---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hw2advanced
  results: []
---

<!-- 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. -->

# hw2advanced

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2408
- Precision: {'precision': 0.9067133278302073}
- Recall: {'recall': 0.903472079391197}
- F1: {'f1': 0.9050586148832505}
- Accuracy: {'accuracy': 0.9160687311178247}

## 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: 5e-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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision                         | Recall                         | F1                         | Accuracy                         |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:|
| 0.4744        | 1.0   | 1324 | 0.3413          | {'precision': 0.8554480519619763} | {'recall': 0.8395979020979021} | {'f1': 0.8466159268992823} | {'accuracy': 0.866786253776435}  |
| 0.3566        | 2.0   | 2648 | 0.2408          | {'precision': 0.9067133278302073} | {'recall': 0.903472079391197}  | {'f1': 0.9050586148832505} | {'accuracy': 0.9160687311178247} |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2