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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: DBERT_Emotions_tuned
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
---
<!-- 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. -->
# DBERT_Emotions_tuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1828
- Accuracy: 0.925
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.1 | 100 | 0.7513 | 0.7365 |
| No log | 0.2 | 200 | 0.3693 | 0.8895 |
| No log | 0.3 | 300 | 0.3118 | 0.906 |
| No log | 0.4 | 400 | 0.3048 | 0.9055 |
| 0.5368 | 0.5 | 500 | 0.2649 | 0.9225 |
| 0.5368 | 0.6 | 600 | 0.2192 | 0.9235 |
| 0.5368 | 0.7 | 700 | 0.2254 | 0.9245 |
| 0.5368 | 0.8 | 800 | 0.2016 | 0.931 |
| 0.5368 | 0.9 | 900 | 0.1685 | 0.935 |
| 0.2254 | 1.0 | 1000 | 0.1926 | 0.9295 |
| 0.2254 | 1.1 | 1100 | 0.2128 | 0.928 |
| 0.2254 | 1.2 | 1200 | 0.2008 | 0.9325 |
| 0.2254 | 1.3 | 1300 | 0.1662 | 0.9385 |
| 0.2254 | 1.4 | 1400 | 0.1945 | 0.939 |
| 0.1315 | 1.5 | 1500 | 0.1652 | 0.939 |
| 0.1315 | 1.6 | 1600 | 0.1820 | 0.938 |
| 0.1315 | 1.7 | 1700 | 0.1660 | 0.938 |
| 0.1315 | 1.8 | 1800 | 0.1590 | 0.93 |
| 0.1315 | 1.9 | 1900 | 0.1601 | 0.935 |
| 0.1295 | 2.0 | 2000 | 0.1645 | 0.9345 |
| 0.1295 | 2.1 | 2100 | 0.1845 | 0.9305 |
| 0.1295 | 2.2 | 2200 | 0.1784 | 0.9355 |
| 0.1295 | 2.3 | 2300 | 0.2042 | 0.9365 |
| 0.1295 | 2.4 | 2400 | 0.1852 | 0.9365 |
| 0.0891 | 2.5 | 2500 | 0.1797 | 0.94 |
| 0.0891 | 2.6 | 2600 | 0.1741 | 0.9365 |
| 0.0891 | 2.7 | 2700 | 0.1758 | 0.9385 |
| 0.0891 | 2.8 | 2800 | 0.1771 | 0.944 |
| 0.0891 | 2.9 | 2900 | 0.1688 | 0.9385 |
| 0.0848 | 3.0 | 3000 | 0.1671 | 0.94 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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