language:
- en
license: mit
library_name: transformers
tags:
- generated_from_trainer
datasets:
- 0Tick/E621-Random-PostsTag-Scrape
metrics:
- accuracy
co2_eq_emissions: 100
pipeline_tag: text-generation
base_model: distilgpt2
model-index:
- name: e621TagAutocomplete
results: []
Model description
This is a fine-tuned version of distilgpt2 which is intended to be used with the promptgen extension inside the AUTOMATIC1111 WebUI. It is trained on the raw tags of e621 with underscores and spaces
Training
This model is a fine-tuned version of distilgpt2 on a dataset of the tags of 116k random posts of e621.net. It achieves the following results on the evaluation set:
- Loss: 4.3983
- Accuracy: 0.3865
Training and evaluation data
Use this collab notebook to train your own model. Also used to train this model
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Intended uses & limitations
Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model.
The developers of GPT-2 state in their model card that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including:
- Writing assistance: Grammar assistance, autocompletion (for normal prose or code)
- Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.
- Entertainment: Creation of games, chat bots, and amusing generations.
Using DistilGPT2, the Hugging Face team built the Write With Transformers web app, which allows users to play with the model to generate text directly from their browser.
Out-of-scope Uses
OpenAI states in the GPT-2 model card:
Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2