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: 0.9395
- name: F1
type: f1
value: 0.9393105000343236
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: 0.3355
- Accuracy: 0.9395
- F1: 0.9393
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 |
|---|---|---|---|---|---|
| 0.0251 | 1.0 | 250 | 0.2793 | 0.9375 | 0.9377 |
| 0.0187 | 2.0 | 500 | 0.3246 | 0.931 | 0.9313 |
| 0.0147 | 3.0 | 750 | 0.3264 | 0.9365 | 0.9367 |
| 0.0116 | 4.0 | 1000 | 0.3252 | 0.938 | 0.9381 |
| 0.0097 | 5.0 | 1250 | 0.3036 | 0.9365 | 0.9366 |
| 0.0086 | 6.0 | 1500 | 0.3190 | 0.9395 | 0.9394 |
| 0.0063 | 7.0 | 1750 | 0.3181 | 0.939 | 0.9390 |
| 0.0042 | 8.0 | 2000 | 0.3493 | 0.938 | 0.9378 |
| 0.004 | 9.0 | 2250 | 0.3350 | 0.9405 | 0.9402 |
| 0.0025 | 10.0 | 2500 | 0.3355 | 0.9395 | 0.9393 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1