Model Card for SG0.1.pth
Model Details
Model Description
This model, named SG1.0.pth, 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.
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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
Model Sources
Useshttps://huggingface.co/spaces/zeroMN/zeroMN-SG1.0
Direct Use
The SG1.0.pth model can be used directly for tasks such as image classification, object detection, and audio processing without any fine-tuning. It is designed to handle a wide range of input modalities and can be integrated into various applications.
Downstream Use
The model can be fine-tuned for specific tasks such as visual question answering (VQA), image captioning, and audio recognition. It is particularly useful for multimodal tasks that require understanding both visual and audio inputs.
Out-of-Scope Use
The zeroTT model is not designed for tasks that require highly specialized knowledge or domain-specific expertise beyond its current capabilities. It may not perform well on tasks that require fine-grained recognition or highly specialized audio processing.
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 the code below to get started with the SG1.0.pth model.
1import torch
2
3# Load the model
4model = torch.load('path/to/SG0.1.pth.pth')
5model.eval()
6
7# Example input
8dummy_input = torch.randn(1, 3, 224, 224) # Example input for image processing
9
10# Forward pass
11output = model(dummy_input)
12print(output)
