| --- |
| license: apache-2.0 |
| datasets: |
| - SRDdev/Youtube-Scripts |
| language: |
| - en |
| pipeline_tag: text-generation |
| widget: |
| - text: Introduction to Keras ? |
| example_title: Example 1 |
| - text: Introduction to Vertex AI Feature Store |
| exmaple_title: Example 2 |
| tags: |
| - Text-Generation |
| --- |
| |
| # ScriptGPT |
|
|
|
|
| ## 🖊️ Model description |
| ScriptGPT is a language model trained on a dataset of 5,000 YouTube videos that explain artificial intelligence (AI) concepts. |
| ScriptGPT is a Causal language transformer. The model resembles the GPT2 architecture, |
| the model is a Causal Language model meaning it predicts the probability of a sequence of words based on the preceding words in the sequence. |
| It generates a probability distribution over the next word given the previous words, without incorporating future words. |
|
|
| The goal of ScriptGPT is to generate scripts for AI videos that are coherent, informative, and engaging. |
| This can be useful for content creators who are looking for inspiration or who want to automate the process of generating video scripts. |
| To use ScriptGPT, users can provide a prompt or a starting sentence, and the model will generate a sequence of words that follow the context and style of the training data. |
|
|
| Models |
| - [Script_GPT](https://huggingface.co/SRDdev/Script_GPT) : AI content Model |
| - [ScriptGPT-small](https://huggingface.co/SRDdev/ScriptGPT-small) : Generalized Content Model |
|
|
| More models are coming soon... |
|
|
| ## 🛒 Intended uses |
| The intended uses of ScriptGPT include generating scripts for videos that explain artificial intelligence concepts, providing inspiration for content creators, and |
| automating the process of generating video scripts. |
|
|
|
|
| ## 📝 How to use |
| You can use this model directly with a pipeline for text generation. |
|
|
| 1. __Load Model__ |
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| tokenizer = AutoTokenizer.from_pretrained("SRDdev/Script_GPT") |
| model = AutoModelForCausalLM.from_pretrained("SRDdev/Script_GPT") |
| ``` |
|
|
| 2. __Pipeline__ |
| ```python |
| from transformers import pipeline |
| generator = pipeline('text-generation', model= model , tokenizer=tokenizer) |
| |
| context = "Introduction to Vertex AI Feature Store" |
| length_to_generate = 200 |
| |
| script = generator(context, max_length=length_to_generate, do_sample=True)[0]['generated_text'] |
| ``` |
| <p style="opacity: 0.8">Keeping the context more technical and related to AI will generate better outputs</p> |
|
|
| ## 🎈Limitations and bias |
| > The model is trained on Youtube Scripts and will work better for that. It may also generate random information and users should be aware of that and cross-validate the results. |
|
|
| The used is linked [here](https://www.kaggle.com/datasets/jfcaro/5000-transcripts-of-youtube-ai-related-videos) |
|
|
| ## Citations |
| ``` |
| @model{ |
| Name=Shreyas Dixit |
| framework=Pytorch |
| Year=Jan 2023 |
| Pipeline=text-generation |
| Github=https://github.com/SRDdev |
| LinkedIn=https://www.linkedin.com/in/srddev |
| } |
| ``` |
