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bert-finetuned-sem_eval-english

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yLachin/BERT_TEST

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  1. README.md +106 -0
  2. config.json +43 -0
  3. model.safetensors +3 -0
  4. runs/Jan01_09-25-59_1513f7f513f2/events.out.tfevents.1704101169.1513f7f513f2.2438.0 +3 -0
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  6. runs/Jan01_09-56-29_1513f7f513f2/events.out.tfevents.1704103001.1513f7f513f2.2438.2 +3 -0
  7. runs/Jan01_10-07-46_1513f7f513f2/events.out.tfevents.1704103680.1513f7f513f2.2438.3 +3 -0
  8. runs/Jan01_10-07-46_1513f7f513f2/events.out.tfevents.1704105527.1513f7f513f2.2438.4 +3 -0
  9. special_tokens_map.json +7 -0
  10. tokenizer.json +0 -0
  11. tokenizer_config.json +55 -0
  12. training_args.bin +3 -0
  13. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: bert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: bert-finetuned-sem_eval-english
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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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+ # bert-finetuned-sem_eval-english
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.5200
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+ - F1: 0.4973
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+ - Roc Auc: 0.6802
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+ - Accuracy: 0.3210
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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: 5e-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 | F1 | Roc Auc | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.4307 | 0.68 | 500 | 0.4058 | 0.5119 | 0.6801 | 0.3991 |
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+ | 0.3923 | 1.35 | 1000 | 0.4189 | 0.5473 | 0.7017 | 0.4203 |
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+ | 0.3699 | 2.03 | 1500 | 0.4344 | 0.4529 | 0.6505 | 0.3375 |
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+ | 0.2839 | 2.7 | 2000 | 0.4574 | 0.4345 | 0.6402 | 0.3415 |
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+ | 0.2339 | 3.38 | 2500 | 0.5325 | 0.5061 | 0.6804 | 0.3604 |
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+ | 0.1835 | 4.05 | 3000 | 0.5906 | 0.5145 | 0.6889 | 0.3368 |
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+ | 0.121 | 4.73 | 3500 | 0.6763 | 0.4772 | 0.6658 | 0.3178 |
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+ | 0.0916 | 5.41 | 4000 | 0.7520 | 0.5172 | 0.6919 | 0.3438 |
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+ | 0.0718 | 6.08 | 4500 | 0.8309 | 0.5115 | 0.6903 | 0.3170 |
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+ | 0.0519 | 6.76 | 5000 | 0.8800 | 0.4762 | 0.6652 | 0.3107 |
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+ | 0.0396 | 7.43 | 5500 | 0.9691 | 0.4753 | 0.6683 | 0.2555 |
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+ | 0.0319 | 8.11 | 6000 | 1.0172 | 0.4945 | 0.6787 | 0.3147 |
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+ | 0.0289 | 8.78 | 6500 | 1.0847 | 0.4983 | 0.6815 | 0.3186 |
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+ | 0.026 | 9.46 | 7000 | 1.1104 | 0.4889 | 0.6733 | 0.3352 |
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+ | 0.0186 | 10.14 | 7500 | 1.1639 | 0.4787 | 0.6682 | 0.3123 |
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+ | 0.0207 | 10.81 | 8000 | 1.1984 | 0.4715 | 0.6645 | 0.2981 |
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+ | 0.0151 | 11.49 | 8500 | 1.1868 | 0.4792 | 0.6681 | 0.3068 |
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+ | 0.0136 | 12.16 | 9000 | 1.2526 | 0.5127 | 0.6906 | 0.3178 |
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+ | 0.0131 | 12.84 | 9500 | 1.2530 | 0.4893 | 0.6736 | 0.3162 |
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+ | 0.0124 | 13.51 | 10000 | 1.2486 | 0.4978 | 0.6796 | 0.3139 |
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+ | 0.0106 | 14.19 | 10500 | 1.3166 | 0.5045 | 0.6831 | 0.3352 |
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+ | 0.01 | 14.86 | 11000 | 1.3071 | 0.4876 | 0.6734 | 0.3155 |
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+ | 0.0091 | 15.54 | 11500 | 1.3456 | 0.4716 | 0.6637 | 0.3060 |
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+ | 0.0075 | 16.22 | 12000 | 1.3318 | 0.4940 | 0.6762 | 0.3297 |
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+ | 0.0085 | 16.89 | 12500 | 1.4009 | 0.4912 | 0.6770 | 0.3052 |
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+ | 0.0079 | 17.57 | 13000 | 1.3524 | 0.4906 | 0.6740 | 0.3328 |
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+ | 0.0055 | 18.24 | 13500 | 1.4259 | 0.4761 | 0.6668 | 0.3044 |
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+ | 0.0057 | 18.92 | 14000 | 1.4156 | 0.4832 | 0.6724 | 0.2997 |
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+ | 0.0034 | 19.59 | 14500 | 1.4554 | 0.4779 | 0.6674 | 0.3139 |
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+ | 0.0048 | 20.27 | 15000 | 1.4369 | 0.4729 | 0.6641 | 0.3044 |
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+ | 0.0039 | 20.95 | 15500 | 1.4665 | 0.4914 | 0.6775 | 0.3021 |
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+ | 0.0033 | 21.62 | 16000 | 1.4586 | 0.4875 | 0.6734 | 0.3131 |
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+ | 0.0024 | 22.3 | 16500 | 1.4830 | 0.4884 | 0.6742 | 0.3155 |
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+ | 0.0034 | 22.97 | 17000 | 1.4911 | 0.4932 | 0.6771 | 0.3210 |
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+ | 0.003 | 23.65 | 17500 | 1.4832 | 0.5045 | 0.6848 | 0.3226 |
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+ | 0.002 | 24.32 | 18000 | 1.4772 | 0.4679 | 0.6613 | 0.3028 |
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+ | 0.0028 | 25.0 | 18500 | 1.4742 | 0.5071 | 0.6858 | 0.3368 |
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+ | 0.0018 | 25.68 | 19000 | 1.4784 | 0.4972 | 0.6789 | 0.3328 |
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+ | 0.0022 | 26.35 | 19500 | 1.4965 | 0.4997 | 0.6808 | 0.3328 |
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+ | 0.0021 | 27.03 | 20000 | 1.5143 | 0.4858 | 0.6723 | 0.3194 |
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+ | 0.0016 | 27.7 | 20500 | 1.5262 | 0.4868 | 0.6731 | 0.3186 |
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+ | 0.0021 | 28.38 | 21000 | 1.5200 | 0.4973 | 0.6802 | 0.3210 |
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+ | 0.0015 | 29.05 | 21500 | 1.5159 | 0.4862 | 0.6722 | 0.3186 |
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+ | 0.0017 | 29.73 | 22000 | 1.5102 | 0.4876 | 0.6727 | 0.3249 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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+ "architectures": [
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+ "BertForSequenceClassification"
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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