---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# finetuned_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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