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-2024-02-10
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: 0.747
- name: F1
type: f1
value: 0.6949375855120276
distilbert-base-uncased-finetuned-emotion-2024-02-10
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: 0.7689
- Accuracy: 0.747
- F1: 0.6949
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: 1e-06
- 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: 9
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.331 | 1.0 | 250 | 1.2185 | 0.572 | 0.4495 |
| 1.1818 | 2.0 | 500 | 1.1132 | 0.5905 | 0.4665 |
| 1.0888 | 3.0 | 750 | 1.0287 | 0.6235 | 0.5262 |
| 1.0059 | 4.0 | 1000 | 0.9443 | 0.6905 | 0.6258 |
| 0.9335 | 5.0 | 1250 | 0.8771 | 0.7135 | 0.6539 |
| 0.872 | 6.0 | 1500 | 0.8277 | 0.7285 | 0.6726 |
| 0.8313 | 7.0 | 1750 | 0.7945 | 0.741 | 0.6871 |
| 0.8047 | 8.0 | 2000 | 0.7757 | 0.747 | 0.6942 |
| 0.7931 | 9.0 | 2250 | 0.7689 | 0.747 | 0.6949 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1