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trainer10

Text Classification
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distilbert
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End of training

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  1. README.md +118 -0
  2. model.safetensors +1 -1
  3. runs/Mar13_10-47-05_25c553de5b64/events.out.tfevents.1710326961.25c553de5b64.114.0 +3 -0
  4. runs/Mar13_10-47-05_25c553de5b64/events.out.tfevents.1710330728.25c553de5b64.114.1 +3 -0
  5. training_args.bin +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-cased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: trainer10
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+ results: []
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+ ---
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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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+
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+ # trainer10
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+
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+ This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.7683
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+ - Precision: 0.6720
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+ - Recall: 0.6548
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+ - F1: 0.6399
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+ - Accuracy: 0.6548
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+ - 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: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.9224 | 0.57 | 30 | 1.8591 | 0.2040 | 0.2857 | 0.2111 | 0.2857 |
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+ | 1.8121 | 1.13 | 60 | 1.7775 | 0.1794 | 0.2738 | 0.1945 | 0.2738 |
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+ | 1.664 | 1.7 | 90 | 1.6257 | 0.3041 | 0.4286 | 0.3424 | 0.4286 |
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+ | 1.4444 | 2.26 | 120 | 1.4228 | 0.6559 | 0.5833 | 0.5436 | 0.5833 |
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+ | 1.1691 | 2.83 | 150 | 1.2442 | 0.5333 | 0.5595 | 0.5310 | 0.5595 |
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+ | 0.8134 | 3.4 | 180 | 1.1036 | 0.6356 | 0.6190 | 0.5953 | 0.6190 |
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+ | 0.6325 | 3.96 | 210 | 1.0135 | 0.6977 | 0.6429 | 0.6292 | 0.6429 |
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+ | 0.3398 | 4.53 | 240 | 0.9762 | 0.65 | 0.6310 | 0.6221 | 0.6310 |
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+ | 0.2877 | 5.09 | 270 | 1.0084 | 0.6312 | 0.6071 | 0.5929 | 0.6071 |
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+ | 0.1449 | 5.66 | 300 | 1.0798 | 0.6629 | 0.6429 | 0.6378 | 0.6429 |
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+ | 0.0931 | 6.23 | 330 | 1.1399 | 0.6166 | 0.5952 | 0.5883 | 0.5952 |
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+ | 0.0551 | 6.79 | 360 | 1.3079 | 0.6214 | 0.6071 | 0.6033 | 0.6071 |
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+ | 0.0343 | 7.36 | 390 | 1.2162 | 0.6883 | 0.6548 | 0.6420 | 0.6548 |
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+ | 0.0179 | 7.92 | 420 | 1.3300 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0118 | 8.49 | 450 | 1.3735 | 0.6494 | 0.6310 | 0.6201 | 0.6310 |
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+ | 0.0138 | 9.06 | 480 | 1.5237 | 0.6630 | 0.6310 | 0.6198 | 0.6310 |
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+ | 0.0081 | 9.62 | 510 | 1.4684 | 0.6896 | 0.6667 | 0.6515 | 0.6667 |
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+ | 0.0065 | 10.19 | 540 | 1.5379 | 0.6759 | 0.6548 | 0.6413 | 0.6548 |
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+ | 0.0057 | 10.75 | 570 | 1.5456 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.005 | 11.32 | 600 | 1.4894 | 0.6616 | 0.6310 | 0.6172 | 0.6310 |
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+ | 0.0044 | 11.89 | 630 | 1.5614 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0042 | 12.45 | 660 | 1.6137 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0036 | 13.02 | 690 | 1.6251 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0033 | 13.58 | 720 | 1.6231 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0033 | 14.15 | 750 | 1.6595 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.003 | 14.72 | 780 | 1.6430 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0027 | 15.28 | 810 | 1.6801 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0026 | 15.85 | 840 | 1.6682 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0025 | 16.42 | 870 | 1.6700 | 0.6605 | 0.6310 | 0.6187 | 0.6310 |
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+ | 0.0024 | 16.98 | 900 | 1.6956 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0022 | 17.55 | 930 | 1.6928 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.002 | 18.11 | 960 | 1.6913 | 0.6605 | 0.6310 | 0.6187 | 0.6310 |
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+ | 0.002 | 18.68 | 990 | 1.6892 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+ | 0.002 | 19.25 | 1020 | 1.7129 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0019 | 19.81 | 1050 | 1.7287 | 0.6744 | 0.6429 | 0.6300 | 0.6429 |
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+ | 0.0018 | 20.38 | 1080 | 1.7370 | 0.6605 | 0.6310 | 0.6187 | 0.6310 |
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+ | 0.0017 | 20.94 | 1110 | 1.7435 | 0.6605 | 0.6310 | 0.6187 | 0.6310 |
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+ | 0.0017 | 21.51 | 1140 | 1.7369 | 0.6561 | 0.6310 | 0.6180 | 0.6310 |
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+ | 0.0016 | 22.08 | 1170 | 1.7509 | 0.6561 | 0.6310 | 0.6180 | 0.6310 |
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+ | 0.0016 | 22.64 | 1200 | 1.7543 | 0.6700 | 0.6429 | 0.6293 | 0.6429 |
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+ | 0.0015 | 23.21 | 1230 | 1.7679 | 0.6700 | 0.6429 | 0.6293 | 0.6429 |
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+ | 0.0015 | 23.77 | 1260 | 1.7700 | 0.6561 | 0.6310 | 0.6180 | 0.6310 |
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+ | 0.0016 | 24.34 | 1290 | 1.7573 | 0.6614 | 0.6429 | 0.6294 | 0.6429 |
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+ | 0.0015 | 24.91 | 1320 | 1.7603 | 0.6614 | 0.6429 | 0.6294 | 0.6429 |
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+ | 0.0014 | 25.47 | 1350 | 1.7618 | 0.6614 | 0.6429 | 0.6294 | 0.6429 |
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+ | 0.0015 | 26.04 | 1380 | 1.7613 | 0.6614 | 0.6429 | 0.6294 | 0.6429 |
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+ | 0.0013 | 26.6 | 1410 | 1.7580 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+ | 0.0014 | 27.17 | 1440 | 1.7621 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+ | 0.0013 | 27.74 | 1470 | 1.7657 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+ | 0.0013 | 28.3 | 1500 | 1.7679 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+ | 0.0013 | 28.87 | 1530 | 1.7679 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+ | 0.0013 | 29.43 | 1560 | 1.7683 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+ | 0.0013 | 30.0 | 1590 | 1.7683 | 0.6720 | 0.6548 | 0.6399 | 0.6548 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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