Awesome

Styleformer

A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more.For instance, understand What makes text formal or casual/informal.

Usecases for Styleformer
Installation
Quick Start
Knobs
Models
Dataset
Benchmark
Streamlit Demo
References
Citation

Usecases for Styleformer

Area 1: Data Augmentation

Augment training datasets with various fine-grained language styles.

Area 2: Post-processing

Apply style transfers to machine generated text.
e.g.
- Refine a Summarised text to active voice + formal tone.
- Refine a Translated text to more casual tone to reach younger audience.

Area 3: Controlled paraphrasing

Formal <=> Casual and Active <=> style transfers adds a notion of control over how we paraphrase when compared to free-form paraphrase where there is control or guarantee over the paraphrases.

Area 4: Assisted writing

Integrate this to any human writing interfaces like email clients, messaging tools or social media post authoring tools. Your creativity is your limit to te uses.
e.g.
- Polish an email with business tone for professional uses.

Installation

pip install git+https://github.com/PrithivirajDamodaran/Styleformer.git

Quick Start

~~python [IMPORTANT] If you are using in notebook, use the below line to login: from huggingface_hub import notebook_login notebook_login() else use: huggingface-cli login~~

Casual to Formal (Available now !)

from styleformer import Styleformer
import torch
import warnings
warnings.filterwarnings("ignore")

'''
#uncomment for re-producability
def set_seed(seed):
  torch.manual_seed(seed)
  if torch.cuda.is_available():
    torch.cuda.manual_seed_all(seed)

set_seed(1234)
'''

# style = [0=Casual to Formal, 1=Formal to Casual, 2=Active to Passive, 3=Passive to Active etc..]
sf = Styleformer(style = 0) 

source_sentences = [
"I am quitting my job",
"Jimmy is on crack and can't trust him",
"What do guys do to show that they like a gal?",
"i loooooooooooooooooooooooove going to the movies.",
"That movie was fucking awesome",
"My mom is doing fine",
"That was funny LOL" , 
"It's piece of cake, we can do it",
"btw - ur avatar looks familiar",
"who gives a crap?",
"Howdy Lucy! been ages since we last met.",
"Dude, this car's dope!",
"She's my bestie from college",
"I kinda have a feeling that he has a crush on you.",
"OMG! It's finger-lickin' good.",
]   

for source_sentence in source_sentences:
    target_sentence = sf.transfer(source_sentence)
    print("-" *100)
    print("[Casual] ", source_sentence)
    print("-" *100)
    if target_sentence is not None:
        print("[Formal] ",target_sentence)
        print()
    else:
        print("No good quality transfers available !")

[Casual]  I am quitting my job
[Formal]  I will be stepping down from my job.
----------------------------------------------------------------------------------------------------
[Casual]  Jimmy is on crack and can't trust him
[Formal]  Jimmy is a crack addict I cannot trust him
----------------------------------------------------------------------------------------------------
[Casual]  What do guys do to show that they like a gal?
[Formal]  What do guys do to demonstrate their affinity for women?
----------------------------------------------------------------------------------------------------
[Casual]  i loooooooooooooooooooooooove going to the movies.
[Formal]  I really like to go to the movies.
----------------------------------------------------------------------------------------------------
[Casual]  That movie was fucking awesome
[Formal]  That movie was wonderful.
----------------------------------------------------------------------------------------------------
[Casual]  My mom is doing fine
[Formal]  My mother is doing well.
----------------------------------------------------------------------------------------------------
[Casual]  That was funny LOL
[Formal]  That was hilarious
----------------------------------------------------------------------------------------------------
[Casual]  It's piece of cake, we can do it
[Formal]  The whole process is simple and is possible.
----------------------------------------------------------------------------------------------------
[Casual]  btw - ur avatar looks familiar
[Formal]  Also, your avatar looks familiar.
----------------------------------------------------------------------------------------------------
[Casual]  who gives a crap?
[Formal]  Who cares?
----------------------------------------------------------------------------------------------------
[Casual]  Howdy Lucy! been ages since we last met.
[Formal]  Hello, Lucy It has been a long time since we last met.
----------------------------------------------------------------------------------------------------
[Casual]  Dude, this car's dope!
[Formal]  I find this car very appealing.
----------------------------------------------------------------------------------------------------
[Casual]  She's my bestie from college
[Formal]  She is my best friend from college.
----------------------------------------------------------------------------------------------------
[Casual]  I kinda have a feeling that he has a crush on you.
[Formal]  I have a feeling that he is attracted to you.
----------------------------------------------------------------------------------------------------
[Casual]  OMG! It's finger-lickin' good.
[Formal]  It is so good, it is delicious.
----------------------------------------------------------------------------------------------------

