metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0416
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: 0.9375
- name: F1
type: f1
value: 0.9376066970307232
- name: Precision
type: precision
value: 0.907678056501393
distilbert-base-uncased_emotion_ft_0416
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: 0.1420
- Accuracy: 0.9375
- F1: 0.9376
- Precision: 0.9077
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: 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|---|---|---|---|---|---|---|
| 0.2093 | 1.0 | 250 | 0.1715 | 0.9345 | 0.9349 | 0.9042 |
| 0.1325 | 2.0 | 500 | 0.1523 | 0.9335 | 0.9340 | 0.8994 |
| 0.1017 | 3.0 | 750 | 0.1437 | 0.9365 | 0.9369 | 0.9029 |
| 0.08 | 4.0 | 1000 | 0.1420 | 0.9375 | 0.9376 | 0.9077 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1