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--- |
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library_name: transformers |
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license: mit |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: finetuned_model |
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results: [] |
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datasets: |
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- cassieli226/cities-text-dataset |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# finetuned_model |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0029 |
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- Accuracy: 1.0 |
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- F1: 1.0 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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## Model description |
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It is a finetuned model for binary classification of description of city. It will result in either Pittsburgh or Shanghai. |
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## Intended uses & limitations |
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This is for education and demonstration purposes. |
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## Training and evaluation data |
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The data for finetuning the model comes from this HF dataset: cassieli226/cities-text-dataset |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1213 | 1.0 | 80 | 0.0831 | 0.975 | 0.9750 | 0.9762 | 0.975 | |
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| 0.006 | 2.0 | 160 | 0.0034 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.002 | 3.0 | 240 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0016 | 4.0 | 320 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0014 | 5.0 | 400 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.56.1 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.0 |
