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--- |
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license: apache-2.0 |
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base_model: ltg/norbert3-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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: norbert3-large-user-needs-v2 |
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results: [] |
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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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# norbert3-large-user-needs-v2 |
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This model is a fine-tuned version of [ltg/norbert3-large](https://huggingface.co/ltg/norbert3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1392 |
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- Accuracy: 0.7067 |
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- F1: 0.6946 |
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- Precision: 0.6905 |
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- Recall: 0.7067 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 4 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 375 | 0.8059 | 0.6747 | 0.6472 | 0.6569 | 0.6747 | |
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| 0.9129 | 2.0 | 750 | 0.9030 | 0.6453 | 0.6142 | 0.5975 | 0.6453 | |
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| 0.7636 | 3.0 | 1125 | 0.7755 | 0.6667 | 0.6292 | 0.6250 | 0.6667 | |
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| 0.6003 | 4.0 | 1500 | 1.0267 | 0.6773 | 0.6591 | 0.6928 | 0.6773 | |
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| 0.6003 | 5.0 | 1875 | 1.9897 | 0.6267 | 0.6378 | 0.6526 | 0.6267 | |
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| 0.2905 | 6.0 | 2250 | 2.0507 | 0.704 | 0.6913 | 0.6879 | 0.704 | |
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| 0.0901 | 7.0 | 2625 | 2.7638 | 0.6853 | 0.6590 | 0.6863 | 0.6853 | |
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| 0.0365 | 8.0 | 3000 | 2.6138 | 0.696 | 0.6875 | 0.6907 | 0.696 | |
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| 0.0365 | 9.0 | 3375 | 3.0024 | 0.6667 | 0.6585 | 0.6543 | 0.6667 | |
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| 0.0162 | 10.0 | 3750 | 2.9416 | 0.6933 | 0.6829 | 0.6798 | 0.6933 | |
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| 0.0022 | 11.0 | 4125 | 3.2015 | 0.6827 | 0.6558 | 0.6790 | 0.6827 | |
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| 0.0114 | 12.0 | 4500 | 3.3133 | 0.6933 | 0.6694 | 0.6916 | 0.6933 | |
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| 0.0114 | 13.0 | 4875 | 3.2376 | 0.6773 | 0.6695 | 0.6647 | 0.6773 | |
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| 0.0042 | 14.0 | 5250 | 3.1392 | 0.7067 | 0.6946 | 0.6905 | 0.7067 | |
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| 0.0035 | 15.0 | 5625 | 3.2710 | 0.6907 | 0.6770 | 0.6705 | 0.6907 | |
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| 0.0045 | 16.0 | 6000 | 3.3476 | 0.6933 | 0.6847 | 0.6841 | 0.6933 | |
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| 0.0045 | 17.0 | 6375 | 3.2386 | 0.696 | 0.6904 | 0.6932 | 0.696 | |
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| 0.0065 | 18.0 | 6750 | 3.4263 | 0.6853 | 0.6700 | 0.6607 | 0.6853 | |
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| 0.0029 | 19.0 | 7125 | 3.4898 | 0.6827 | 0.6652 | 0.6579 | 0.6827 | |
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| 0.0013 | 20.0 | 7500 | 3.5103 | 0.68 | 0.6624 | 0.6554 | 0.68 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.1.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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