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Text Classification
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End of training

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  1. README.md +44 -15
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. runs/Mar21_14-01-45_87faeb00998b/events.out.tfevents.1711029723.87faeb00998b.2104.6 +3 -0
  5. runs/Mar21_14-01-45_87faeb00998b/events.out.tfevents.1711030114.87faeb00998b.2104.7 +3 -0
  6. training_args.bin +2 -2
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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.5612
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- - Precision: 0.6306
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- - Recall: 0.5952
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- - F1: 0.5919
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- - Accuracy: 0.5952
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  ## Model description
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@@ -43,7 +43,7 @@ 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-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -55,19 +55,48 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.1019 | 0.57 | 30 | 4.2596 | 0.6256 | 0.5595 | 0.5513 | 0.5595 |
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- | 0.0012 | 1.13 | 60 | 3.3033 | 0.7069 | 0.7024 | 0.6951 | 0.7024 |
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- | 0.0 | 1.7 | 90 | 3.2128 | 0.7355 | 0.7262 | 0.7211 | 0.7262 |
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- | 0.0008 | 2.26 | 120 | 4.5126 | 0.6228 | 0.5833 | 0.5803 | 0.5833 |
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- | 0.0 | 2.83 | 150 | 4.5958 | 0.6228 | 0.5833 | 0.5803 | 0.5833 |
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- | 0.0 | 3.4 | 180 | 4.5741 | 0.6228 | 0.5833 | 0.5803 | 0.5833 |
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- | 0.0 | 3.96 | 210 | 4.5647 | 0.6228 | 0.5833 | 0.5803 | 0.5833 |
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- | 0.0 | 4.53 | 240 | 4.5612 | 0.6306 | 0.5952 | 0.5919 | 0.5952 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.38.2
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.18.0
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  - Tokenizers 0.15.2
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6683
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+ - Precision: 0.7949
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+ - Recall: 0.7899
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+ - F1: 0.7889
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+ - Accuracy: 0.7899
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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: 2e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.944 | 0.14 | 30 | 1.9173 | 0.0510 | 0.1569 | 0.0723 | 0.1569 |
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+ | 1.9012 | 0.27 | 60 | 1.8549 | 0.2641 | 0.2325 | 0.2007 | 0.2325 |
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+ | 1.7674 | 0.41 | 90 | 1.6500 | 0.3579 | 0.3922 | 0.3300 | 0.3922 |
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+ | 1.5686 | 0.54 | 120 | 1.4486 | 0.4476 | 0.4426 | 0.3816 | 0.4426 |
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+ | 1.5083 | 0.68 | 150 | 1.3363 | 0.6332 | 0.5686 | 0.5467 | 0.5686 |
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+ | 1.3913 | 0.81 | 180 | 1.1930 | 0.6850 | 0.6779 | 0.6709 | 0.6779 |
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+ | 1.1313 | 0.95 | 210 | 1.0729 | 0.7114 | 0.7031 | 0.6923 | 0.7031 |
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+ | 1.0349 | 1.08 | 240 | 1.0719 | 0.6903 | 0.6471 | 0.6283 | 0.6471 |
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+ | 0.9403 | 1.22 | 270 | 0.9282 | 0.7511 | 0.7311 | 0.7225 | 0.7311 |
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+ | 0.8064 | 1.35 | 300 | 0.8638 | 0.7553 | 0.7255 | 0.7165 | 0.7255 |
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+ | 0.723 | 1.49 | 330 | 0.8107 | 0.7309 | 0.7115 | 0.7089 | 0.7115 |
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+ | 0.768 | 1.62 | 360 | 0.7942 | 0.7458 | 0.7283 | 0.7239 | 0.7283 |
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+ | 0.678 | 1.76 | 390 | 0.7072 | 0.7732 | 0.7647 | 0.7634 | 0.7647 |
