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--- |
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library_name: transformers |
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license: mit |
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base_model: raghavbali/gpt2-finetuned-headliner |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: gpt2-instruct-tuned-translator2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# GPT2 Instruction Tuned English To German Headline Translation Model |
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- 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 |
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- The dataset was derived using LLaMA3.1 and GPT4o models for generating the translations |
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- This model is a fine-tuned version of [raghavbali/gpt2-finetuned-headliner](https://huggingface.co/raghavbali/gpt2-finetuned-headliner). |
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## Model description |
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This model leverages a Stanford Alpaca style instruction tuning dataset, the format is as follows: |
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```md |
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###Translate English Text to German:{text} ###Output: {translated_text} |
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``` |
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The format is slightly modified to reduce the additional tokens required for the instructions as GPT2 context size is very limited. |
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The model is trained on small ~5k sample to showcase the impact of instruction tuning on overall alignment of the model towards requested task |
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## Intended uses & limitations |
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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. |
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The model also attempts at completing the news headlines given as prompt and has a high tendency to hallucinate. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 4 |
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- num_epochs: 1 |
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### Training results |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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