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: 0.9355
- name: F1
type: f1
value: 0.9356656226665225
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: 0.3181
- Accuracy: 0.9355
- F1: 0.9357
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 |
|---|---|---|---|---|---|
| 0.0507 | 1.0 | 250 | 0.2184 | 0.936 | 0.9361 |
| 0.0368 | 2.0 | 500 | 0.2840 | 0.9345 | 0.9344 |
| 0.029 | 3.0 | 750 | 0.3019 | 0.9345 | 0.9348 |
| 0.0186 | 4.0 | 1000 | 0.3011 | 0.936 | 0.9361 |
| 0.0129 | 5.0 | 1250 | 0.3036 | 0.9375 | 0.9377 |
| 0.0139 | 6.0 | 1500 | 0.3131 | 0.938 | 0.9382 |
| 0.0115 | 7.0 | 1750 | 0.3076 | 0.939 | 0.9390 |
| 0.0091 | 8.0 | 2000 | 0.3198 | 0.934 | 0.9339 |
| 0.0096 | 9.0 | 2250 | 0.3140 | 0.9345 | 0.9345 |
| 0.011 | 10.0 | 2500 | 0.3181 | 0.9355 | 0.9357 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1