Update README.md

README.md

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+### Introduction
+The foundational technology for generative prompt models are Language-Image pretraining models such as CLIP (Contrastive Language-Image Pre-Training) which aligned Language-Image latent of image and text encoder. We can apply latent vector for zero-short classification and image searching. For generative prompt models, we can train generative model using frozen image encoder and then replace image encoder with text encoder to be a prompt of generative model in the inference pipeline. 
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+**Scope of work**
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+From limited of computing resources, datasets, engineers we purpose to train CLIP model with 2 stage training of CLIP model
+- **Stage 1:** Language encoder distillation training
+We will train Thai (or Bilingual EN-TH) text encoder with original CLIP encoder following Multilingual-CLIP using EN-EN, EN-TH text pairs of machine translation datasets.
+- **Stage 2:** Continue CLIP pretraining with frozen image encoder
+Distillation training model may not understand all of token especially specific words. We have to continue CLIP (or LiT, or SigLiT) pretraining with frozen image encoder to learn details of specific words.
+After we have our own CLIP model we will replace CLIP application text encoder with our own text encoder or we may finetuning application model to push performance of our model.
+
 ## How to use
 - #### Install python package
 ```python
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 ### Authors
 * Konthee Boonmeeprakob ([email protected])
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