metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pk-distilbert-fine-tuned
results: []
pk-distilbert-fine-tuned
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: 1.5887
- Precision: 0.1857
- Recall: 0.4310
- F1: 0.2596
- Accuracy: 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: 0.02
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 1870 | 1.5887 | 0.1857 | 0.4310 | 0.2596 | 0.4310 |
| 0.0 | 2.0 | 3740 | 1.5887 | 0.1857 | 0.4310 | 0.2596 | 0.4310 |
| 0.0 | 3.0 | 5610 | 1.5887 | 0.1857 | 0.4310 | 0.2596 | 0.4310 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1