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ModernBERT-large_v3_scratch

Text Classification
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modernbert
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  1. README.md +108 -0
  2. classification_report_test.txt +14 -0
  3. confusion_matrix_test.csv +4 -0
  4. model.safetensors +1 -1
  5. model_predict.csv +0 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: answerdotai/ModernBERT-large
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: ModernBERT-large_v3_scratch
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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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+ # ModernBERT-large_v3_scratch
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+
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+ This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1638
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+ - Accuracy: 0.9008
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+ - Precision Macro: 0.7724
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+ - Recall Macro: 0.7784
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+ - F1 Macro: 0.7752
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+ - F1 Weighted: 0.9013
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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: 0.0001
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 40
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
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+ | 2.1409 | 1.0 | 179 | 0.4797 | 0.8155 | 0.7656 | 0.5858 | 0.5889 | 0.8001 |
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+ | 1.8913 | 2.0 | 358 | 0.4433 | 0.8383 | 0.7709 | 0.6087 | 0.6125 | 0.8239 |
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+ | 1.7772 | 3.0 | 537 | 0.3867 | 0.8629 | 0.7665 | 0.6576 | 0.6777 | 0.8535 |
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+ | 1.3739 | 4.0 | 716 | 0.3396 | 0.8819 | 0.7647 | 0.6833 | 0.7033 | 0.8742 |
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+ | 1.121 | 5.0 | 895 | 0.3194 | 0.8926 | 0.7935 | 0.7307 | 0.7533 | 0.8884 |
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+ | 0.8297 | 6.0 | 1074 | 0.4077 | 0.8800 | 0.8479 | 0.6714 | 0.7001 | 0.8696 |
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+ | 0.7174 | 7.0 | 1253 | 0.4211 | 0.8737 | 0.7463 | 0.7607 | 0.7510 | 0.8748 |
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+ | 0.5598 | 8.0 | 1432 | 0.4373 | 0.8932 | 0.7960 | 0.6906 | 0.7144 | 0.8848 |
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+ | 0.4317 | 9.0 | 1611 | 0.5494 | 0.8711 | 0.7343 | 0.7678 | 0.7460 | 0.8748 |
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+ | 0.3809 | 10.0 | 1790 | 0.4896 | 0.8920 | 0.7838 | 0.7139 | 0.7367 | 0.8865 |
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+ | 0.2739 | 11.0 | 1969 | 0.6534 | 0.8888 | 0.7627 | 0.7727 | 0.7671 | 0.8896 |
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+ | 0.1934 | 12.0 | 2148 | 0.5885 | 0.9008 | 0.8028 | 0.7404 | 0.7633 | 0.8968 |
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+ | 0.1742 | 13.0 | 2327 | 0.7146 | 0.8825 | 0.8056 | 0.7260 | 0.7535 | 0.8781 |
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+ | 0.0825 | 14.0 | 2506 | 0.8700 | 0.8970 | 0.7733 | 0.7348 | 0.7497 | 0.8938 |
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+ | 0.0688 | 15.0 | 2685 | 0.8066 | 0.8939 | 0.7636 | 0.7315 | 0.7448 | 0.8910 |
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+ | 0.0796 | 16.0 | 2864 | 0.8853 | 0.8970 | 0.8123 | 0.7289 | 0.7564 | 0.8920 |
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+ | 0.1044 | 17.0 | 3043 | 0.8411 | 0.8913 | 0.7614 | 0.7502 | 0.7554 | 0.8904 |
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+ | 0.0893 | 18.0 | 3222 | 0.8432 | 0.8983 | 0.7941 | 0.7347 | 0.7564 | 0.8942 |
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+ | 0.0274 | 19.0 | 3401 | 0.9003 | 0.8926 | 0.7772 | 0.7345 | 0.7515 | 0.8894 |
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+ | 0.0161 | 20.0 | 3580 | 1.0964 | 0.8907 | 0.7648 | 0.7677 | 0.7659 | 0.8909 |
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+ | 0.0066 | 21.0 | 3759 | 0.9782 | 0.8958 | 0.7639 | 0.7616 | 0.7627 | 0.8956 |
