---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-custom
  results: []
widget:
- text: "2020/01/27 New water filter $210.00"
  example_title: "Item line 1"
- text: "2024/04/04 supply and fit new timer knob and thermostat $03.35"
  example_title: "Item line 2"
- text: "2024/04/01 callout 110.00"
  example_title: "Item line 3"  
---


<!-- 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. -->

# distilbert-base-uncased-custom

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: 0.0006
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log        | 1.0   | 50   | 0.0015          | 1.0       | 1.0    | 1.0 | 1.0      |
| No log        | 2.0   | 100  | 0.0007          | 1.0       | 1.0    | 1.0 | 1.0      |
| No log        | 3.0   | 150  | 0.0006          | 1.0       | 1.0    | 1.0 | 1.0      |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.14.1
- Tokenizers 0.19.1