semvec/pypi-packages huggingface dataset:
InkCode 1.0
Coloring and formatting text in ANSI-compatible terminals
Requires Python: >=3.11

# InkCode - ANSI Text Colorizer and Formatter

`InkCode` is a Python function that colorizes and formats text for ANSI-compatible terminals. It provides a simple way to enhance the appearance of text by adding color and formatting options.

## Usage

```python
from inkcode import ink_text

text = "Hello, world!"
color = "green"
format = "bold"

result = ink_text(text, color, format)
print(result)
```

This example will print the text "Hello, world!" in bold green.

## Parameters

- `text` (str): The text to be colorized and formatted.
- `color` (str): The desired text color. Available colors are black, red, green, yellow, blue, magenta, cyan, and white.
- `format` (str, optional): The desired text format. Available formats are bold, faint, italic, underline, blink, reverse, crossed_out, double_underline, framed, encircled, overlined, and reset_all. Defaults to None.

## Return Value

The function returns a string containing the colorized and formatted text.

## Examples

```python
# Colorize text in red
red_text = ink_text("Error!", "red")
print(red_text)

# Colorize text in red another way and format with bold
print(ink_text("Error!", "red", "bold"))

# Format text with underline and color it blue
blue_underline_text = ink_text("Important", "blue", "underline")
print(blue_underline_text)
```

## Notes

- This function uses ANSI escape codes to apply color and formatting. It may not work properly in environments that do not support ANSI escape codes.

Feel free to use and modify this function to suit your needs. If you encounter any issues or have suggestions for improvement, please let us know.

**Note**: Make sure to handle exceptions appropriately when using this function to prevent unexpected behavior.
taskbots 0.1.0
A package for developing Task Bots
Requires Python: >=3.8

# Mask-Predict


### Download model 
Description | Dataset | Model
---|---|---
MASK-PREDICT | [WMT14 English-German] | [download (.tar.bz2)](http://dl.fbaipublicfiles.com/fairseq/models/maskPredict_en_de.tar.gz)
MASK-PREDICT | [WMT14 German-English] | [download (.tar.bz2)](http://dl.fbaipublicfiles.com/fairseq/models/maskPredict_de_en.tar.gz)
MASK-PREDICT | [WMT16 English-Romanian] | [download (.tar.bz2)](http://dl.fbaipublicfiles.com/fairseq/models/maskPredict_en_ro.tar.gz)
MASK-PREDICT | [WMT16 Romanian-English] | [download (.tar.bz2)](http://dl.fbaipublicfiles.com/fairseq/models/maskPredict_ro_en.tar.gz)
MASK-PREDICT | [WMT17 English-Chinese] | [download (.tar.bz2)](http://dl.fbaipublicfiles.com/fairseq/models/maskPredict_en_zh.tar.gz)
MASK-PREDICT | [WMT17 Chinese-English] | [download (.tar.bz2)](http://dl.fbaipublicfiles.com/fairseq/models/maskPredict_zh_en.tar.gz)

### Preprocess

text=PATH_YOUR_DATA

output_dir=PATH_YOUR_OUTPUT

src=source_language

tgt=target_language

model_path=PATH_TO_MASKPREDICT_MODEL_DIR

python preprocess.py --source-lang ${src} --target-lang ${tgt} --trainpref $text/train --validpref $text/valid --testpref $text/test  --destdir ${output_dir}/data-bin  --workers 60  --srcdict ${model_path}/maskPredict_${src}_${tgt}/dict.${src}.txt --tgtdict ${model_path}/maskPredict_${src}_${tgt}/dict.${tgt}.txt

### Train


model_dir=PLACE_TO_SAVE_YOUR_MODEL

python train.py ${output_dir}/data-bin --arch bert_transformer_seq2seq --share-all-embeddings --criterion label_smoothed_length_cross_entropy --label-smoothing 0.1 --lr 5e-4 --warmup-init-lr 1e-7 --min-lr 1e-9 --lr-scheduler inverse_sqrt --warmup-updates 10000 --optimizer adam --adam-betas '(0.9, 0.999)' --adam-eps 1e-6 --task translation_self --max-tokens 8192 --weight-decay 0.01 --dropout 0.3 --encoder-layers 6 --encoder-embed-dim 512 --decoder-layers 6 --decoder-embed-dim 512  --fp16 --max-source-positions 10000 --max-target-positions 10000 --max-update 300000 --seed 0 --save-dir ${model_dir}

### Evaluation


python generate_cmlm.py ${output_dir}/data-bin  --path ${model_dir}/checkpoint_best_average.pt  --task translation_self --remove-bpe --max-sentences 20 --decoding-iterations 10  --decoding-strategy mask_predict

# License
MASK-PREDICT is CC-BY-NC 4.0.
The license applies to the pre-trained models as well.

# Citation

Please cite as:

```bibtex
@inproceedings{ghazvininejad2019MaskPredict,
  title = {Mask-Predict: Parallel Decoding of Conditional Masked Language Models},
  author = {Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
  year = {2019},
}
```