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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: convnext-tiny-224_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. -->
# convnext-tiny-224_finetuned_on_unlabelled_IA_with_snorkel_labels
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4381
- Precision: 0.8239
- Recall: 0.7919
- F1: 0.8058
- Accuracy: 0.8629
## 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: 0.001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 112 | 0.5589 | 0.7547 | 0.5380 | 0.5097 | 0.7679 |
| No log | 2.0 | 224 | 0.5578 | 0.7691 | 0.5387 | 0.5103 | 0.7690 |
| No log | 3.0 | 336 | 0.4812 | 0.8513 | 0.7371 | 0.7709 | 0.8555 |
| No log | 4.0 | 448 | 0.4387 | 0.8734 | 0.6539 | 0.6835 | 0.8259 |
| 0.482 | 5.0 | 560 | 0.4427 | 0.8322 | 0.6250 | 0.6449 | 0.8085 |
| 0.482 | 6.0 | 672 | 0.6234 | 0.8219 | 0.5702 | 0.5635 | 0.7848 |
| 0.482 | 7.0 | 784 | 0.6187 | 0.8791 | 0.6070 | 0.6196 | 0.8054 |
| 0.482 | 8.0 | 896 | 0.3953 | 0.8683 | 0.7134 | 0.7507 | 0.8502 |
| 0.3656 | 9.0 | 1008 | 0.4381 | 0.8239 | 0.7919 | 0.8058 | 0.8629 |
| 0.3656 | 10.0 | 1120 | 0.5346 | 0.7794 | 0.7900 | 0.7844 | 0.8370 |
| 0.3656 | 11.0 | 1232 | 0.3685 | 0.8678 | 0.7600 | 0.7943 | 0.8681 |
| 0.3656 | 12.0 | 1344 | 0.6900 | 0.6244 | 0.6667 | 0.6099 | 0.6435 |
| 0.3656 | 13.0 | 1456 | 0.6097 | 0.6832 | 0.7149 | 0.6931 | 0.7511 |
| 0.2987 | 14.0 | 1568 | 0.5435 | 0.8746 | 0.6754 | 0.7096 | 0.8354 |
| 0.2987 | 15.0 | 1680 | 0.5525 | 0.7277 | 0.7690 | 0.7411 | 0.7890 |
| 0.2987 | 16.0 | 1792 | 0.5003 | 0.8086 | 0.7694 | 0.7856 | 0.8507 |
| 0.2987 | 17.0 | 1904 | 0.8172 | 0.6183 | 0.6576 | 0.6074 | 0.6450 |
| 0.2598 | 18.0 | 2016 | 0.6102 | 0.6977 | 0.7489 | 0.7070 | 0.75 |
| 0.2598 | 19.0 | 2128 | 0.4260 | 0.8523 | 0.7497 | 0.7822 | 0.8602 |
| 0.2598 | 20.0 | 2240 | 0.5503 | 0.8276 | 0.6770 | 0.7079 | 0.8281 |
| 0.2598 | 21.0 | 2352 | 0.4574 | 0.7994 | 0.7785 | 0.7879 | 0.8481 |
| 0.2598 | 22.0 | 2464 | 0.6307 | 0.8620 | 0.6353 | 0.6592 | 0.8165 |
| 0.2111 | 23.0 | 2576 | 0.4605 | 0.8196 | 0.7697 | 0.7894 | 0.8555 |
| 0.2111 | 24.0 | 2688 | 0.5290 | 0.8152 | 0.7320 | 0.7592 | 0.8434 |
| 0.2111 | 25.0 | 2800 | 0.4754 | 0.8755 | 0.7216 | 0.7599 | 0.8550 |
| 0.2111 | 26.0 | 2912 | 0.5161 | 0.8428 | 0.7436 | 0.7750 | 0.8555 |
| 0.1638 | 27.0 | 3024 | 0.5753 | 0.7358 | 0.7278 | 0.7316 | 0.8043 |
| 0.1638 | 28.0 | 3136 | 0.6403 | 0.8468 | 0.7016 | 0.7360 | 0.8412 |
| 0.1638 | 29.0 | 3248 | 0.5418 | 0.7912 | 0.7473 | 0.7647 | 0.8381 |
| 0.1638 | 30.0 | 3360 | 0.5651 | 0.8240 | 0.7315 | 0.7607 | 0.8460 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.0
- Tokenizers 0.13.1
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