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.
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.
pip install git+https://github.com/SuperBruceJia/Styleformer.gitfrom 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.
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[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?
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[Casual] i loooooooooooooooooooooooove going to the movies.
[Formal] I really like to go to the movies.
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[Casual] That movie was fucking awesome
[Formal] That movie was wonderful.
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[Casual] My mom is doing fine
[Formal] My mother is doing well.
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[Casual] That was funny LOL
[Formal] That was hilarious
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[Casual] It's piece of cake, we can do it
[Formal] The whole process is simple and is possible.
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[Casual] btw - ur avatar looks familiar
[Formal] Also, your avatar looks familiar.
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[Casual] who gives a crap?
[Formal] Who cares?
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[Casual] Howdy Lucy! been ages since we last met.
[Formal] Hello, Lucy It has been a long time since we last met.
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[Casual] Dude, this car's dope!
[Formal] I find this car very appealing.
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[Casual] She's my bestie from college
[Formal] She is my best friend from college.
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[Casual] I kinda have a feeling that he has a crush on you.
[Formal] I have a feeling that he is attracted to you.
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[Casual] OMG! It's finger-lickin' good.
[Formal] It is so good, it is delicious.
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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.
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[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..
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# style = [0=Casual to Formal, 1=Formal to Casual, 2=Active to Passive, 3=Passive to Active etc..]
sf = Styleformer(style = 2) # style = [0=Casual to Formal, 1=Formal to Casual, 2=Active to Passive, 3=Passive to Active etc..]
sf = Styleformer(style = 3) # 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)| 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 |
- 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)
pip install streamlit
streamlit run streamlit_app.py