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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: emotions_bert
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_bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5151
- F1 Micro: 0.6887
- F1 Macro: 0.6024
- Accuracy: 0.1929
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.7549 | 0.4082 | 20 | 0.6455 | 0.6125 | 0.4264 | 0.1243 |
| 0.6144 | 0.8163 | 40 | 0.5675 | 0.6510 | 0.5188 | 0.1670 |
| 0.5496 | 1.2245 | 60 | 0.5414 | 0.6747 | 0.5570 | 0.1883 |
| 0.4878 | 1.6327 | 80 | 0.5191 | 0.6849 | 0.5894 | 0.2104 |
| 0.4754 | 2.0408 | 100 | 0.5140 | 0.6810 | 0.5909 | 0.2013 |
| 0.4027 | 2.4490 | 120 | 0.5169 | 0.6849 | 0.5880 | 0.2207 |
| 0.3986 | 2.8571 | 140 | 0.5151 | 0.6887 | 0.6024 | 0.1929 |
| 0.3711 | 3.2653 | 160 | 0.5187 | 0.6820 | 0.5991 | 0.2188 |
| 0.325 | 3.6735 | 180 | 0.5263 | 0.6753 | 0.5928 | 0.1942 |
| 0.3303 | 4.0816 | 200 | 0.5294 | 0.6900 | 0.5949 | 0.2149 |
| 0.2801 | 4.4898 | 220 | 0.5420 | 0.6840 | 0.5953 | 0.2097 |
| 0.2748 | 4.8980 | 240 | 0.5583 | 0.6797 | 0.5861 | 0.2162 |
| 0.2452 | 5.3061 | 260 | 0.5781 | 0.6758 | 0.5871 | 0.1981 |
| 0.2253 | 5.7143 | 280 | 0.5889 | 0.6715 | 0.5812 | 0.1929 |
| 0.226 | 6.1224 | 300 | 0.5955 | 0.6793 | 0.5852 | 0.2207 |
| 0.1958 | 6.5306 | 320 | 0.6120 | 0.6734 | 0.5861 | 0.2032 |
| 0.1952 | 6.9388 | 340 | 0.6209 | 0.6744 | 0.5806 | 0.2084 |
| 0.1758 | 7.3469 | 360 | 0.6339 | 0.6756 | 0.5789 | 0.2136 |
| 0.1691 | 7.7551 | 380 | 0.6412 | 0.6773 | 0.5779 | 0.2188 |
| 0.1613 | 8.1633 | 400 | 0.6431 | 0.6761 | 0.5794 | 0.2142 |
| 0.1486 | 8.5714 | 420 | 0.6532 | 0.6718 | 0.5763 | 0.2104 |
| 0.1529 | 8.9796 | 440 | 0.6577 | 0.6737 | 0.5747 | 0.2136 |
| 0.1436 | 9.3878 | 460 | 0.6658 | 0.6734 | 0.5744 | 0.2194 |
| 0.1399 | 9.7959 | 480 | 0.6640 | 0.6735 | 0.5745 | 0.2188 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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