zeroMN/SHMT

Model Sources

You need to use separate code, audio, text, and natural language together with the model. Because the model will use separate word segmenters and vocabularies to achieve the best results when dealing with special cases.

• Repository: https://huggingface.co/zeroMN/SHMT
• kaggle: [https://www.kaggle.com/models/zeroeva/evolutionary-multi-modal) (https://www.kaggle.com/models/zeroeva/evolutionary-multi-modal)
• Demo: https://huggingface.co/spaces/zeroMN/zeroMN-SHMT

Multi-Modal Model

Model Card for Evolutionary

-- ###############################################

Model breast_cancer_wisconsin_original test

from ucimlrepo import fetch_ucirepo 
fetch dataset 
breast_cancer_wisconsin_original = fetch_ucirepo(id=15) 
  
data (as pandas dataframes) 
X = breast_cancer_wisconsin_original.data.features 
y = breast_cancer_wisconsin_original.data.targets 
 
metadata 
print(breast_cancer_wisconsin_original.metadata) 
 
variable information 
print(breast_cancer_wisconsin_original.variables) 

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0 0.93 0.99 0.96 79

1 0.98 0.90 0.94 58

-- #accuracy 0.95 137

-- This model, named Evolutionary Multi-Modal Model, is a multimodal transformer designed to handle a variety of tasks including vision and audio processing. It is built on top of the adapter-transformers and transformers libraries and is intended to be a versatile base model for both direct use and fine-tuning.

-- Developed by: Independent researcher Funded by : Self-funded Shared by : Independent researcher Model type: Multimodal Language(s) (NLP): English zh License: Apache-2.0 Finetuned from model : None

Uses：https://huggingface.co/zeroMN/SHMT

Direct Use

git lfs install

git clone https://huggingface.co/zeroMN/SHMT.git

Downstream Use

The model can be fine-tuned for specific tasks such as visual question answering (VQA), image captioning, and audio recognition.

Out-of-Scope Use

The Evolved Multimodal Model is not suitable for tasks that require high expertise or domain-specific expertise beyond its current capabilities. The number of speech frames still needs to be fine-tuned by yourself.

Bias, Risks, and Limitations

Recommendations

Users (both direct and downstream) should be made aware of the following risks, biases, and limitations:

• Bias: The model may exhibit biases present in the training data, particularly if the data is not representative of all populations.
• Risks: The model should not be used in critical applications where high accuracy and reliability are required without thorough testing and validation.
• Limitations: The model may not perform well on tasks that require fine-grained recognition or highly specialized audio processing.

How to Get Started with the Model

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zeroMN/SHMT")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zeroMN/SHMT")