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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: levit-192_finetuned_on_unlabelled_IA_with_snorkel_labels
results: []
---
<!-- 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. -->
# levit-192_finetuned_on_unlabelled_IA_with_snorkel_labels
This model is a fine-tuned version of [facebook/levit-192](https://huggingface.co/facebook/levit-192) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.9836
- Recall: 0.9822
- F1: 0.9829
- Accuracy: 0.9873
## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 253 | nan | 0.9743 | 0.9791 | 0.9766 | 0.9826 |
| 0.0557 | 2.0 | 506 | nan | 0.9829 | 0.9801 | 0.9815 | 0.9863 |
| 0.0557 | 3.0 | 759 | nan | 0.9836 | 0.9822 | 0.9829 | 0.9873 |
| 0.0543 | 4.0 | 1012 | nan | 0.9839 | 0.9775 | 0.9807 | 0.9858 |
| 0.0543 | 5.0 | 1265 | nan | 0.9616 | 0.9727 | 0.9670 | 0.9752 |
| 0.0457 | 6.0 | 1518 | nan | 0.9563 | 0.9699 | 0.9629 | 0.9720 |
| 0.0457 | 7.0 | 1771 | nan | 0.9822 | 0.9808 | 0.9815 | 0.9863 |
| 0.0418 | 8.0 | 2024 | nan | 0.9735 | 0.9769 | 0.9752 | 0.9815 |
| 0.0418 | 9.0 | 2277 | nan | 0.9832 | 0.9811 | 0.9822 | 0.9868 |
| 0.0396 | 10.0 | 2530 | nan | 0.9843 | 0.9815 | 0.9829 | 0.9873 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
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