doc(README): fix typo's

README.md

@@ -25,11 +25,11 @@ The system relies on the Flax/JAX infrastructure, which are ideal for TPU traini
 
 The main components of the architecture include:
 
-* An encoder, based on [BART](https://arxiv.org/abs/1910.13461). The encoder's mission is to transform a sequence of input text tokens to a sequence of image tokens. The input tokens are extracted from the text prompt by using the model's tokenizer. The image tokens are a fixed-length sequence, and they represent indices in a VQGAN-based pre-trained codebook.
+* An encoder, based on [BART](https://arxiv.org/abs/1910.13461). The encoder transforms a sequence of input text tokens to a sequence of image tokens. The input tokens are extracted from the text prompt by using the model's tokenizer. The image tokens are a fixed-length sequence, and they represent indices in a VQGAN-based pre-trained codebook.
 
-* A decoder, with converts the image tokens to an image for visualization. As mentioned above, the decoder is based on a [VQGAN model](https://compvis.github.io/taming-transformers/).
+* A decoder, which converts the image tokens to image pixels. As mentioned above, the decoder is based on a [VQGAN model](https://compvis.github.io/taming-transformers/).
 
-The model definition we use for the encoder can be downloaded from our [Github repo](https://github.com/borisdayma/dalle-mini). The encoder is reprensented by the class `CustomFlaxBartForConditionalGeneration`.
+The model definition we use for the encoder can be downloaded from our [Github repo](https://github.com/borisdayma/dalle-mini). The encoder is represented by the class `CustomFlaxBartForConditionalGeneration`.
 
 To use the decoder, you need to follow the instructions in our accompanying VQGAN model in the hub, [flax-community/vqgan_f16_16384](https://huggingface.co/flax-community/vqgan_f16_16384).
 
@@ -37,8 +37,8 @@ To use the decoder, you need to follow the instructions in our accompanying VQGA
 
 The easiest way to get familiar with the code and the models is to follow the inference notebook we provide in our [github repo](https://github.com/borisdayma/dalle-mini/blob/main/dev/inference/inference_pipeline.ipynb). For your convenience, you can open it in Google Colaboratory: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/dalle-mini/blob/main/dev/inference/inference_pipeline.ipynb)
 
-If you just want to test the trained model and see what it comes up with, please visit [our demo](https://huggingface.co/spaces/flax-community/dalle-mini), available as a Space in huggingface's hub.
+If you just want to test the trained model and see what it comes up with, please visit [our demo](https://huggingface.co/spaces/flax-community/dalle-mini), available in 🤗 Spaces.
 
 ### Additional Details
 
-Our [report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA) contains a lot of details about how the model was trained and shows many examples that demonstrate its capabilities.
+Our [report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA) contains more details about how the model was trained and shows many examples that demonstrate its capabilities.