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
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/PrithivirajDamodaran/Styleformer.git
(削除) 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 (削除ここまで)
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.
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[Casual] Jimmy is on crack and can't trust him
[Formal] Jimmy is a crack addict I cannot trust him
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[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
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[Formal] Please leave the room now
[Casual] leave the room now.
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[Formal] It is a delicious icecream
[Casual] It is a yummy icecream
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[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
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[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
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[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.
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[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 |
| prithivida/positive_to_negative_styletransfer | Seq2Seq | WIP |
| prithivida/negative_to_positive_styletransfer | Seq2Seq | WIP |
- 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)
- TBD
pip install streamlit
streamlit run streamlit_app.py
- Formality Style Transfer for Noisy Text: Leveraging Out-of-Domain Parallel Data for In-Domain Training via POS Masking
- Generative Text Style Transfer for Improved Language Sophistication
- Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
- TBD