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: 0.94
- name: F1
type: f1
value: 0.9399138482178033
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: 0.2319
- Accuracy: 0.94
- F1: 0.9399
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: 15
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
No log | 1.0 | 63 | 0.1858 | 0.9375 | 0.9373 |
No log | 2.0 | 126 | 0.2010 | 0.9295 | 0.9300 |
No log | 3.0 | 189 | 0.1832 | 0.936 | 0.9365 |
0.0589 | 4.0 | 252 | 0.1928 | 0.9345 | 0.9340 |
0.0589 | 5.0 | 315 | 0.2094 | 0.937 | 0.9367 |
0.0589 | 6.0 | 378 | 0.2016 | 0.937 | 0.9369 |
0.0589 | 7.0 | 441 | 0.2205 | 0.936 | 0.9354 |
0.0427 | 8.0 | 504 | 0.2143 | 0.936 | 0.9355 |
0.0427 | 9.0 | 567 | 0.2184 | 0.9355 | 0.9357 |
0.0427 | 10.0 | 630 | 0.2216 | 0.9365 | 0.9365 |
0.0427 | 11.0 | 693 | 0.2313 | 0.938 | 0.9380 |
0.0261 | 12.0 | 756 | 0.2311 | 0.9395 | 0.9394 |
0.0261 | 13.0 | 819 | 0.2274 | 0.9395 | 0.9394 |
0.0261 | 14.0 | 882 | 0.2302 | 0.9395 | 0.9395 |
0.0261 | 15.0 | 945 | 0.2319 | 0.94 | 0.9399 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1