| library_name: peft | |
| base_model: google/flan-t5-large | |
| # Model Card for honicky/t5-short-story-character-extractor | |
| I trained this model as part of a learning project to build a children's story authoring tool for parents of young children. See http://www.storytime.glass/ | |
| This model takes in a short story and outputs a comma separated list of characters in the story. | |
| I'm not sure yet how useful this fine-tune is: rather it is for me to learn about the nuts and bolts of fine-tuning. | |
| ## Model Details | |
| The model is a fine-tune of a sequence-to-sequence model [Flan T5 Large](https://huggingface.co./google/flan-t5-large), so a different architecture from decoder-only models like GPT*. Maybe this allows it to perform this transformation task (transform a story into a list of characters) using a smaller model? | |
| * Trained using `transformers.Seq2SeqTrainer` plus the corresponding collator, tokenizer etc | |
| - **Developed by:** RJ Honicky | |
| - **Model type:** Encoder-Decoder Transformer | |
| - **Language(s):** English (fine tune data set) | |
| - **License:** MIT | |
| - **Finetuned from model:** `google/flan-t5-large` | |
| ### Model Sources | |
| - **Repository:** https://github.com/honicky/character-extraction | |
| ## Uses | |
| Primarily for use in https://github.com/honicky/story-time and for learning. | |