metadata
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: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9365
- name: F1
type: f1
value: 0.9364320826595705
distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.2455
- Accuracy: 0.9365
- F1: 0.9364
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.1025 | 1.0 | 250 | 0.1811 | 0.932 | 0.9324 |
| 0.0973 | 2.0 | 500 | 0.1672 | 0.9355 | 0.9363 |
| 0.0851 | 3.0 | 750 | 0.1822 | 0.934 | 0.9339 |
| 0.0668 | 4.0 | 1000 | 0.1879 | 0.9335 | 0.9330 |
| 0.0508 | 5.0 | 1250 | 0.2047 | 0.937 | 0.9371 |
| 0.0427 | 6.0 | 1500 | 0.2234 | 0.933 | 0.9327 |
| 0.0326 | 7.0 | 1750 | 0.2331 | 0.933 | 0.9329 |
| 0.0276 | 8.0 | 2000 | 0.2420 | 0.9335 | 0.9331 |
| 0.0225 | 9.0 | 2250 | 0.2420 | 0.9345 | 0.9344 |
| 0.017 | 10.0 | 2500 | 0.2455 | 0.9365 | 0.9364 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2