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model_information.py
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import pandas as pd
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# Define the data
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data = {
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"Original Name" : [],
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"Proper Display Name": [],
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"Link" : [],
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}
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# Add model information to the
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data['Original Name'].append('salmonn_7b')
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data['Proper Display Name'].append('SALMONN-7B')
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data['Link'].append('https://arxiv.org/html/2310.13289v2')
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data['Original Name'].append('wavllm_fairseq')
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data['Proper Display Name'].append('WavLLM')
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data['Link'].append('https://arxiv.org/abs/2404.00656')
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data['Original Name'].append('Qwen2-Audio-7B-Instruct')
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data['Proper Display Name'].append('Qwen2-Audio-7B-Instruct')
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data['Link'].append('https://arxiv.org/abs/2407.10759')
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data['Original Name'].append('whisper_large_v3_with_llama_3_8b_instruct')
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data['Proper Display Name'].append('Whisper-Large-v3+Llama-3-8B-Instruct')
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data['Link'].append('https://arxiv.org/abs/2406.16020')
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data['Original Name'].append('mowe_audio')
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data['Proper Display Name'].append('MOWE-Audio')
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data['Link'].append('https://arxiv.org/abs/2409.06635')
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data['Original Name'].append('qwen_audio_chat')
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data['Proper Display Name'].append('Qwen-Audio-Chat')
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data['Link'].append('https://arxiv.org/abs/2311.07919')
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data['Original Name'].append('meralion_audiollm_v1_lora')
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data['Proper Display Name'].append('MERaLion-AudioLLM-v1-LoRA')
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data['Link'].append('https://www.a-star.edu.sg/i2r/research/I2RTechs/research/i2r-techs-solutions/unlocking-the-potential-of-large-language-models-(llms)-with-i-r-s-merlion-ai')
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data['Original Name'].append('meralion_audiollm_v1_mse')
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data['Proper Display Name'].append('MERaLion-AudioLLM-v1-MSE')
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data['Link'].append('https://www.a-star.edu.sg/i2r/research/I2RTechs/research/i2r-techs-solutions/unlocking-the-potential-of-large-language-models-(llms)-with-i-r-s-merlion-ai')
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data['Original Name'].append('stage2_whisper3_fft_mlp100_gemma2_9b_lora')
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data['Proper Display Name'].append('Stage2-Whisper3-FFT-MLP100-Gemma2-9B-LoRA')
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data['Link'].append('https://www.a-star.edu.sg/i2r/research/I2RTechs/research/i2r-techs-solutions/unlocking-the-potential-of-large-language-models-(llms)-with-i-r-s-merlion-ai')
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def get_dataframe():
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"""
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Returns a DataFrame with the data and drops rows with missing values.
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"""
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df = pd.DataFrame(data)
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return df.dropna(axis=0)
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