ScriptForge_Plus is a language model trained on a dataset of 5000 YouTube videos that cover different domains of AI. ScriptForge_Plus 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_Plus 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 ScriptForge_Plus, 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

ScriptForge_Plus : AI content Model
ScriptForge-small : Generalized Content Model

More models are coming soon...

🛒 Intended uses

The intended uses of ScriptForge_Plus 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

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SRDdev/ScriptForge_Plus")
model = AutoModelForCausalLM.from_pretrained("SRDdev/ScriptForge_Plus")

Pipeline

python

from transformers import pipeline
generator = pipeline('text-generation', model= model , tokenizer=tokenizer)

context = "What is Deep Learning"
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
      }