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+ | 0.027 | 22.0 | 3938 | 1.0439 | 0.8913 | 0.7557 | 0.7800 | 0.7663 | 0.8935 |
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+ | 0.0569 | 23.0 | 4117 | 0.9039 | 0.9033 | 0.8002 | 0.7709 | 0.7838 | 0.9016 |
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+ | 0.0126 | 24.0 | 4296 | 0.9952 | 0.9002 | 0.7845 | 0.7529 | 0.7663 | 0.8979 |
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+ | 0.0047 | 25.0 | 4475 | 0.9702 | 0.9052 | 0.7872 | 0.7849 | 0.7860 | 0.9051 |
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+ | 0.0091 | 26.0 | 4654 | 1.0793 | 0.8970 | 0.7821 | 0.7575 | 0.7682 | 0.8953 |
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+ | 0.0038 | 27.0 | 4833 | 1.0187 | 0.9027 | 0.7781 | 0.7714 | 0.7745 | 0.9022 |
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+ | 0.0028 | 28.0 | 5012 | 1.0220 | 0.9015 | 0.7739 | 0.7746 | 0.7742 | 0.9015 |
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+ | 0.0025 | 29.0 | 5191 | 1.0514 | 0.9015 | 0.7757 | 0.7746 | 0.7751 | 0.9014 |
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+ | 0.0002 | 30.0 | 5370 | 1.0703 | 0.9027 | 0.7771 | 0.7796 | 0.7783 | 0.9029 |
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+ | 0.0138 | 31.0 | 5549 | 1.0361 | 0.9021 | 0.7767 | 0.7790 | 0.7778 | 0.9023 |
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+ | 0.0017 | 32.0 | 5728 | 1.0631 | 0.9027 | 0.7777 | 0.7836 | 0.7806 | 0.9032 |
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+ | 0.0015 | 33.0 | 5907 | 1.0906 | 0.9008 | 0.7708 | 0.7782 | 0.7743 | 0.9014 |
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+ | 0.0111 | 34.0 | 6086 | 1.1079 | 0.9002 | 0.7703 | 0.7778 | 0.7739 | 0.9008 |
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+ | 0.0001 | 35.0 | 6265 | 1.1265 | 0.8996 | 0.7698 | 0.7774 | 0.7735 | 0.9002 |
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+ | 0.0012 | 36.0 | 6444 | 1.1395 | 0.9008 | 0.7707 | 0.7783 | 0.7743 | 0.9014 |
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+ | 0.0001 | 37.0 | 6623 | 1.1534 | 0.9015 | 0.7728 | 0.7788 | 0.7757 | 0.9019 |
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+ | 0.0001 | 38.0 | 6802 | 1.1619 | 0.9008 | 0.7724 | 0.7784 | 0.7752 | 0.9013 |
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+ | 0.0001 | 39.0 | 6981 | 1.1634 | 0.9015 | 0.7728 | 0.7788 | 0.7757 | 0.9019 |
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+ | 0.0007 | 40.0 | 7160 | 1.1638 | 0.9008 | 0.7724 | 0.7784 | 0.7752 | 0.9013 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.55.0
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+ - Pytorch 2.7.0+cu126
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+ - Datasets 4.0.0
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+ - Tokenizers 0.21.4
classification_report_test.txt ADDED
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+ precision recall f1-score support
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+
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+ negative 0.88 0.91 0.90 1409
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+ neutral 0.37 0.32 0.34 167
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+ positive 0.92 0.90 0.91 1590
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+
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+ accuracy 0.87 3166
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+ macro avg 0.72 0.71 0.72 3166
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+ weighted avg 0.87 0.87 0.87 3166
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+
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+ Confusion matrix:
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+ [[1287 44 78]
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+ [ 60 54 53]
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+ [ 115 49 1426]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1287,44,78
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+ neutral,60,54,53
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+ positive,115,49,1426
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model_predict.csv ADDED
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