---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: WO_CausalModel_2x
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# WO_CausalModel_2x

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6035

## Model description

It is focused on generating realistic WO descriptions when prompted with a given WO's priority, activity type, maintenance type, and location.

## Intended uses & limitations

This is a proof of concept model for a larger project. 

## 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 151  | 0.6217          |
| No log        | 2.0   | 302  | 0.6133          |
| No log        | 3.0   | 453  | 0.6087          |
| 0.6243        | 4.0   | 604  | 0.6079          |
| 0.6243        | 5.0   | 755  | 0.6049          |
| 0.6243        | 6.0   | 906  | 0.6035          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2