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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: emotions_distilbert_im
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. -->
# emotions_distilbert_im
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5190
- F1 Micro: 0.6929
- F1 Macro: 0.5934
- Accuracy: 0.2362
## 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- 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 | F1 Micro | F1 Macro | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.7627 | 0.41 | 20 | 0.6619 | 0.5987 | 0.3531 | 0.1599 |
| 0.6258 | 0.82 | 40 | 0.5710 | 0.6609 | 0.4929 | 0.1929 |
| 0.5609 | 1.22 | 60 | 0.5496 | 0.6672 | 0.5097 | 0.2233 |
| 0.504 | 1.63 | 80 | 0.5260 | 0.6765 | 0.5840 | 0.1948 |
| 0.4856 | 2.04 | 100 | 0.5152 | 0.6864 | 0.5912 | 0.1981 |
| 0.4246 | 2.45 | 120 | 0.5190 | 0.6929 | 0.5934 | 0.2362 |
| 0.4157 | 2.86 | 140 | 0.5321 | 0.6757 | 0.5763 | 0.2058 |
| 0.4027 | 3.27 | 160 | 0.5186 | 0.6800 | 0.5914 | 0.2149 |
| 0.346 | 3.67 | 180 | 0.5274 | 0.6725 | 0.5888 | 0.1981 |
| 0.3562 | 4.08 | 200 | 0.5346 | 0.6811 | 0.5884 | 0.2272 |
| 0.3097 | 4.49 | 220 | 0.5413 | 0.6767 | 0.5863 | 0.2136 |
| 0.2989 | 4.9 | 240 | 0.5637 | 0.6832 | 0.5886 | 0.2259 |
| 0.2701 | 5.31 | 260 | 0.5745 | 0.6803 | 0.5911 | 0.2272 |
| 0.2505 | 5.71 | 280 | 0.5946 | 0.6783 | 0.5807 | 0.2155 |
| 0.2508 | 6.12 | 300 | 0.6194 | 0.6822 | 0.5764 | 0.2272 |
| 0.2171 | 6.53 | 320 | 0.6293 | 0.6800 | 0.5790 | 0.2181 |
| 0.2164 | 6.94 | 340 | 0.6322 | 0.6805 | 0.5806 | 0.2097 |
| 0.1949 | 7.35 | 360 | 0.6663 | 0.6775 | 0.5709 | 0.2155 |
| 0.1852 | 7.76 | 380 | 0.6763 | 0.6768 | 0.5749 | 0.2129 |
| 0.1821 | 8.16 | 400 | 0.6757 | 0.6791 | 0.5743 | 0.2227 |
| 0.1653 | 8.57 | 420 | 0.6862 | 0.6757 | 0.5728 | 0.2155 |
| 0.166 | 8.98 | 440 | 0.6989 | 0.6786 | 0.5749 | 0.2233 |
| 0.1593 | 9.39 | 460 | 0.7019 | 0.6784 | 0.5765 | 0.2220 |
| 0.1512 | 9.8 | 480 | 0.7010 | 0.6781 | 0.5744 | 0.2233 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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