| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | pipeline_tag: text-generation |
| | widget: |
| | - text: 10 Meditation tips |
| | example_title: Health Exmaple |
| | - text: Cooking red sauce pasta |
| | example_title: Cooking Example |
| | - text: Introduction to Keras |
| | example_title: Technology Example |
| | tags: |
| | - text-generation |
| | --- |
| | # ScriptForge-small |
| |
|
| | ## 🖊️ Model description |
| | ScriptForge-small is a language model trained on a dataset of 100 YouTube videos that cover different domains of Youtube videos. |
| | ScriptForge-small 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 ScriptForge-small is to generate scripts for Youtube 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-small, 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/ScriptForge) : AI content Model |
| | - [ScriptGPT-small](https://huggingface.co/SRDdev/ScriptForge-small) : Generalized Content Model |
| |
|
| | More models are coming soon... |
| |
|
| | ## 🛒 Intended uses |
| | The intended uses of ScriptForge-small include generating scripts for videos, 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/ScriptForge-small") |
| | model = AutoModelForCausalLM.from_pretrained("SRDdev/ScriptForge-small") |
| | ``` |
| |
|
| | 2. __Pipeline__ |
| | ```python |
| | from transformers import pipeline |
| | generator = pipeline('text generation, model= model , tokenizer=tokenizer) |
| | |
| | context = "Cooking red sauce pasta" |
| | length_to_generate = 250 |
| | |
| | script = generator(context, max_length=length_to_generate, do_sample=True)[0]['generated_text'] |
| | |
| | script |
| | ``` |
| | <p style="opacity: 0.8">The model may generate random information as it is still in beta version</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. |
| |
|
| | ## 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 |
| | } |
| | ``` |
