Create README.md

README.md

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+---
+language:
+- en
+tags:
+- chemistry
+- biology
+- text-generation-inference
+- medical
+- art
+---
+
+The model can process input sequences and generate output sequences, making it suitable for tasks like language translation, text summarization, and dialogue generation.
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+Language Understanding: The model can understand natural language input and generate coherent responses based on the context provided.
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+Imaginary Writing: It can generate imaginative and creative text, allowing for the generation of stories, poems, and other fictional content.
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+No Pre-trained Model Usage: The model does not rely on pre-trained language models like GPT or BERT, making it more customizable and potentially better suited for specific tasks or domains.
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+Encoder-Decoder Architecture: The model follows an Encoder-Decoder paradigm, where the encoder processes input sequences and the decoder generates corresponding output sequences.
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+Flexible Text Generation: The model can generate text with varying lengths, from short sentences to longer passages, and can be controlled to limit the length of the generated output.
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+Training Capabilities: The model can be trained using input-output pairs, allowing for supervised learning on specific datasets tailored to the task at hand.
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+Overall, the N-GEN-2 model is a versatile architecture capable of generating natural language text for a wide range of applications, from storytelling to language translation, without relying on pre-trained models.
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+
+N-GEN-2 BY TNSA-AI