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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: train[:2000]
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.895
- name: F1
type: f1
value: 0.8961058726378275
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
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: 0.7264
- Accuracy: 0.895
- Balanced accuracy: 0.8746
- F1: 0.8961
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|
| 0.001 | 1.0 | 25 | 0.7713 | 0.89 | 0.8807 | 0.8915 |
| 0.0069 | 2.0 | 50 | 0.7734 | 0.905 | 0.8906 | 0.9070 |
| 0.0019 | 3.0 | 75 | 0.8670 | 0.88 | 0.8749 | 0.8819 |
| 0.0012 | 4.0 | 100 | 0.7387 | 0.895 | 0.8806 | 0.8953 |
| 0.0002 | 5.0 | 125 | 0.7841 | 0.885 | 0.8649 | 0.8858 |
| 0.0002 | 6.0 | 150 | 0.7415 | 0.9 | 0.8753 | 0.9001 |
| 0.0002 | 7.0 | 175 | 0.7378 | 0.895 | 0.8719 | 0.8955 |
| 0.0002 | 8.0 | 200 | 0.7452 | 0.89 | 0.8711 | 0.8910 |
| 0.0002 | 9.0 | 225 | 0.7555 | 0.89 | 0.8787 | 0.8908 |
| 0.0001 | 10.0 | 250 | 0.7541 | 0.895 | 0.8822 | 0.8959 |
| 0.0001 | 11.0 | 275 | 0.7536 | 0.9 | 0.8857 | 0.9009 |
| 0.0001 | 12.0 | 300 | 0.7530 | 0.9 | 0.8857 | 0.9009 |
| 0.0001 | 13.0 | 325 | 0.7542 | 0.9 | 0.8857 | 0.9009 |
| 0.0001 | 14.0 | 350 | 0.7532 | 0.895 | 0.8746 | 0.8957 |
| 0.0002 | 15.0 | 375 | 0.8554 | 0.88 | 0.8424 | 0.8803 |
| 0.0001 | 16.0 | 400 | 0.7700 | 0.9 | 0.8867 | 0.9011 |
| 0.0001 | 17.0 | 425 | 0.7302 | 0.895 | 0.8746 | 0.8961 |
| 0.0001 | 18.0 | 450 | 0.7304 | 0.895 | 0.8746 | 0.8961 |
| 0.0001 | 19.0 | 475 | 0.7284 | 0.895 | 0.8746 | 0.8961 |
| 0.0001 | 20.0 | 500 | 0.7264 | 0.895 | 0.8746 | 0.8961 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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