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: 0.9245
- name: F1
type: f1
value: 0.924405660125098
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: 0.2167
- Accuracy: 0.9245
- F1: 0.9244
Prediction Labels mapping:
- sadness (label_0)
- joy (label_1)
- love (label_2)
- anger (label_3)
- fear (label_4)
- surprise (label_5)
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: 2
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
0.8156 | 1.0 | 250 | 0.3209 | 0.9055 | 0.9041 |
0.2541 | 2.0 | 500 | 0.2167 | 0.9245 | 0.9244 |
Framework versions
- Transformers 4.37.2
- Pytorch 1.13.0
- Datasets 2.19.0
- Tokenizers 0.15.1