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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filter_sort
metrics:
- f1
- accuracy
model-index:
- name: favs-filtersort-multilabel-classification-bert-base-cased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: filter_sort
type: filter_sort
config: default
split: train
args: default
metrics:
- name: F1
type: f1
value: 0.767123287671233
- name: Accuracy
type: accuracy
value: 0.3
---
<!-- 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. -->
# favs-filtersort-multilabel-classification-bert-base-cased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_sort dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2847
- F1: 0.7671
- Roc Auc: 0.8361
- Accuracy: 0.3
## 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: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.7037 | 1.0 | 12 | 0.6056 | 0.5714 | 0.7341 | 0.0 |
| 0.5962 | 2.0 | 24 | 0.5212 | 0.5067 | 0.6787 | 0.0 |
| 0.5393 | 3.0 | 36 | 0.4396 | 0.6197 | 0.7439 | 0.1 |
| 0.4682 | 4.0 | 48 | 0.3963 | 0.5714 | 0.7079 | 0.0 |
| 0.409 | 5.0 | 60 | 0.3710 | 0.6061 | 0.7297 | 0.0 |
| 0.3923 | 6.0 | 72 | 0.3571 | 0.6286 | 0.7477 | 0.1 |
| 0.3682 | 7.0 | 84 | 0.3439 | 0.6849 | 0.7862 | 0.2 |
| 0.367 | 8.0 | 96 | 0.3286 | 0.6479 | 0.7605 | 0.1 |
| 0.3633 | 9.0 | 108 | 0.3194 | 0.6761 | 0.7772 | 0.2 |
| 0.3359 | 10.0 | 120 | 0.3145 | 0.6761 | 0.7772 | 0.2 |
| 0.3327 | 11.0 | 132 | 0.3054 | 0.6957 | 0.7848 | 0.2 |
| 0.3206 | 12.0 | 144 | 0.2998 | 0.7297 | 0.8156 | 0.2 |
| 0.3125 | 13.0 | 156 | 0.2926 | 0.7222 | 0.8066 | 0.2 |
| 0.3073 | 14.0 | 168 | 0.2847 | 0.7671 | 0.8361 | 0.3 |
| 0.2972 | 15.0 | 180 | 0.2819 | 0.7606 | 0.8271 | 0.3 |
| 0.2933 | 16.0 | 192 | 0.2773 | 0.7606 | 0.8271 | 0.3 |
| 0.2798 | 17.0 | 204 | 0.2752 | 0.7606 | 0.8271 | 0.3 |
| 0.2737 | 18.0 | 216 | 0.2731 | 0.7606 | 0.8271 | 0.3 |
| 0.2866 | 19.0 | 228 | 0.2720 | 0.7606 | 0.8271 | 0.3 |
| 0.2729 | 20.0 | 240 | 0.2713 | 0.7606 | 0.8271 | 0.3 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
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