README.md

metadata

language:
  - en
  - zh
license: apache-2.0
library_name: transformers
tags:
  - multimodal
  - vqa
  - text
  - audio
datasets:
  - synthetic-dataset
metrics:
  - accuracy
  - bleu
  - wer
model-index:
  - name: Evolutionary Multi-Modal Model
    results:
      - task:
          type: vqa
          name: Visual Question Answering
        dataset:
          type: synthetic-dataset
          name: Synthetic Multimodal Dataset
          split: test
        metrics:
          - type: accuracy
            value: 85
pipeline_tag: text-generation

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 Description

-- 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

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("zeroMN/SHMT")
tokenizer = AutoTokenizer.from_pretrained("zeroMN/SHMT")

input_text = "Tell me a joke."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)

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 the code below to get started with the SG1.0.pth model.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("zeroMN/SHMT")
tokenizer = AutoTokenizer.from_pretrained("zeroMN/SHMT")

input_text = "Tell me a joke."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)