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+ | 0.5985 | 1.89 | 420 | 0.7083 | 0.7579 | 0.7451 | 0.7413 | 0.7451 |
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+ | 0.5363 | 2.03 | 450 | 0.6739 | 0.7851 | 0.7787 | 0.7773 | 0.7787 |
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+ | 0.3922 | 2.16 | 480 | 0.6643 | 0.7562 | 0.7535 | 0.7505 | 0.7535 |
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+ | 0.4482 | 2.3 | 510 | 0.7095 | 0.7805 | 0.7675 | 0.7659 | 0.7675 |
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+ | 0.3507 | 2.43 | 540 | 0.6652 | 0.7699 | 0.7619 | 0.7592 | 0.7619 |
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+ | 0.3825 | 2.57 | 570 | 0.6504 | 0.7725 | 0.7675 | 0.7657 | 0.7675 |
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+ | 0.3617 | 2.7 | 600 | 0.6122 | 0.8084 | 0.8011 | 0.8014 | 0.8011 |
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+ | 0.3178 | 2.84 | 630 | 0.6242 | 0.7963 | 0.7899 | 0.7884 | 0.7899 |
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+ | 0.303 | 2.97 | 660 | 0.5939 | 0.8038 | 0.7983 | 0.7977 | 0.7983 |
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+ | 0.2146 | 3.11 | 690 | 0.6557 | 0.7957 | 0.7871 | 0.7838 | 0.7871 |
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+ | 0.2301 | 3.24 | 720 | 0.6149 | 0.7879 | 0.7871 | 0.7864 | 0.7871 |
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+ | 0.2089 | 3.38 | 750 | 0.6139 | 0.7959 | 0.7927 | 0.7932 | 0.7927 |
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+ | 0.188 | 3.51 | 780 | 0.6278 | 0.7971 | 0.7927 | 0.7921 | 0.7927 |
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+ | 0.1495 | 3.65 | 810 | 0.6432 | 0.8145 | 0.8067 | 0.8069 | 0.8067 |
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+ | 0.1817 | 3.78 | 840 | 0.6433 | 0.8150 | 0.8095 | 0.8093 | 0.8095 |
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+ | 0.1572 | 3.92 | 870 | 0.6510 | 0.8078 | 0.8011 | 0.8012 | 0.8011 |
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+ | 0.1364 | 4.05 | 900 | 0.6502 | 0.7986 | 0.7927 | 0.7922 | 0.7927 |
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+ | 0.0995 | 4.19 | 930 | 0.6821 | 0.7933 | 0.7843 | 0.7827 | 0.7843 |
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+ | 0.1209 | 4.32 | 960 | 0.6681 | 0.8020 | 0.7955 | 0.7949 | 0.7955 |
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+ | 0.1258 | 4.46 | 990 | 0.6662 | 0.8056 | 0.7983 | 0.7985 | 0.7983 |
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+ | 0.0961 | 4.59 | 1020 | 0.6615 | 0.7924 | 0.7871 | 0.7867 | 0.7871 |
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+ | 0.1352 | 4.73 | 1050 | 0.6642 | 0.7888 | 0.7843 | 0.7834 | 0.7843 |
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+ | 0.1076 | 4.86 | 1080 | 0.6677 | 0.7988 | 0.7927 | 0.7920 | 0.7927 |
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+ | 0.1062 | 5.0 | 1110 | 0.6683 | 0.7949 | 0.7899 | 0.7889 | 0.7899 |
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  ### Framework versions
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+ - Transformers 4.39.0
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.18.0
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  - Tokenizers 0.15.2
config.json CHANGED
@@ -38,6 +38,6 @@
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  "sinusoidal_pos_embds": false,
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  "tie_weights_": true,
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  "torch_dtype": "float32",
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- "transformers_version": "4.38.2",
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  "vocab_size": 30522
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  }
 
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  "sinusoidal_pos_embds": false,
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  "tie_weights_": true,
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  "torch_dtype": "float32",
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+ "transformers_version": "4.39.0",
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  "vocab_size": 30522
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  }
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