---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: final_V1-distilbert-text-classification-model
  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. -->

# final_V1-distilbert-text-classification-model

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: 0.1494
- Accuracy: 0.9672
- F1: 0.8312
- Precision: 0.8275
- Recall: 0.8357

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6662        | 0.11  | 50   | 1.6945          | 0.2888   | 0.0820 | 0.1958    | 0.1341 |
| 0.7494        | 0.22  | 100  | 0.6947          | 0.8034   | 0.4962 | 0.4949    | 0.5054 |
| 0.2779        | 0.33  | 150  | 0.4631          | 0.8980   | 0.6685 | 0.6550    | 0.6829 |
| 0.2204        | 0.44  | 200  | 0.3938          | 0.8999   | 0.6686 | 0.6659    | 0.6758 |
| 0.137         | 0.55  | 250  | 0.4153          | 0.9065   | 0.6707 | 0.6537    | 0.6898 |
| 0.1931        | 0.66  | 300  | 0.3093          | 0.9166   | 0.7089 | 0.7728    | 0.7046 |
| 0.1356        | 0.76  | 350  | 0.3384          | 0.9152   | 0.6904 | 0.8123    | 0.6978 |
| 0.1065        | 0.87  | 400  | 0.4172          | 0.9144   | 0.7233 | 0.7804    | 0.7174 |
| 0.105         | 0.98  | 450  | 0.4521          | 0.8852   | 0.7078 | 0.7342    | 0.7051 |
| 0.1275        | 1.09  | 500  | 0.2837          | 0.9262   | 0.7365 | 0.7927    | 0.7275 |
| 0.0754        | 1.2   | 550  | 0.3979          | 0.9180   | 0.7164 | 0.8039    | 0.7133 |
| 0.0861        | 1.31  | 600  | 0.1506          | 0.9604   | 0.8259 | 0.8247    | 0.8280 |
| 0.0514        | 1.42  | 650  | 0.1397          | 0.9664   | 0.8277 | 0.8264    | 0.8293 |
| 0.0536        | 1.53  | 700  | 0.1566          | 0.9642   | 0.8279 | 0.8255    | 0.8308 |
| 0.0351        | 1.64  | 750  | 0.1804          | 0.9620   | 0.8276 | 0.8251    | 0.8312 |
| 0.0862        | 1.75  | 800  | 0.1445          | 0.9655   | 0.8314 | 0.8307    | 0.8322 |
| 0.0461        | 1.86  | 850  | 0.1492          | 0.9669   | 0.8306 | 0.8291    | 0.8324 |
| 0.0663        | 1.97  | 900  | 0.2054          | 0.9604   | 0.8292 | 0.8299    | 0.8295 |
| 0.0482        | 2.07  | 950  | 0.1498          | 0.9655   | 0.8294 | 0.8272    | 0.8324 |
| 0.0299        | 2.18  | 1000 | 0.1657          | 0.9650   | 0.8292 | 0.8269    | 0.8321 |
| 0.0348        | 2.29  | 1050 | 0.1473          | 0.9686   | 0.8310 | 0.8291    | 0.8332 |
| 0.0283        | 2.4   | 1100 | 0.1470          | 0.9694   | 0.8333 | 0.8297    | 0.8376 |
| 0.0115        | 2.51  | 1150 | 0.1496          | 0.9691   | 0.8336 | 0.8317    | 0.8358 |
| 0.004         | 2.62  | 1200 | 0.1671          | 0.9650   | 0.8301 | 0.8280    | 0.8329 |
| 0.0054        | 2.73  | 1250 | 0.1560          | 0.9694   | 0.8333 | 0.8325    | 0.8343 |
| 0.0217        | 2.84  | 1300 | 0.1553          | 0.9696   | 0.8334 | 0.8326    | 0.8345 |
| 0.0054        | 2.95  | 1350 | 0.1603          | 0.9691   | 0.8332 | 0.8324    | 0.8343 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2