---
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