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: 0.938
- name: F1
type: f1
value: 0.9379552147966106
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: 0.1541
- Accuracy: 0.938
- F1: 0.9380
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7817 | 1.0 | 250 | 0.2605 | 0.917 | 0.9177 |
| 0.1947 | 2.0 | 500 | 0.1744 | 0.931 | 0.9306 |
| 0.1305 | 3.0 | 750 | 0.1558 | 0.9375 | 0.9382 |
| 0.1017 | 4.0 | 1000 | 0.1426 | 0.9375 | 0.9371 |
| 0.083 | 5.0 | 1250 | 0.1383 | 0.9385 | 0.9381 |
| 0.0696 | 6.0 | 1500 | 0.1591 | 0.94 | 0.9401 |
| 0.0604 | 7.0 | 1750 | 0.1557 | 0.9355 | 0.9354 |
| 0.0521 | 8.0 | 2000 | 0.1541 | 0.938 | 0.9380 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2