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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
base_model: bert-base-uncased
model-index:
- name: final-lr2e-5-bs16-fp16-2
results: []
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# final-lr2e-5-bs16-fp16-2
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.
It achieves the following results on the evaluation set:
- Loss: 0.4823
- F1 Macro: 0.8301
- F1 Weighted: 0.8772
- F1: 0.7388
- Accuracy: 0.8792
- Confusion Matrix: [[2834 196]
[ 287 683]]
- Confusion Matrix Norm: [[0.93531353 0.06468647]
[0.29587629 0.70412371]]
- Classification Report: precision recall f1-score support
0 0.908042 0.935314 0.921476 3030.00000
1 0.777019 0.704124 0.738778 970.00000
accuracy 0.879250 0.879250 0.879250 0.87925
macro avg 0.842531 0.819719 0.830127 4000.00000
weighted avg 0.876269 0.879250 0.877172 4000.00000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.3333 | 1.0 | 1000 | 0.3064 | 0.8165 | 0.8672 | 0.7181 | 0.8692 | [[2811 219]
[ 304 666]] | [[0.92772277 0.07227723]
[0.31340206 0.68659794]] | precision recall f1-score support
0 0.902408 0.927723 0.914890 3030.00000
1 0.752542 0.686598 0.718059 970.00000
accuracy 0.869250 0.869250 0.869250 0.86925
macro avg 0.827475 0.807160 0.816475 4000.00000
weighted avg 0.866065 0.869250 0.867159 4000.00000 |
| 0.2271 | 2.0 | 2000 | 0.3905 | 0.8238 | 0.8708 | 0.7326 | 0.871 | [[2777 253]
[ 263 707]] | [[0.91650165 0.08349835]
[0.27113402 0.72886598]] | precision recall f1-score support
0 0.913487 0.916502 0.914992 3030.000
1 0.736458 0.728866 0.732642 970.000
accuracy 0.871000 0.871000 0.871000 0.871
macro avg 0.824973 0.822684 0.823817 4000.000
weighted avg 0.870557 0.871000 0.870772 4000.000 |
| 0.1435 | 3.0 | 3000 | 0.4823 | 0.8301 | 0.8772 | 0.7388 | 0.8792 | [[2834 196]
[ 287 683]] | [[0.93531353 0.06468647]
[0.29587629 0.70412371]] | precision recall f1-score support
0 0.908042 0.935314 0.921476 3030.00000
1 0.777019 0.704124 0.738778 970.00000
accuracy 0.879250 0.879250 0.879250 0.87925
macro avg 0.842531 0.819719 0.830127 4000.00000
weighted avg 0.876269 0.879250 0.877172 4000.00000 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2 |