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te-zsc-authentic

Text Classification
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roberta
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End of training

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  1. README.md +36 -36
  2. logs/events.out.tfevents.1717066759.47e332b3b642.2555.10 +3 -0
  3. model.safetensors +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -9,23 +9,23 @@ metrics:
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  - recall
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  - f1
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  model-index:
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- - name: AUTH_300524_epoch_3
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  results: []
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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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  should probably proofread and complete it, then remove this comment. -->
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- # AUTH_300524_epoch_3
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  This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4712
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- - Accuracy: 0.9048
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- - Precision: 0.9066
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- - Recall: 0.9048
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- - F1: 0.9047
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- - Ratio: 0.4669
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  ## Model description
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@@ -61,34 +61,34 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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- | 0.4853 | 0.0354 | 10 | 0.5104 | 0.8898 | 0.8907 | 0.8898 | 0.8897 | 0.5240 |
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- | 0.4589 | 0.0708 | 20 | 0.5011 | 0.8828 | 0.8868 | 0.8828 | 0.8825 | 0.4489 |
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- | 0.4639 | 0.1062 | 30 | 0.4819 | 0.8948 | 0.8954 | 0.8948 | 0.8948 | 0.4810 |
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- | 0.4768 | 0.1416 | 40 | 0.4985 | 0.8918 | 0.8954 | 0.8918 | 0.8915 | 0.4519 |
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- | 0.4726 | 0.1770 | 50 | 0.4828 | 0.8948 | 0.8959 | 0.8948 | 0.8947 | 0.4729 |
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- | 0.4797 | 0.2124 | 60 | 0.5044 | 0.8788 | 0.8866 | 0.8788 | 0.8781 | 0.4289 |
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- | 0.4668 | 0.2478 | 70 | 0.5080 | 0.8848 | 0.8855 | 0.8848 | 0.8847 | 0.4790 |
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- | 0.4904 | 0.2832 | 80 | 0.5072 | 0.8878 | 0.8903 | 0.8878 | 0.8876 | 0.4599 |
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- | 0.469 | 0.3186 | 90 | 0.5092 | 0.8818 | 0.8842 | 0.8818 | 0.8816 | 0.5401 |
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- | 0.4814 | 0.3540 | 100 | 0.4992 | 0.8958 | 0.8979 | 0.8958 | 0.8957 | 0.4639 |
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- | 0.4696 | 0.3894 | 110 | 0.5132 | 0.8858 | 0.8922 | 0.8858 | 0.8853 | 0.4359 |
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- | 0.4763 | 0.4248 | 120 | 0.5040 | 0.8908 | 0.8909 | 0.8908 | 0.8908 | 0.5070 |
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- | 0.4758 | 0.4602 | 130 | 0.5035 | 0.8928 | 0.8945 | 0.8928 | 0.8927 | 0.4669 |
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- | 0.4932 | 0.4956 | 140 | 0.4999 | 0.8868 | 0.8899 | 0.8868 | 0.8865 | 0.4549 |
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- | 0.5136 | 0.5310 | 150 | 0.4858 | 0.8908 | 0.8943 | 0.8908 | 0.8905 | 0.4529 |
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- | 0.5225 | 0.5664 | 160 | 0.4778 | 0.8898 | 0.8898 | 0.8898 | 0.8898 | 0.5060 |
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- | 0.4901 | 0.6018 | 170 | 0.4831 | 0.8948 | 0.8959 | 0.8948 | 0.8947 | 0.4729 |
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- | 0.4292 | 0.6372 | 180 | 0.5023 | 0.8928 | 0.8966 | 0.8928 | 0.8925 | 0.4509 |
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- | 0.502 | 0.6726 | 190 | 0.5078 | 0.8918 | 0.8975 | 0.8918 | 0.8914 | 0.4399 |
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- | 0.4907 | 0.7080 | 200 | 0.4845 | 0.8968 | 0.8990 | 0.8968 | 0.8967 | 0.4629 |
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- | 0.4522 | 0.7434 | 210 | 0.4779 | 0.8988 | 0.8994 | 0.8988 | 0.8988 | 0.4810 |
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- | 0.5293 | 0.7788 | 220 | 0.4759 | 0.9018 | 0.9041 | 0.9018 | 0.9017 | 0.4619 |
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- | 0.4977 | 0.8142 | 230 | 0.4809 | 0.8928 | 0.8980 | 0.8928 | 0.8924 | 0.4429 |
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- | 0.4937 | 0.8496 | 240 | 0.4754 | 0.8998 | 0.9021 | 0.8998 | 0.8997 | 0.4619 |
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- | 0.523 | 0.8850 | 250 | 0.4736 | 0.8978 | 0.8984 | 0.8978 | 0.8978 | 0.4800 |
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- | 0.5729 | 0.9204 | 260 | 0.4713 | 0.9008 | 0.9022 | 0.9008 | 0.9007 | 0.4709 |
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- | 0.4823 | 0.9558 | 270 | 0.4712 | 0.9048 | 0.9066 | 0.9048 | 0.9047 | 0.4669 |
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- | 0.495 | 0.9912 | 280 | 0.4713 | 0.9048 | 0.9066 | 0.9048 | 0.9047 | 0.4669 |
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  ### Framework versions
 
