metadata
license: apache-2.0
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.924884946845687
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: 0.2455
- Accuracy: 0.925
- F1: 0.9249
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 63 | 0.8670 | 0.7095 | 0.6491 |
| No log | 2.0 | 126 | 0.3938 | 0.886 | 0.8804 |
| No log | 3.0 | 189 | 0.2669 | 0.921 | 0.9201 |
| 0.6268 | 4.0 | 252 | 0.2455 | 0.925 | 0.9249 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 2.6.1
- Tokenizers 0.10.3