 3.0 | 21 | 0.4160 | 0.81 | 0.8257 | 0.9 | 0.7627 |
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+ | 0.3762 | 4.0 | 28 | 0.3439 | 0.86 | 0.8772 | 0.9091 | 0.8475 |
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+ | 0.3176 | 5.0 | 35 | 0.3046 | 0.88 | 0.8947 | 0.9273 | 0.8644 |
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+ | 0.2659 | 6.0 | 42 | 0.2937 | 0.88 | 0.8947 | 0.9273 | 0.8644 |
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+ | 0.2592 | 7.0 | 49 | 0.2940 | 0.87 | 0.8889 | 0.8966 | 0.8814 |
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+ | 0.213 | 8.0 | 56 | 0.2920 | 0.87 | 0.8889 | 0.8966 | 0.8814 |
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+ | 0.1946 | 9.0 | 63 | 0.2899 | 0.88 | 0.8947 | 0.9273 | 0.8644 |
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+ | 0.2042 | 10.0 | 70 | 0.2909 | 0.88 | 0.8966 | 0.9123 | 0.8814 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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