---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: uniBERT.distilBERT.1
  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.1

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: 1.5970
- Accuracy: (0.48257372654155495,)
- F1: (0.48821617755360286,)
- Precision: (0.5906810375519806,)
- Recall: 0.4826

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------------------:|:----------------------:|:------:|
| 3.191         | 1.0   | 163  | 2.8674          | (0.13136729222520108,) | (0.10551643479608444,) | (0.13934178819155285,) | 0.1314 |
| 2.4168        | 2.0   | 326  | 2.2479          | (0.23771224307417338,) | (0.2196759312115438,)  | (0.46668150798486385,) | 0.2377 |
| 1.8497        | 3.0   | 489  | 1.9548          | (0.30473637176050045,) | (0.30606381370679575,) | (0.5287854015073502,)  | 0.3047 |
| 1.2962        | 4.0   | 652  | 1.7795          | (0.36371760500446826,) | (0.3751096263138952,)  | (0.5125374899925749,)  | 0.3637 |
| 1.176         | 5.0   | 815  | 1.7043          | (0.4066130473637176,)  | (0.41869044107007675,) | (0.5244984950622734,)  | 0.4066 |
| 0.8751        | 6.0   | 978  | 1.6665          | (0.4316353887399464,)  | (0.44145109290311435,) | (0.532683425541351,)   | 0.4316 |
| 0.7541        | 7.0   | 1141 | 1.6273          | (0.4450402144772118,)  | (0.4544413917407461,)  | (0.5812233728930422,)  | 0.4450 |
| 0.6257        | 8.0   | 1304 | 1.6054          | (0.46291331546023234,) | (0.46971058022945406,) | (0.5769805019581083,)  | 0.4629 |
| 0.5855        | 9.0   | 1467 | 1.5948          | (0.47542448614834676,) | (0.4826847668965276,)  | (0.5865283417376732,)  | 0.4754 |
| 0.5672        | 10.0  | 1630 | 1.5970          | (0.48257372654155495,) | (0.48821617755360286,) | (0.5906810375519806,)  | 0.4826 |


### Framework versions

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