Update README.md

README.md

@@ -39,53 +39,6 @@ pipeline_tag: text-generation
 ##     Multi-Modal Model
 # Model Card for Evolutionary
 
-## 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.