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: 0.9235
- name: F1
type: f1
value: 0.923296474937779
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: 0.2195
- Accuracy: 0.9235
- F1: 0.9233
Model description
distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.8537 | 1.0 | 250 | 0.3235 | 0.897 | 0.8958 |
| 0.2506 | 2.0 | 500 | 0.2195 | 0.9235 | 0.9233 |
Validation metrics
'test_loss': 0.2194512039422989, 'test_accuracy': 0.9235, 'test_f1': 0.923296474937779, 'test_runtime': 4.4213, 'test_samples_per_second': 452.361, 'test_steps_per_second': 7.238
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1