Formal to Casual (Available now !)

from styleformer import Styleformer
import warnings
warnings.filterwarnings("ignore")

# style = [0=Casual to Formal, 1=Formal to Casual, 2=Active to Passive, 3=Passive to Active etc..]
sf = Styleformer(style = 1) 
import torch
def set_seed(seed):
  torch.manual_seed(seed)
  if torch.cuda.is_available():
    torch.cuda.manual_seed_all(seed)

set_seed(1212)

source_sentences = [
"I would love to meet attractive men in town",
"Please leave the room now",
"It is a delicious icecream",
"I am not paying this kind of money for that nonsense",
"He is on cocaine and he cannot be trusted with this",
"He is a very nice man and has a charming personality",
"Let us go out for dinner",
"We went to Barcelona for the weekend. We have a lot of things to tell you.",
]   

for source_sentence in source_sentences:
    # inference_on = [-1=Regular model On CPU, 0-998= Regular model On GPU, 999=Quantized model On CPU]
    target_sentence = sf.transfer(source_sentence, inference_on=-1, quality_filter=0.95, max_candidates=5)
    print("[Formal] ", source_sentence)
    if target_sentence is not None:
        print("[Casual] ",target_sentence)
    else:
        print("No good quality transfers available !")
    print("-" *100)

[Formal]  I would love to meet attractive men in town
[Casual]  i want to meet hot guys in town
----------------------------------------------------------------------------------------------------
[Formal]  Please leave the room now
[Casual]  leave the room now.
----------------------------------------------------------------------------------------------------
[Formal]  It is a delicious icecream
[Casual]  It is a yummy icecream
----------------------------------------------------------------------------------------------------
[Formal]  I am not paying this kind of money for that nonsense
[Casual]  But I'm not paying this kind of money for that crap
----------------------------------------------------------------------------------------------------
[Formal]  He is on cocaine and he cannot be trusted with this
[Casual]  he is on coke and he can't be trusted with this
----------------------------------------------------------------------------------------------------
[Formal]  He is a very nice man and has a charming personality
[Casual]  he is a really nice guy with a cute personality.
----------------------------------------------------------------------------------------------------
[Formal]  Let us go out for dinner
[Casual]  let's hang out for dinner.
----------------------------------------------------------------------------------------------------
[Formal]  We went to Barcelona for the weekend. We have a lot of things to tell you.
[Casual]  hehe..we went to barcelona for the weekend..we got a lot of things to tell ya..
----------------------------------------------------------------------------------------------------

Active to Passive (Available now !)

# style = [0=Casual to Formal, 1=Formal to Casual, 2=Active to Passive, 3=Passive to Active etc..]
sf = Styleformer(style = 2)

Passive to Active (Available now !)

# style = [0=Casual to Formal, 1=Formal to Casual, 2=Active to Passive, 3=Passive to Active etc..]
sf = Styleformer(style = 3)

Knobs

# inference_on = [-1=Regular model On CPU, 0-998= Regular model On GPU, 999=Quantized model On CPU]
target_sentence = sf.transfer(source_sentence, inference_on=-1, quality_filter=0.95, max_candidates=5)

Models

Model	Type	Status
prithivida/informal_to_formal_styletransfer	Seq2Seq	Beta
prithivida/formal_to_informal_styletransfer	Seq2Seq	Beta
prithivida/active_to_passive_styletransfer	Seq2Seq	Beta
prithivida/passive_to_active_styletransfer	Seq2Seq	Beta
prithivida/positive_to_negative_styletransfer	Seq2Seq	WIP
prithivida/negative_to_positive_styletransfer	Seq2Seq	WIP

Dataset

The casual <=> formal dataset was generated using ideas mentioned in reference paper 1
The positive <=> negative dataset was generated using ideas mentioned in reference paper 3
Fined tuned on T5 on a Tesla T4 GPU and it took ~2 hours to train each of the above models with batch_size = 16 and epochs = 5.(Will share training args shortly)

Benchmark

Streamlit Demo

pip install streamlit
streamlit run streamlit_app.py