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--- |
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license: apache-2.0 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: uwb_atcc |
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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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# uwb_atcc |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6191 |
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- Accuracy: 0.9103 |
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- Precision: 0.9239 |
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- Recall: 0.9161 |
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- F1: 0.9200 |
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- Report: precision recall f1-score support |
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0 0.89 0.90 0.90 463 |
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1 0.92 0.92 0.92 596 |
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accuracy 0.91 1059 |
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macro avg 0.91 0.91 0.91 1059 |
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weighted avg 0.91 0.91 0.91 1059 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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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- training_steps: 3000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| No log | 3.36 | 500 | 0.2346 | 0.9207 | 0.9197 | 0.9413 | 0.9303 | precision recall f1-score support |
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0 0.92 0.89 0.91 463 |
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1 0.92 0.94 0.93 596 |
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accuracy 0.92 1059 |
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macro avg 0.92 0.92 0.92 1059 |
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weighted avg 0.92 0.92 0.92 1059 |
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| 0.2212 | 6.71 | 1000 | 0.3161 | 0.9046 | 0.9260 | 0.9027 | 0.9142 | precision recall f1-score support |
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0 0.88 0.91 0.89 463 |
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1 0.93 0.90 0.91 596 |
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accuracy 0.90 1059 |
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macro avg 0.90 0.90 0.90 1059 |
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weighted avg 0.91 0.90 0.90 1059 |
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| 0.2212 | 10.07 | 1500 | 0.4337 | 0.9065 | 0.9191 | 0.9144 | 0.9167 | precision recall f1-score support |
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0 0.89 0.90 0.89 463 |
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1 0.92 0.91 0.92 596 |
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accuracy 0.91 1059 |
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macro avg 0.90 0.91 0.91 1059 |
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weighted avg 0.91 0.91 0.91 1059 |
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| 0.0651 | 13.42 | 2000 | 0.4743 | 0.9178 | 0.9249 | 0.9295 | 0.9272 | precision recall f1-score support |
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0 0.91 0.90 0.91 463 |
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1 0.92 0.93 0.93 596 |
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accuracy 0.92 1059 |
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macro avg 0.92 0.92 0.92 1059 |
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weighted avg 0.92 0.92 0.92 1059 |
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| 0.0651 | 16.78 | 2500 | 0.5538 | 0.9103 | 0.9196 | 0.9211 | 0.9204 | precision recall f1-score support |
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0 0.90 0.90 0.90 463 |
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1 0.92 0.92 0.92 596 |
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accuracy 0.91 1059 |
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macro avg 0.91 0.91 0.91 1059 |
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weighted avg 0.91 0.91 0.91 1059 |
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| 0.0296 | 20.13 | 3000 | 0.6191 | 0.9103 | 0.9239 | 0.9161 | 0.9200 | precision recall f1-score support |
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0 0.89 0.90 0.90 463 |
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1 0.92 0.92 0.92 596 |
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accuracy 0.91 1059 |
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macro avg 0.91 0.91 0.91 1059 |
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weighted avg 0.91 0.91 0.91 1059 |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.0 |
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- Tokenizers 0.13.2 |
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