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  - recall
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  - f1
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  model-index:
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+ - name: AUTH_300524_epoch_4
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  results: []
14
  ---
15
 
16
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
  should probably proofread and complete it, then remove this comment. -->
18
 
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+ # AUTH_300524_epoch_4
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  This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4656
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+ - Accuracy: 0.9038
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+ - Precision: 0.9047
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+ - Recall: 0.9038
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+ - F1: 0.9038
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+ - Ratio: 0.4760
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | 0.4294 | 0.0354 | 10 | 0.5003 | 0.9018 | 0.9020 | 0.9018 | 0.9018 | 0.5100 |
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+ | 0.386 | 0.0708 | 20 | 0.5308 | 0.8938 | 0.8952 | 0.8938 | 0.8937 | 0.4699 |
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+ | 0.4424 | 0.1062 | 30 | 0.4881 | 0.8998 | 0.9000 | 0.8998 | 0.8998 | 0.4900 |
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+ | 0.42 | 0.1416 | 40 | 0.4916 | 0.9068 | 0.9091 | 0.9068 | 0.9067 | 0.4629 |
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+ | 0.418 | 0.1770 | 50 | 0.4905 | 0.8968 | 0.8968 | 0.8968 | 0.8968 | 0.4950 |
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+ | 0.4402 | 0.2124 | 60 | 0.5034 | 0.8988 | 0.9027 | 0.8988 | 0.8986 | 0.4509 |
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+ | 0.4141 | 0.2478 | 70 | 0.5085 | 0.9028 | 0.9061 | 0.9028 | 0.9026 | 0.4549 |
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+ | 0.4836 | 0.2832 | 80 | 0.4875 | 0.9028 | 0.9029 | 0.9028 | 0.9028 | 0.4910 |
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+ | 0.4361 | 0.3186 | 90 | 0.4876 | 0.8998 | 0.8998 | 0.8998 | 0.8998 | 0.4980 |
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+ | 0.45 | 0.3540 | 100 | 0.4985 | 0.8938 | 0.8938 | 0.8938 | 0.8938 | 0.5040 |
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+ | 0.4648 | 0.3894 | 110 | 0.5236 | 0.8858 | 0.8954 | 0.8858 | 0.8851 | 0.4218 |
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+ | 0.4714 | 0.4248 | 120 | 0.5009 | 0.8888 | 0.8888 | 0.8888 | 0.8888 | 0.5010 |
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+ | 0.4628 | 0.4602 | 130 | 0.4971 | 0.8868 | 0.8871 | 0.8868 | 0.8867 | 0.4850 |
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+ | 0.4513 | 0.4956 | 140 | 0.4971 | 0.8968 | 0.9003 | 0.8968 | 0.8966 | 0.4529 |
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+ | 0.4905 | 0.5310 | 150 | 0.4873 | 0.8938 | 0.8969 | 0.8938 | 0.8936 | 0.4559 |
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+ | 0.4875 | 0.5664 | 160 | 0.4760 | 0.8948 | 0.8948 | 0.8948 | 0.8948 | 0.4950 |
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+ | 0.4593 | 0.6018 | 170 | 0.4818 | 0.8918 | 0.8918 | 0.8918 | 0.8918 | 0.4960 |
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+ | 0.403 | 0.6372 | 180 | 0.4927 | 0.8928 | 0.8936 | 0.8928 | 0.8927 | 0.4770 |
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+ | 0.4838 | 0.6726 | 190 | 0.5039 | 0.8958 | 0.9001 | 0.8958 | 0.8955 | 0.4479 |
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+ | 0.4512 | 0.7080 | 200 | 0.4913 | 0.8978 | 0.9009 | 0.8978 | 0.8976 | 0.4559 |
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+ | 0.4415 | 0.7434 | 210 | 0.4874 | 0.8988 | 0.8989 | 0.8988 | 0.8988 | 0.4930 |
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+ | 0.5317 | 0.7788 | 220 | 0.4786 | 0.9018 | 0.9021 | 0.9018 | 0.9018 | 0.4860 |
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+ | 0.4718 | 0.8142 | 230 | 0.4746 | 0.9008 | 0.9041 | 0.9008 | 0.9006 | 0.4549 |
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+ | 0.473 | 0.8496 | 240 | 0.4686 | 0.9028 | 0.9044 | 0.9028 | 0.9027 | 0.4689 |
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+ | 0.499 | 0.8850 | 250 | 0.4689 | 0.9028 | 0.9031 | 0.9028 | 0.9028 | 0.4870 |
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+ | 0.5655 | 0.9204 | 260 | 0.4661 | 0.9068 | 0.9074 | 0.9068 | 0.9068 | 0.4810 |
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+ | 0.4583 | 0.9558 | 270 | 0.4654 | 0.9048 | 0.9057 | 0.9048 | 0.9048 | 0.4770 |
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+ | 0.4734 | 0.9912 | 280 | 0.4656 | 0.9038 | 0.9047 | 0.9038 | 0.9038 | 0.4760 |
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  ### Framework versions
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