---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert-base-uncased-lora-text-classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3834
- Precision: 0.8310
- Recall: 0.8708
- F1 and accuracy: {'accuracy': 0.7877237851662404, 'f1': 0.8504504504504504}

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- 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 | Precision | Recall | F1 and accuracy                                            |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log        | 1.0   | 391  | 0.5803          | 0.7346    | 0.9705 | {'accuracy': 0.7365728900255755, 'f1': 0.836248012718601}  |
| 0.5606        | 2.0   | 782  | 0.5085          | 0.8259    | 0.8229 | {'accuracy': 0.7570332480818415, 'f1': 0.8243992606284658} |
| 0.4687        | 3.0   | 1173 | 0.6925          | 0.8007    | 0.8745 | {'accuracy': 0.7621483375959079, 'f1': 0.8359788359788359} |
| 0.3603        | 4.0   | 1564 | 0.8182          | 0.7955    | 0.9188 | {'accuracy': 0.7800511508951407, 'f1': 0.8527397260273973} |
| 0.3603        | 5.0   | 1955 | 0.8375          | 0.8413    | 0.8413 | {'accuracy': 0.7800511508951407, 'f1': 0.8413284132841329} |
| 0.2736        | 6.0   | 2346 | 1.0186          | 0.8235    | 0.8782 | {'accuracy': 0.7851662404092071, 'f1': 0.8500000000000001} |
| 0.1993        | 7.0   | 2737 | 1.1566          | 0.8224    | 0.9225 | {'accuracy': 0.8081841432225064, 'f1': 0.8695652173913043} |
| 0.1491        | 8.0   | 3128 | 1.2136          | 0.8502    | 0.8376 | {'accuracy': 0.7851662404092071, 'f1': 0.8438661710037174} |
| 0.1224        | 9.0   | 3519 | 1.3815          | 0.8231    | 0.8930 | {'accuracy': 0.7928388746803069, 'f1': 0.8566371681415929} |
| 0.1224        | 10.0  | 3910 | 1.3834          | 0.8310    | 0.8708 | {'accuracy': 0.7877237851662404, 'f1': 0.8504504504504504} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1