semvec/pypi-packages huggingface dataset:
avania 0.1.8
Pays tribute to Laravel

# Avania Framework

## Development Team

### Founder

[Simon](https://github.com/StarLxa)

### Developers

[Simon](https://github.com/StarLxa),
[Tommy](https://github.com/slboki),

### Creative Minds

[Simon](https://github.com/StarLxa),
[Lionet](https://github.com/lionet1224),
[Tommy](https://github.com/slboki),

### Documentation Maintainer

[Simon](https://github.com/StarLxa),

## Origins

The development of this project stemmed from our experience using Laravel for project development. However, due to limitations in PHP's underlying structure, such as issues with multithreading and low-level code, we encountered some problems that were difficult to resolve. As a result, we decided to switch to Python for development. However, we found that most Python frameworks' usage patterns were relatively outdated and lacked the elegance of Laravel. Therefore, we embarked on developing the Avania framework to address these issues.

## Project Components

- **AVANIA-ORM**: A low-level database connection framework built on MySQL.
- **AVANIA-ROUTE**: A routing system based on Flask and WSGI.
- **AVANIA-MIDDLEWARES**: Middleware for AVANIA-ROUTE.
- **AVANIA-EXCEPTIONS**: Global exception handling.
- **AVANIA-I18N**: Multilingual support, coming soon.
- **AVANIA-CRON**: Scheduled tasks, coming soon.
- **AVANIA-SOCKET**: WebSocket task queue, coming soon.
- **AVANIA-AI**: Includes image recognition and other AI processing libraries, coming soon.
- **AVANIA-MONITOR**: Connection to cameras, coming soon.
- **More**: We also encourage everyone to submit their desired features to the issues section. We value each submission and will respond promptly. Don't hesitate to share your ideas!

## Usage

Please refer to the specific documentation for each module.

---

We welcome everyone to submit more code to maintain this project. Each person whose code submission is approved will be added to the list of developers.

---

Our vision for this project is to surpass Laravel and become even more convenient and powerful.
metaagents 0.1.0
A package for developing Meta Agents
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},
}
```
multiagents 0.1.0
A package for developing Multi-agents
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},
}
```