Update README.md

README.md

@@ -6,10 +6,9 @@ RUDOLPH: One Hyper-Tasking Transformer Can be Creative as DALL-E and GPT-3 and S
 
 This is a fine-tuned version of the pre-trained [RuDOLPH 2.7B model](https://huggingface.co/sberbank-ai/RuDOLPH-2.7B). Model was trained by [Sber AI](https://github.com/ai-forever) and [AIRI](https://airi.net) teams.
 
-
 # Model Description
 
-**RU**ssian **D**ecoder **O**n **L**anguage **P**icture **H**yper-tasking (**RUDOLPH**) **2.7B** is the largest text-image-text transformer designed for an easy fine-tuning for a number of tasks: from generating images by text description and image classification to visual question answering and more. This model demonstrates the power of Hyper-tasking Transformers.
+**RU**ssian **D**ecoder **O**n **L**anguage **P**icture **H**yper-tasking (**RUDOLPH**) **2.7B** is the largest text-image-text transformer designed for an easy fine-tuning for a range of tasks: from generating images by text description and image classification to visual question answering and more. This model demonstrates the power of Hyper-tasking Transformers.
 
 *Hyper-tasking means generalized multi-tasking, e.g., the model that can solve almost all tasks within supported modalities (two modalities in case of RUDOLPH: images and Russian texts).*
 
@@ -22,26 +21,35 @@ This is a fine-tuned version of the pre-trained [RuDOLPH 2.7B model](https://hug
 
 The model was prepared as a baseline for FusionBrain Challenge 2.0 (as a part of AI Journey Contest 2022) and is a fine-tuned version of the pre-trained [RuDOLPH 2.7B model](https://huggingface.co/sberbank-ai/RuDOLPH-2.7B) using 6 tasks:
 
-* Text QA – SberQUaD dataset.
-* Math QA – DeepMind Mathematics Dataset.
-* Image Captioning – COCO dataset (with automated translation).
-* Image Generation – COCO dataset (with automated translation).
-* VQA – COCO dataset with prepared question set.
-* Text Recognition in the Wild
+* Text QA – [SberQUaD dataset](https://huggingface.co/datasets/sberquad).
+* Math QA – [DeepMind Mathematics Dataset](https://github.com/deepmind/mathematics_dataset).
+* Image Captioning – [COCO dataset](https://cocodataset.org/#home) (with automated translation).
+* Image Generation – [COCO dataset](https://cocodataset.org/#home) (with automated translation).
+* VQA12-layer, 768-hidden, 12-heads, 110M parameters.[COCO dataset](https://cocodataset.org/#home) with prepared question set.
+* Text Recognition in the Wild – the dataset consisting of synthetic and real-world human-annotated data for text recognition task.
 
 # Details of architecture
 
 ### Parameters
 
+![rudolph27b_masks.png](https://s3.amazonaws.com/moonup/production/uploads/1663662426135-5f91b1208a61a359f44e1851.png)
+
+The maximum sequence length that this model may be used with depends on the modality and stands for 384 - 576 - 128 for the left text tokens, image tokens, and right text tokens, respectively.
+
+RUDOLPH 2.7B is a Transformer-based decoder model with the following parameters:
+
+* num-layers (32) — Number of hidden layers in the Transformer decoder.
+* hidden-size (2560) — Dimensionality of the hidden layers.
+* num_attention_heads (32) — Number of attention heads for each attention layer.
 
 ### Sparse Attention Mask
 
 The primary proposed method is to modify the sparse transformer's attention mask to better control modalities. It allows us to calculate the transitions of modalities in both directions, unlike another similar work DALL-E Transformer, which used only one direction, "text to image". The proposed "image to right text" direction is achieved by extension sparse attention mask to the right for auto-repressively text generation with both image and left text condition.
 
-![rudolph27b_masks.png](https://s3.amazonaws.com/moonup/production/uploads/1663662426135-5f91b1208a61a359f44e1851.png)
+<img src=https://github.com/lizagonch/ru-dolph-fbc2/blob/develop_v1/pics/scheme-rudolph_27B.jpg>
 
 # Authors
 
 + Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)
-+ Liza Goncharova:
-+ Nastia Maltseva:
++ Liza Goncharova: [Github](https://github.com/lizagonch)
++ Nastya Maltseva: [Github](https://github.com/NastyaMittseva)