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model_name: Evolutionary Multi-Modal Model
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model_type: transformer
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license: mit
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language: en zh
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datasets:
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- "Custom"
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tags:
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- text-generation
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- code-generation
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- speech-recognition
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- multi-modal
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- evolutionary
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base_model: facebook/bart-base
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finetuned_from: gpt2, bert-base-uncased, facebook/wav2vec2-base-960h, openai/clip-vit-base-patch32
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dataset: Custom Multi-Modal Dataset
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metrics:
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- perplexity
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- bleu
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- wer
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- cer
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library_name: transformers
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pipeline_tag: text-generation
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inference:
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parameters:
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max_length: 50
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top_k: 50
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top_p: 0.95
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temperature: 1.2
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do_sample: true
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speech_recognition:
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waveform_path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
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task: "speech_recognition"
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output_audio_key: "Transcription"
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text_generation:
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input_text: "What is the future of AI?"
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task: "text_generation"
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output_text_key: "Generated Text"
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code_generation:
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input_code: "def add(a, b): return"
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task: "code_generation"
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output_code_key: "Generated Code"
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tests:
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- name: speech_recognition_test
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waveform_path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
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expected_output: "Expected transcription"
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- name: text_generation_test
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input_text: "What is the future of AI?"
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expected_output: "Predicted text about AI"
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- name: code_generation_test
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input_code: "def add(a, b): return"
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expected_output: "def add(a, b): return a + b"
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extra_info:
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author: zero
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version: 1.0
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description: |
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This Evolutionary Multi-Modal Model is designed for tasks like text generation, code generation,
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speech recognition, and vision understanding. It leverages the capabilities of multiple pre-trained
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models and applies evolutionary techniques to optimize performance across these tasks.
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citation:
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- |
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@article{your_reference_2025,
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title={Evolutionary Multi-Modal Model for Enhanced Performance},
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author={Your Name},
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journal={Journal of AI Research},
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year={2025}
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}
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