Skip to main content

Transformers kit - NLP library for different downstream tasks, built on huggingface project

Project description

🤖 TFKit - Transformer Kit 🤗

NLP library for different downstream tasks, built on huggingface 🤗 project,
for developing wide variety of nlp tasks.

Feature

  • support Bert/GPT/GPT2/XLM/XLNet/RoBERTa/CTRL/ALBert
  • modularize data loading
  • easy to modify
  • special loss function for handling different cases: FocalLoss/ FocalBCELoss/ NegativeCrossEntropyLoss/ SmoothCrossEntropyLoss
  • eval on different benchmark - EM / F1 / BLEU / METEOR / ROUGE / CIDEr / Classification Report / ...
  • multi-class multi-task multi-label classifier
  • word/sentence level text generation
  • support beamsarch on decoding
  • token tagging

Package Overview

tfkit NLP library for different downstream tasks, built on huggingface project
tfkit.classifier multi-class multi-task multi-label classifier
tfkit.gen_once text generation in one time built on masklm model
tfkit.gen_onebyone text generation in one word by one word built on masklm model
tfkit.tag token tagging model
tfkit.train.py Run training
tfkit.eval.py Run evaluation

Installation

TFKit requires Python 3.6 or later.

Installing via pip

pip install tfkit

Running TFKit

Once you've installed TFKit, you can run train.py for training or eval.py for evaluation.

$ tfkit-train
Run training

arguments:
  --train       training data path       
  --valid       validation data path       
  --maxlen      maximum text length       
  --model       type of model         ['once', 'onebyone', 'classify', 'tagRow', 'tagCol']
  --config      pre-train model       bert-base-multilingual-cased

optional arguments:
  -h, --help    show this help message and exit
  --resume      resume from previous training
  --savedir     dir for model saving
  --worker      number of worker
  --batch       batch size
  --lr          learning rate
  --epoch       epoch rate
  --tensorboard enable tensorboard
  --cache       enable data caching
$ tfkit-eval
Run evaluation on different benchmark
arguments:
  --model       model for evaluate       
  --valid       validation data path        
  --metric      metric for evaluate         ['em', 'nlg', 'classification']
  --config      pre-train model             bert-base-multilingual-cased

optional arguments:
  -h, --help    show this help message and exit
  --batch       batch size
  --topk        select top k result in classification task 
  --outprint    enable printing result in console
  --beamsearch  enable beamsearch for text generation task

Dataset format

once

csv file with 2 row - input, target
each token separate by space
no header needed
Example:

"i go to school by bus","我 坐 巴 士 上 學"

onebyone

csv file with 2 row - input, target
each token separate by space
no header needed
Example:

"i go to school by bus","我 坐 巴 士 上 學"

classify

csv file with header
header - input,task1,task2...taskN
if some task have multiple label, use / to separate each label - label1/label2/label3
Example:

SENTENCE,LABEL,Task2
"The prospective ultrasound findings were correlated with the final diagnoses , laparotomy findings , and pathology findings .",outcome/other,1

tagRow

csv file with 2 row - input, target
each token separate by space
no header needed
Example:

"在 歐 洲 , 梵 語 的 學 術 研 究 , 由 德 國 學 者 陸 特 和 漢 斯 雷 頓 開 創 。 後 來 威 廉 · 瓊 斯 發 現 印 歐 語 系 , 也 要 歸 功 於 對 梵 語 的 研 究 。 此 外 , 梵 語 研 究 , 也 對 西 方 文 字 學 及 歷 史 語 言 學 的 發 展 , 貢 獻 不 少 。 1 7 8 6 年 2 月 2 日 , 亞 洲 協 會 在 加 爾 各 答 舉 行 。 [SEP] 陸 特 和 漢 斯 雷 頓 開 創 了 哪 一 地 區 對 梵 語 的 學 術 研 究 ?",O A A O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

tagCol

csv file with 2 row - input, target
each token separate by space
no header needed
Example:

別 O
只 O
能 R
想 O
自 O
己 O
, O
想 M
你 M
周 O
圍 O
的 O
人 O
。 O

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for tfkit, version 0.1.4
Filename, size File type Python version Upload date Hashes
Filename, size tfkit-0.1.4-py3-none-any.whl (31.6 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size tfkit-0.1.4.tar.gz (19.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page