metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0526
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: 0.9375
- name: F1
type: f1
value: 0.937552703246777
- name: Precision
type: precision
value: 0.9169515578018389
distilbert-base-uncased_emotion_ft_0526
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: 0.2275
- Accuracy: 0.9375
- F1: 0.9376
- Precision: 0.9170
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|---|---|---|---|---|---|---|
| 0.2131 | 1.0 | 2000 | 0.2301 | 0.93 | 0.9305 | 0.9008 |
| 0.1881 | 2.0 | 4000 | 0.1854 | 0.9385 | 0.9388 | 0.9080 |
| 0.1012 | 3.0 | 6000 | 0.2200 | 0.935 | 0.9353 | 0.9066 |
| 0.0642 | 4.0 | 8000 | 0.2275 | 0.9375 | 0.9376 | 0.9170 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3