---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: emotions_distilroberta
  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_distilroberta

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co./distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5345
- F1 Micro: 0.6750
- F1 Macro: 0.5924
- Accuracy: 0.2078

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.7583        | 0.41  | 20   | 0.6581          | 0.5780   | 0.3558   | 0.1055   |
| 0.6291        | 0.82  | 40   | 0.5893          | 0.6285   | 0.4836   | 0.1424   |
| 0.5796        | 1.22  | 60   | 0.5759          | 0.6319   | 0.5261   | 0.1618   |
| 0.5288        | 1.63  | 80   | 0.5409          | 0.6585   | 0.5526   | 0.1430   |
| 0.5111        | 2.04  | 100  | 0.5339          | 0.6681   | 0.5648   | 0.1961   |
| 0.4635        | 2.45  | 120  | 0.5291          | 0.6684   | 0.5714   | 0.1786   |
| 0.4544        | 2.86  | 140  | 0.5282          | 0.6726   | 0.5787   | 0.1618   |
| 0.4398        | 3.27  | 160  | 0.5281          | 0.6736   | 0.5833   | 0.2052   |
| 0.3948        | 3.67  | 180  | 0.5309          | 0.6650   | 0.5896   | 0.1890   |
| 0.41          | 4.08  | 200  | 0.5265          | 0.6785   | 0.5782   | 0.2168   |
| 0.3722        | 4.49  | 220  | 0.5345          | 0.6750   | 0.5924   | 0.2078   |
| 0.3617        | 4.9   | 240  | 0.5295          | 0.6769   | 0.5822   | 0.2155   |
| 0.3362        | 5.31  | 260  | 0.5358          | 0.6696   | 0.5854   | 0.1851   |
| 0.3204        | 5.71  | 280  | 0.5438          | 0.6762   | 0.5747   | 0.2097   |
| 0.3194        | 6.12  | 300  | 0.5503          | 0.6764   | 0.5768   | 0.1832   |
| 0.2921        | 6.53  | 320  | 0.5599          | 0.6734   | 0.5787   | 0.1961   |
| 0.2938        | 6.94  | 340  | 0.5532          | 0.6753   | 0.5863   | 0.1806   |
| 0.2708        | 7.35  | 360  | 0.5634          | 0.6735   | 0.5782   | 0.1922   |
| 0.2625        | 7.76  | 380  | 0.5716          | 0.6727   | 0.5756   | 0.1961   |
| 0.2565        | 8.16  | 400  | 0.5671          | 0.6739   | 0.5798   | 0.1922   |
| 0.2403        | 8.57  | 420  | 0.5816          | 0.6688   | 0.5735   | 0.1728   |
| 0.2466        | 8.98  | 440  | 0.5818          | 0.6739   | 0.5744   | 0.1871   |
| 0.2331        | 9.39  | 460  | 0.5826          | 0.6722   | 0.5762   | 0.1922   |
| 0.2233        | 9.8   | 480  | 0.5843          | 0.6738   | 0.5768   | 0.1942   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2