---
library_name: transformers
license: mit
base_model: raghavbali/gpt2-finetuned-headliner
tags:
- generated_from_trainer
model-index:
- name: gpt2-instruct-tuned-translator2
  results: []
---

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# GPT2 Instruction Tuned English To German Headline Translation Model

- This model makes use of a english to german news headline translation dataset derived from [Harvard/abc-news-dataset](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SYBGZL) for the task of instruction tuning
- The dataset was derived using LLaMA3.1 and GPT4o models for generating the translations
- This model is a fine-tuned version of [raghavbali/gpt2-finetuned-headliner](https://huggingface.co/raghavbali/gpt2-finetuned-headliner).

## Model description

This model leverages a Stanford Alpaca style instruction tuning dataset, the format is as follows:
```md
###Translate English Text to German:{text} ###Output: {translated_text}
```
The format is slightly modified to reduce the additional tokens required for the instructions as GPT2 context size is very limited.
The model is trained on small ~5k sample to showcase the impact of instruction tuning on overall alignment of the model towards requested task

## Intended uses & limitations

This is only for learning purposes. The model seems to have picked up German vocabulary as well as sentence structures to a good extent but the actual translations are at time grossly incorrect.
The model also attempts at completing the news headlines given as prompt and has a high tendency to hallucinate.


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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
- lr_scheduler_warmup_steps: 4
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1