|
--- |
|
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 |
|
|
