Update README.md

README.md

@@ -31,103 +31,77 @@ model-index:
 pipeline_tag: text2text-generation
 ---
 
-Model Name: Evolutionary Multi-Modal Model
+# Model Card for Evolutionary Multi-Modal Model
 
-Model Type: Transformer
+## Model Details
 
-License: MIT
+### Model Description
 
-Language: English, Chinese
+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.
 
-Datasets: Custom
+--
+**Developed by:** Independent researcher
+**Funded by :** Self-funded
+**Shared by :** Independent researcher
+**Model type:** MEvolutionary Multi-Modal Model
+**Language(s) (NLP):** English zh
+**License:** Apache-2.0
+**Finetuned from model :** None
 
-Tags:
+### Model Sources
 
-Text Generation
+- **Repository:** [https://huggingface.co/zeroMN/SG1.0](https://huggingface.co/zeroMN/SG1.0)
+- **Paper:** [Paper Title](https://arxiv.org/abs/your-paper-id) (if applicable)
+- **Demo:** [https://huggingface.co/spaces/zeroMN/zeroMN-SG1.0](https://huggingface.co/spaces/zeroMN/zeroMN-SG1.0) (if applicable)
 
-Code Generation
+## Useshttps://huggingface.co/spaces/zeroMN/zeroMN-Evolutionary Multi-Modal Model
 
-Speech Recognition
+### Direct Use
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-Multi-Modal
+model = AutoModelForSeq2SeqLM.from_pretrained("zeroMN/SHMT")
+tokenizer = AutoTokenizer.from_pretrained("zeroMN/SHMT")
 
-Evolutionary
+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)
 
-Base Model: Facebook/BART-Base
+print(generated_text)
+```
+### Downstream Use
 
-Finetuned From: GPT-2, BERT-Base-Uncased, Facebook/wav2vec2-base-960h, OpenAI/CLIP-ViT-Base-Patch32
+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.
 
-Dataset: Custom Multi-Modal Dataset
+### Out-of-Scope Use
 
-Metrics
-Perplexity
+The `Evolutionary Multi-Modal Model` 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.
 
-BLEU
+## Bias, Risks, and Limitations
 
-WER
+### Recommendations
 
-CER
+Users (both direct and downstream) should be made aware of the following risks, biases, and limitations:
 
-Library Name
-Transformers
+- **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.
 
-Pipeline Tag
-Text Generation
+## How to Get Started with the Model
 
-Inference Parameters
-Max Length: 50
+Use the code below to get started with the `SG1.0.pth` model.
 
-Top K: 50
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-Top P: 0.95
+model = AutoModelForSeq2SeqLM.from_pretrained("zeroMN/SHMT")
+tokenizer = AutoTokenizer.from_pretrained("zeroMN/SHMT")
 
-Temperature: 1.2
+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)
 
-Do Sample: True
-
-Speech Recognition
-Waveform Path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
-
-Task: "speech_recognition"
-
-Output Audio Key: "Transcription"
-
-Text Generation
-Input Text: "What is the future of AI?"
-
-Task: "text_generation"
-
-Output Text Key: "Generated Text"
-
-Code Generation
-Input Code: "def add(a, b): return"
-
-Task: "code_generation"
-
-Output Code Key: "Generated Code"
-
-Tests
-Name: Speech Recognition Test
-
-Waveform Path: "C:/Users/baby7/Desktop/权重参数/sample-15s.wav"
-
-Expected Output: "Expected transcription"
-
-Name: Text Generation Test
-
-Input Text: "What is the future of AI?"
-
-Expected Output: "Predicted text about AI"
-
-Name: Code Generation Test
-
-Input Code: "def add(a, b): return"
-
-Expected Output: "def add(a, b): return a + b"
-
-Extra Information
-Author: Zero
-
-Version: 1.0
-
-Description: This Evolutionary Multi-Modal Model is designed for tasks like text generation, code generation, speech recognition, and vision understanding. It leverages the capabilities of multiple pre-trained models and applies evolutionary techniques to optimize performance across these tasks.
+print(generated_text)
+```