---
base_model: distilbert/distilbert-base-uncased
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3829
- Accuracy: 0.9002
- F1: 0.9024
- Precision: 0.8993
- Recall: 0.9054

## 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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1293        | 1.0   | 4210  | 0.3084          | 0.8922   | 0.8941 | 0.8941    | 0.8941 |
| 0.0939        | 2.0   | 8420  | 0.3646          | 0.8933   | 0.9001 | 0.8604    | 0.9437 |
| 0.0981        | 3.0   | 12630 | 0.3829          | 0.9002   | 0.9024 | 0.8993    | 0.9054 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1