metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: finetuned_text_class
results: []
finetuned_text_class
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4994
- Accuracy: 0.7702
- Recall: 0.8076
- Precision: 0.7557
- F1: 0.7808
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: 8e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 0.499 | 0.9961 | 193 | 0.4700 | 0.7602 | 0.7599 | 0.7652 | 0.7625 |
| 0.3852 | 1.9974 | 387 | 0.4994 | 0.7702 | 0.8076 | 0.7557 | 0.7808 |
| 0.1778 | 2.9987 | 581 | 0.6317 | 0.7638 | 0.6688 | 0.8320 | 0.7415 |
| 0.1007 | 4.0 | 775 | 0.8801 | 0.7609 | 0.7662 | 0.7628 | 0.7645 |
| 0.0567 | 4.9806 | 965 | 1.0289 | 0.7657 | 0.7586 | 0.7744 | 0.7664 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1