metadata
library_name: transformers
license: mit
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_model
results: []
datasets:
- cassieli226/cities-text-dataset
language:
- en
finetuned_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0029
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
Model description
It is a finetuned model for binary classification of description of city. It will result in either Pittsburgh or Shanghai.
Intended uses & limitations
This is for education and demonstration purposes.
Training and evaluation data
The data for finetuning the model comes from this HF dataset: cassieli226/cities-text-dataset
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1213 | 1.0 | 80 | 0.0831 | 0.975 | 0.9750 | 0.9762 | 0.975 |
| 0.006 | 2.0 | 160 | 0.0034 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.002 | 3.0 | 240 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0016 | 4.0 | 320 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 5.0 | 400 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0