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 : AI content Model
ScriptGPT-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.

Load Model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SRDdev/ScriptForge-small")
model = AutoModelForCausalLM.from_pretrained("SRDdev/ScriptForge-small")

Pipeline

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

The model may generate random information as it is still in beta version

🎈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
      }