safetensors generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 region:us
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: CuATR-distilbert-LoRA
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# CuATR-distilbert-LoRA

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: 0.6945
- Accuracy: 0.5652
- F1: 0.7222

## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7007        | 0.67  | 1    | 0.6957          | 0.5652   | 0.7222 |
| 0.6984        | 2.0   | 3    | 0.6954          | 0.5652   | 0.7222 |
| 0.7075        | 2.67  | 4    | 0.6952          | 0.5652   | 0.7222 |
| 0.7           | 4.0   | 6    | 0.6951          | 0.5652   | 0.7222 |
| 0.6962        | 4.67  | 7    | 0.6950          | 0.5652   | 0.7222 |
| 0.7003        | 6.0   | 9    | 0.6948          | 0.5652   | 0.7222 |
| 0.6952        | 6.67  | 10   | 0.6947          | 0.5652   | 0.7222 |
| 0.7027        | 8.0   | 12   | 0.6946          | 0.5652   | 0.7222 |
| 0.6995        | 8.67  | 13   | 0.6946          | 0.5652   | 0.7222 |
| 0.6919        | 9.33  | 14   | 0.6945          | 0.5652   | 0.7222 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
