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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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- f1 |
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- accuracy |
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base_model: bert-base-uncased |
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model-index: |
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- name: final-lr2e-5-bs16-fp16-2 |
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results: [] |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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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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# final-lr2e-5-bs16-fp16-2 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an https://github.com/rewire-online/edos dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4823 |
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- F1 Macro: 0.8301 |
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- F1 Weighted: 0.8772 |
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- F1: 0.7388 |
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- Accuracy: 0.8792 |
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- Confusion Matrix: [[2834 196] |
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[ 287 683]] |
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- Confusion Matrix Norm: [[0.93531353 0.06468647] |
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[0.29587629 0.70412371]] |
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- Classification Report: precision recall f1-score support |
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0 0.908042 0.935314 0.921476 3030.00000 |
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1 0.777019 0.704124 0.738778 970.00000 |
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accuracy 0.879250 0.879250 0.879250 0.87925 |
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macro avg 0.842531 0.819719 0.830127 4000.00000 |
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weighted avg 0.876269 0.879250 0.877172 4000.00000 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 12345 |
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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: 3.0 |
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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 | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.3333 | 1.0 | 1000 | 0.3064 | 0.8165 | 0.8672 | 0.7181 | 0.8692 | [[2811 219] |
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[ 304 666]] | [[0.92772277 0.07227723] |
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[0.31340206 0.68659794]] | precision recall f1-score support |
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0 0.902408 0.927723 0.914890 3030.00000 |
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1 0.752542 0.686598 0.718059 970.00000 |
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accuracy 0.869250 0.869250 0.869250 0.86925 |
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macro avg 0.827475 0.807160 0.816475 4000.00000 |
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weighted avg 0.866065 0.869250 0.867159 4000.00000 | |
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| 0.2271 | 2.0 | 2000 | 0.3905 | 0.8238 | 0.8708 | 0.7326 | 0.871 | [[2777 253] |
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[ 263 707]] | [[0.91650165 0.08349835] |
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[0.27113402 0.72886598]] | precision recall f1-score support |
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0 0.913487 0.916502 0.914992 3030.000 |
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1 0.736458 0.728866 0.732642 970.000 |
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accuracy 0.871000 0.871000 0.871000 0.871 |
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macro avg 0.824973 0.822684 0.823817 4000.000 |
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weighted avg 0.870557 0.871000 0.870772 4000.000 | |
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| 0.1435 | 3.0 | 3000 | 0.4823 | 0.8301 | 0.8772 | 0.7388 | 0.8792 | [[2834 196] |
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[ 287 683]] | [[0.93531353 0.06468647] |
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[0.29587629 0.70412371]] | precision recall f1-score support |
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0 0.908042 0.935314 0.921476 3030.00000 |
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1 0.777019 0.704124 0.738778 970.00000 |
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accuracy 0.879250 0.879250 0.879250 0.87925 |
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macro avg 0.842531 0.819719 0.830127 4000.00000 |
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weighted avg 0.876269 0.879250 0.877172 4000.00000 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
