language:
- en
thumbnail: >-
https://styles.redditmedia.com/t5_2to41/styles/communityIcon_qedoavxzocr61.png?width=256&s=9c7c19b81474c3788279b8d6d6823e791d0524fc
datasets:
- 'reddit_tifu (subset: short)'
widget:
- text: I told my friend
license: mit
mgfrantz/distilgpt2-finetuned-reddit-tifu
This model was trained to as practice for fine-tuning a causal language model. There was no intended use case for this model besides having some fun seeing how different things might be screwed up.
Data
This model was trained on "short" subset of reddit_tifu dataset.
The data was split into 90% train and 10% validation using dataset.train_test_split, with a seed of 0.
To prepare the data for training, the "tldr" and "documents" fields were joined by "\n\n".
When multiple items were in the "tldr" or "documents" fields, only the first item was selected for joining.
These joined documents were tokenized using the "distilgpt2" tokenizer.
Finally, tokenized texts were concatenated end-to-end and split into blocks of 128 tokens.
TODO: Add a different separation token between documents that can be used to stop generation.
Training
This model was trained in Colab by fine-tuning distilgpt2 for 174390 steps (3 epochs).
Default training arguments were used, except for learning_rate=2e-5 and weight_decay=0.01.
At the conclusion of training, a training loss of 3.52 and a validation loss of 3.44 were observed.