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: 0.935
- name: F1
type: f1
value: 0.9351633564924363
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: 0.1501
- Accuracy: 0.935
- F1: 0.9352
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: 256
- eval_batch_size: 256
- 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 |
|---|---|---|---|---|---|
| 1.3032 | 1.0 | 63 | 0.8573 | 0.7235 | 0.6635 |
| 0.5915 | 2.0 | 126 | 0.3309 | 0.901 | 0.8998 |
| 0.2647 | 3.0 | 189 | 0.2051 | 0.9305 | 0.9304 |
| 0.1727 | 4.0 | 252 | 0.1764 | 0.933 | 0.9327 |
| 0.1431 | 5.0 | 315 | 0.1648 | 0.9325 | 0.9328 |
| 0.1198 | 6.0 | 378 | 0.1576 | 0.9355 | 0.9354 |
| 0.1075 | 7.0 | 441 | 0.1598 | 0.935 | 0.9351 |
| 0.0987 | 8.0 | 504 | 0.1528 | 0.9335 | 0.9340 |
| 0.0917 | 9.0 | 567 | 0.1513 | 0.9335 | 0.9335 |
| 0.0877 | 10.0 | 630 | 0.1501 | 0.935 | 0.9352 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1