Update README.md

README.md

@@ -34,7 +34,52 @@ pipeline_tag: text2text-generation
 # Model Card for Evolutionary Multi-Modal Model
 
 ## Model Details
+model_name: Evolutionary Multi-Modal Model
+model_type: transformer
+license: mit
+language: en zh
+datasets:
+  - "Custom"
+tags:
+  - text-generation
+  - code-generation
+  - speech-recognition
+  - multi-modal
+  - evolutionary
+base_model: facebook/bart-base
+finetuned_from: gpt2, bert-base-uncased, facebook/wav2vec2-base-960h, openai/clip-vit-base-patch32
+dataset: Custom Multi-Modal Dataset
 
+metrics:
+  - perplexity
+  - bleu
+  - wer
+  - cer
+
+library_name: transformers
+pipeline_tag: text-generation
+inference:
+  parameters:
+    max_length: 50
+    top_k: 50
+    top_p: 0.95
+    temperature: 1.2
+    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"
 ### 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.