semvec/pypi-packages huggingface dataset:
Gubs-Text-Extractor 0.0.0.2
An invoice ocr package
Requires Python: >=3.6

## Data extractor for PDF invoices 

To run the code:

1. Install the package using using the command:  
   `pip install invoiceocr-Gubs`
2. In your script, write:
    `from InvoiceOcrGubs import script`
3. To run the script, run command:
   `python <file_name> --pdf <pdf_name>`
2. A window will open up, select the desired rectangle by dragging with help of   mouse and then press c on keyboard
3. The window will close and the etxt will be showed on the terminal.
rudolph 0.0.1rc10
RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP

[[Colab]](https://colab.research.google.com/drive/1gmTDA13u709OXiAeXWGm7sPixRhEJCga?usp=sharing)


## <img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/rudolph.png?token=GHSAT0AAAAAABQH6MST7ZEGAF274DV33K7KYOYRSBQ" height="60"/> RUDOLPH 🦌🎄☃️

*One Hyper-Tasking Transformer can be creative as DALL-E and GPT-3 and smart as CLIP*

---

**RU**ssian **D**ecoder **O**n **L**anguage **P**icture **H**yper-tasking (**RUDOLPH**) is a 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 model is a generalized multi-tasking model, i.e., the model that can solve almost all tasks within supported modalities, mandatory including mutual pairwise translations between modalities (two modalities in case of RUDOLPH: images and Russian texts).*

![](./pics/scheme.jpg)

# Models
+ [350M (RUDOLPH)](https://huggingface.co/sberbank-ai/RUDOLPH-350M)
+ [1.3B (RUDOLPH)](https://huggingface.co/sberbank-ai/RUDOLPH-1.3B)
+ [2.7B (RUDOLPH)](https://huggingface.co/sberbank-ai/RUDOLPH-2.7B)
+ [2.7B_FBC2 (RUDOLPH-fine-tuned)](https://huggingface.co/sberbank-ai/RUDOLPH-2.7B-FBC2)

The following table shows the values of the parameters corresponding to different RUDOLPH versions.

|     | 350M | 1.3B | 2.7B |
|-----|------|------|------|
| _l_ | 64   | 128  | 384  |
| _r_ | 64   | 128  | 128  |
| _m_ | 16   | 32   | 24   |
| _n_ | 16   | 32   | 24   |

# Sparse Attention Mask

### 350M

`row - col - row - [last] conv`

![](./pics/attention_masks_350m.png)

### 1.3B

`row - col - row - [last] conv`

![](./pics/attention_masks_1300m.png)

### 2.7B

`row - col - row - [last] conv`

![](./pics/attention_masks_2700m.png)

# Installing
```
pip install rudolph==0.0.1rc10
```
# Usage and Fine-Tuning

Usage and fine-tuning examples for different versions of RUDOLPH can be found in jupyters folder.

# Citation

```
@misc{github2022ruDolph,
  title         = {RUDOLPH: One Hyper-Tasking Transformer can be creative as DALL-E and GPT-3 and smart as CLIP},
  author        = {AIRI},
  year          = {2022},
  howpublished  = {\url{https://github.com/ai-forever/ru-dolph}},
}
```

# Supported by

[<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/logo/airi-logo.png" height="50"/>](https://airi.net)
ustr 0.0.2
contains ustr UnderlineString
Requires Python: >=3.7

# xstr

#### New python3 strings just dropped. 

# Installation

```bash
pip3 install xstr
```

```python3
from xstr import ustr
from xtr import dstr
from xstr import hstr
```

or

```python3

import xstr

```

or 

```python3
import hstr
# import dstr
# import ustr
```


# HypenString  --    hstr()

```python3
from xstr import hstr


x = hstr('hello new world and all who inhabit it.')
print(x)
# hello-new-world-and-all-who-inhabit-it.
print(x.original())
# hello new world and all who inhabit it.

```

output


```output
hello-new-world-and-all-who-inhabit-it.
hello new world and all who inhabit it.
```


# DotString   --  dstr()  

```python3
from xstr import dstr


x = dstr('hello new world and all who inhabit it.')
print(x)
# hello.new.world.and.all.who.inhabit.it.
print(x.original())
# hello new world and all who inhabit it.

```


output


```output
hello.new.world.and.all.who.inhabit.it.
hello new world and all who inhabit it.
```

# UnderlineString --  ustr()  

```python3
from xstr import ustr


x = ustr('hello new world and all who inhabit it.')
print(x)
# hello_new_world_and_all_who_inhabit_it.
print(x.original())
# hello new world and all who inhabit it.

```

output


```output
hello_new_world_and_all_who_inhabit_it.
hello new world and all who inhabit it.
```
meihua-xinyi 2022.10.7.0
梅花新易
Requires Python: >=3.6

# 梅花新易

## 下载

### Docker

```
docker pull apachecn0/meihua-xinyi
docker run -tid -p <port>:80 apachecn0/meihua-xinyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install meihua-xinyi
meihua-xinyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g meihua-xinyi
meihua-xinyi <port>
# 访问 http://localhost:{port} 查看文档
```
longzhi-liliang-liaoyu-tutengka-shuomingshu 2022.10.7.0
龙之力量疗愈图腾卡说明书
Requires Python: >=3.6

# 龙之力量疗愈图腾卡说明书

## 下载

### Docker

```
docker pull apachecn0/longzhi-liliang-liaoyu-tutengka-shuomingshu
docker run -tid -p <port>:80 apachecn0/longzhi-liliang-liaoyu-tutengka-shuomingshu
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install longzhi-liliang-liaoyu-tutengka-shuomingshu
longzhi-liliang-liaoyu-tutengka-shuomingshu <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g longzhi-liliang-liaoyu-tutengka-shuomingshu
longzhi-liliang-liaoyu-tutengka-shuomingshu <port>
# 访问 http://localhost:{port} 查看文档
```
liuren-zhanyan-xuanzhu 2022.10.7.0
六壬占验选注
Requires Python: >=3.6

# 六壬占验选注

## 下载

### Docker

```
docker pull apachecn0/liuren-zhanyan-xuanzhu
docker run -tid -p <port>:80 apachecn0/liuren-zhanyan-xuanzhu
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install liuren-zhanyan-xuanzhu
liuren-zhanyan-xuanzhu <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g liuren-zhanyan-xuanzhu
liuren-zhanyan-xuanzhu <port>
# 访问 http://localhost:{port} 查看文档
```
liuren-shikong 2022.10.7.0
六壬时空
Requires Python: >=3.6

# 六壬时空

## 下载

### Docker

```
docker pull apachecn0/liuren-shikong
docker run -tid -p <port>:80 apachecn0/liuren-shikong
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install liuren-shikong
liuren-shikong <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g liuren-shikong
liuren-shikong <port>
# 访问 http://localhost:{port} 查看文档
```