---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_distilbert-base-uncased
  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_distilbert-base-uncased

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: 0.1696
- Accuracy: 0.9277
- Precision: 0.9364
- Recall: 0.9447
- F1: 0.9406

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6033        | 0.09  | 50   | 0.3599          | 0.8509   | 0.8622    | 0.8970 | 0.8792 |
| 0.3466        | 0.18  | 100  | 0.3466          | 0.8527   | 0.9638    | 0.7862 | 0.8660 |
| 0.2446        | 0.28  | 150  | 0.2166          | 0.9073   | 0.9293    | 0.9165 | 0.9229 |
| 0.2277        | 0.37  | 200  | 0.2014          | 0.9137   | 0.9153    | 0.9450 | 0.9299 |
| 0.2099        | 0.46  | 250  | 0.2183          | 0.9174   | 0.9090    | 0.9596 | 0.9336 |
| 0.2276        | 0.55  | 300  | 0.1927          | 0.9195   | 0.9275    | 0.9405 | 0.9340 |
| 0.21          | 0.64  | 350  | 0.1807          | 0.9254   | 0.9381    | 0.9387 | 0.9384 |
| 0.2009        | 0.74  | 400  | 0.1808          | 0.9236   | 0.9471    | 0.9254 | 0.9361 |
| 0.1816        | 0.83  | 450  | 0.1823          | 0.9238   | 0.9173    | 0.9607 | 0.9385 |
| 0.1728        | 0.92  | 500  | 0.1696          | 0.9277   | 0.9364    | 0.9447 | 0.9406 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2