metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: emotional-distilbert
results: []
emotional-distilbert
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2137
- Accuracy: 0.4310
- F1: 0.4257
- Precision: 0.4466
- Recall: 0.4310
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: 16
- eval_batch_size: 16
- 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 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 2.7164 | 1.0 | 276 | 2.6348 | 0.2840 | 0.2368 | 0.4033 | 0.2840 |
| 1.3322 | 2.0 | 552 | 2.0566 | 0.4183 | 0.4064 | 0.4338 | 0.4183 |
| 0.5727 | 3.0 | 828 | 1.9395 | 0.4029 | 0.3975 | 0.4292 | 0.4029 |
| 0.2102 | 4.0 | 1104 | 1.9605 | 0.4156 | 0.4115 | 0.4400 | 0.4156 |
| 0.0697 | 5.0 | 1380 | 2.0963 | 0.4365 | 0.4205 | 0.4438 | 0.4365 |
| 0.0261 | 6.0 | 1656 | 2.2137 | 0.4310 | 0.4257 | 0.4466 | 0.4310 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2