---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: uniBERT.distilBERT.3
  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. -->

# uniBERT.distilBERT.3

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: 1.7293
- Accuracy: (0.4772117962466488,)
- F1: (0.4693132851587397,)
- Precision: (0.5442146665454255,)
- Recall: 0.4772

## 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: 2e-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
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy               | F1                     | Precision              | Recall |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------------------:|:----------------------:|:------:|
| 2.9412        | 1.0   | 210  | 2.7539          | (0.19302949061662197,) | (0.1603618689364134,)  | (0.1948027475862202,)  | 0.1930 |
| 2.1163        | 2.0   | 420  | 2.1123          | (0.30831099195710454,) | (0.2886576454762518,)  | (0.34306886958718397,) | 0.3083 |
| 1.5605        | 3.0   | 630  | 1.9332          | (0.35924932975871315,) | (0.3408370919837017,)  | (0.4437777540758143,)  | 0.3592 |
| 1.2042        | 4.0   | 840  | 1.7857          | (0.4262734584450402,)  | (0.4114641689723971,)  | (0.5423496541710991,)  | 0.4263 |
| 0.9317        | 5.0   | 1050 | 1.7584          | (0.4262734584450402,)  | (0.41517951297641825,) | (0.5270241126881486,)  | 0.4263 |
| 0.7497        | 6.0   | 1260 | 1.7334          | (0.46380697050938335,) | (0.4616472194616382,)  | (0.5561767348377945,)  | 0.4638 |
| 0.6484        | 7.0   | 1470 | 1.7148          | (0.48257372654155495,) | (0.47743545933365816,) | (0.5507046992541056,)  | 0.4826 |
| 0.5396        | 8.0   | 1680 | 1.7341          | (0.47989276139410186,) | (0.4727261312495505,)  | (0.5529949943706518,)  | 0.4799 |
| 0.4599        | 9.0   | 1890 | 1.7252          | (0.4772117962466488,)  | (0.47031578431963555,) | (0.5474190199916943,)  | 0.4772 |
| 0.427         | 10.0  | 2100 | 1.7293          | (0.4772117962466488,)  | (0.4693132851587397,)  | (0.5442146665454255,)  | 0.4772 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2