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modernBert-base_v2

Text Classification
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Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
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  1. README.md +87 -0
  2. classification_report_test.txt +14 -0
  3. confusion_matrix_test.csv +4 -0
  4. model.safetensors +1 -1
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-base
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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-base_v2
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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-base_v2
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+
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+ This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7185
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+ - Accuracy: 0.9116
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+ - Precision Macro: 0.8041
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+ - Recall Macro: 0.7362
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+ - F1 Macro: 0.7592
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+ - F1 Weighted: 0.9065
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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: 3e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 128
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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: linear
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+ - num_epochs: 20
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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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+ | 1.2139 | 1.0 | 90 | 0.5024 | 0.8073 | 0.8182 | 0.5993 | 0.6061 | 0.7934 |
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+ | 0.6774 | 2.0 | 180 | 0.2870 | 0.9033 | 0.8421 | 0.7140 | 0.7451 | 0.8960 |
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+ | 0.4571 | 3.0 | 270 | 0.3474 | 0.8920 | 0.8074 | 0.6669 | 0.6824 | 0.8802 |
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+ | 0.2925 | 4.0 | 360 | 0.3089 | 0.9065 | 0.8778 | 0.7074 | 0.7413 | 0.8977 |
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+ | 0.1725 | 5.0 | 450 | 0.3611 | 0.8958 | 0.7729 | 0.7574 | 0.7646 | 0.8946 |
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+ | 0.0977 | 6.0 | 540 | 0.4743 | 0.9090 | 0.8405 | 0.7388 | 0.7695 | 0.9036 |
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+ | 0.0576 | 7.0 | 630 | 0.6044 | 0.8743 | 0.7234 | 0.8019 | 0.7413 | 0.8878 |
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+ | 0.0338 | 8.0 | 720 | 0.6118 | 0.9040 | 0.7756 | 0.7506 | 0.7615 | 0.9019 |
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+ | 0.016 | 9.0 | 810 | 0.6754 | 0.9071 | 0.8334 | 0.7379 | 0.7670 | 0.9019 |
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+ | 0.0113 | 10.0 | 900 | 0.6732 | 0.9065 | 0.7898 | 0.7606 | 0.7733 | 0.9044 |
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+ | 0.0065 | 11.0 | 990 | 0.7871 | 0.9046 | 0.8046 | 0.7277 | 0.7519 | 0.8992 |
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+ | 0.0037 | 12.0 | 1080 | 0.7134 | 0.9109 | 0.7989 | 0.7147 | 0.7386 | 0.9038 |
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+ | 0.0022 | 13.0 | 1170 | 0.7784 | 0.9015 | 0.7765 | 0.7383 | 0.7529 | 0.8982 |
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+ | 0.0013 | 14.0 | 1260 | 0.7176 | 0.9109 | 0.7832 | 0.7486 | 0.7625 | 0.9079 |
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+ | 0.0011 | 15.0 | 1350 | 0.7681 | 0.9059 | 0.7920 | 0.7371 | 0.7565 | 0.9017 |
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+ | 0.0001 | 16.0 | 1440 | 0.7170 | 0.9071 | 0.7833 | 0.7282 | 0.7479 | 0.9024 |
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+ | 0.0007 | 17.0 | 1530 | 0.7219 | 0.9109 | 0.8022 | 0.7442 | 0.7652 | 0.9068 |
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+ | 0.0003 | 18.0 | 1620 | 0.7379 | 0.9103 | 0.7950 | 0.7398 | 0.7596 | 0.9060 |
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+ | 0.0006 | 19.0 | 1710 | 0.7198 | 0.9116 | 0.8074 | 0.7404 | 0.7635 | 0.9068 |
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+ | 0.0004 | 20.0 | 1800 | 0.7185 | 0.9116 | 0.8041 | 0.7362 | 0.7592 | 0.9065 |
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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 0.91 0.89 0.90 1409
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+ neutral 0.43 0.30 0.35 167
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+ positive 0.89 0.93 0.91 1590
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+
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+ accuracy 0.88 3166
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+ macro avg 0.74 0.71 0.72 3166
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+ weighted avg 0.87 0.88 0.88 3166
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+
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+ Confusion matrix:
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+ [[1259 36 114]
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+ [ 45 50 72]
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+ [ 84 29 1477]]
confusion_matrix_test.csv ADDED
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+ ,negative,neutral,positive
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+ negative,1259,36,114
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+ neutral,45,50,72
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+ positive,84,29,1477
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