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: 0.9405
- name: F1
type: f1
value: 0.9405428930790032
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: 0.2602
- Accuracy: 0.9405
- F1: 0.9405
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: 128
- eval_batch_size: 128
- 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 |
---|---|---|---|---|---|
0.04 | 1.0 | 125 | 0.2096 | 0.9385 | 0.9386 |
0.041 | 2.0 | 250 | 0.2138 | 0.9395 | 0.9396 |
0.0323 | 3.0 | 375 | 0.2165 | 0.94 | 0.9401 |
0.024 | 4.0 | 500 | 0.2315 | 0.941 | 0.9412 |
0.0229 | 5.0 | 625 | 0.2263 | 0.9375 | 0.9374 |
0.0179 | 6.0 | 750 | 0.2561 | 0.9415 | 0.9418 |
0.0149 | 7.0 | 875 | 0.2518 | 0.943 | 0.9433 |
0.0144 | 8.0 | 1000 | 0.2574 | 0.941 | 0.9409 |
0.011 | 9.0 | 1125 | 0.2598 | 0.943 | 0.9430 |
0.009 | 10.0 | 1250 | 0.2602 | 0.9405 | 0.9405 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1