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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 Exmaple |
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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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--- |
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# ScriptForge-small |
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## 🖊️ Model description |
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ScriptForge-small is a language model trained on a dataset of 100 YouTube videos that cover different domains of Youtube videos. |
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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. |
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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-small 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 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. |
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Models |
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- [Script_GPT](https://huggingface.co/SRDdev/ScriptForge) : AI content Model |
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- [ScriptGPT-small](https://huggingface.co/SRDdev/ScriptForge-small) : 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-small 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-small") |
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model = AutoModelForCausalLM.from_pretrained("SRDdev/ScriptForge-small") |
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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 = "Cooking red sauce pasta" |
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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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``` |
