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README.md
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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widget:
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- text: 10 Meditation Tips
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example_title: Health Example
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- text: Cooking red sauce pasta
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example_title: Cooking Example
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- text: Introduction to Keras
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example_title: Technology Example
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Tags:
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- text-generation
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metrics:
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- accuracy
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---
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# ScriptForge_Plus
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## 🖊️ Model description
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ScriptForge_Plus is a language model trained on a dataset of 5000 YouTube videos that cover different domains of AI.
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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.
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It generates a probability distribution over the next word given the previous words, without incorporating future words.
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The goal of ScriptForge_Plus is to generate scripts for Youtube videos that are coherent, informative, and engaging.
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This can be useful for content creators who are looking for inspiration or who want to automate the process of generating video scripts.
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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.
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Models
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- [ScriptForge_Plus](https://huggingface.co/SRDdev/ScriptForge_Plus) : AI content Model
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- [ScriptForge-small](https://huggingface.co/SRDdev/ScriptForge-medium) : Generalized Content Model
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More models are coming soon...
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## 🛒 Intended uses
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The intended uses of ScriptForge_Plus include generating scripts for videos, providing inspiration for content creators, and automating the process of generating video scripts.
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## 📝 How to use
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You can use this model directly with a pipeline for text generation.
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1. __Load Model__
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("SRDdev/ScriptForge_Plus")
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model = AutoModelForCausalLM.from_pretrained("SRDdev/ScriptForge_Plus")
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```
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2. __Pipeline__
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```python
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from transformers import pipeline
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generator = pipeline('text-generation', model= model , tokenizer=tokenizer)
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context = "What is Deep Learning"
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length_to_generate = 250
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script = generator(context, max_length=length_to_generate, do_sample=True)[0]['generated_text']
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script
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```
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<p style="opacity: 0.8">The model may generate random information as it is still in beta version</p>
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## 🎈Limitations and bias
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> 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.
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## Citations
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```
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@model{
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Name=Shreyas Dixit
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framework=Pytorch
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Year=Jan 2023
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Pipeline=text-generation
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Github=https://github.com/SRDdev
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LinkedIn=https://www.linkedin.com/in/srddev
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}
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```
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