metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: training_with_callbacks
results: []
training_with_callbacks
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: 0.1529
- Precision: 0.4993
- Recall: 0.5397
- F1: 0.5187
- Accuracy: 0.9661
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 205 | 0.1641 | 0.3048 | 0.3556 | 0.3282 | 0.9556 |
| No log | 2.0 | 410 | 0.1387 | 0.4741 | 0.4365 | 0.4545 | 0.9642 |
| 0.1943 | 3.0 | 615 | 0.1430 | 0.4690 | 0.4810 | 0.4749 | 0.9648 |
| 0.1943 | 4.0 | 820 | 0.1481 | 0.4993 | 0.5365 | 0.5172 | 0.9655 |
| 0.0496 | 5.0 | 1025 | 0.1529 | 0.4993 | 0.5397 | 0.5187 | 0.9661 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2