Following points are covered in this PR:

• Added Streamlit app. (CTF,FTC,ATP,PTA)
• Fixed bug in PTA style transfer

@PrithivirajDamodaran Attaching screenshot of streamlit app for reference. Let me know your suggestions

Following the code snippet for a better understanding of the problem, I am facing.

from styleformer import Styleformer
import torch
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 = 0) 

source_sentences = [
                   "Corruption in african countries hinders economic, political and social development. It is a major obstacle to economic growth, good governance and fundamental freedoms, such as freedom of speech or the right of citizens to hold governments accountable. In addition, corruption affects the lives of individuals, families and communities. The 10th global corruption barometer (gcb) program - in africa, shows that while many people in africa feel that corruption is on the rise in their country, many also feel confident that, as citizens, they can make a difference in the fight against corruption."
]

for paragraph in source_sentences:
    # sentences = sent_tokenize(paragraph)
    sentences = paragraph.split('. ')
    for source_sentence in 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 !")

Program Output

---

[Casual] Corruption in african countries hinders economic, political and social development

[Formal] In African countries, corruption affects economic, political, and social development.

---

[Casual] It is a major obstacle to economic growth, good governance and fundamental freedoms, such as freedom of speech or the right of citizens to hold governments accountable

[Formal] It's a major obstacle to economic growth, good governance, and fundamental freedoms, such as the freedom of speech or the right of citizens to

---

[Casual] In addition, corruption affects the lives of individuals, families and communities

[Formal] Additionally, corruption has a negative impact on individuals, families and communities.

---

[Casual] The 10th global corruption barometer (gcb) program - in africa, shows that while many people in africa feel that corruption is on the rise in their country, many also feel confident that, as citizens, they can make a difference in the fight against corruption.

[Formal] The tenth Global Corruptibility Barometer (GCB) program - in Africa - shows that while many people in Africa feel that corruption

Please help to fix this for longer sentences. Thanks in advance!

Hi Prithviraj,

Fantastic work you are doing.

While testing your models, I intended to deploy the model wrapped in a flask app on EC2.

Although the results work on Google Colab, I receive the following error on EC2 -

OSError: Can't load config for 'prithivida/parrot_adequacy_on_BART'. Make sure that:

- 'prithivida/parrot_adequacy_on_BART' is a correct model identifier listed on 'https://huggingface.co/models'

- or 'prithivida/parrot_adequacy_on_BART' is the correct path to a directory containing a config.json file

Can you guide me on how this can be resolved?

Regards, Paritosh

Hi, I'm trying to save and load the tokenizer and model. I use the following to save them:

tokenizer = AutoTokenizer.from_pretrained("prithivida/informal_to_formal_styletransfer")
tokenizer.save_pretrained('./data/style_tokenizer')
model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/informal_to_formal_styletransfer")
model.save_pretrained('./data/style_model')

But when I try to load them, from the local path, I get the following error:

OSError: Can't load config for '../data/style_tokenizer'. Make sure that:

- '../data/style_tokenizer' is a correct model identifier listed on 'https://huggingface.co/models'

- or '../data/style_tokenizer' is the correct path to a directory containing a config.json file

This somehow makes sense since saving the vectorizer, no config.json is being created.

Any idea how can I save/load the tokenizer and model?

Hey, Can you please share the code, where you train models? We have tasks similar to issues you solve but in other domains. It might be very helpful for us. Do you fine-tune only T5 or you make additional changes to T5 fine-tuning? Thanks

it keeps throing the same error(diffrent request id) OSError: There was a specific connection error when trying to load prithivida/informal_to_formal_styletransfer: <class 'requests.exceptions.HTTPError'> (Request ID: K9-6-Ks5uMEai7cOcQ3gC)

OSError: prithivida/parrot_adequacy_on_BART is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'

I'm using the latest version and seeing the following issue. I was wondering if anything has changed on the huggingface models front?

I am using this model to do inferencing on 1 million data point using A100 GPU's with 4 GPU. I am launching a inference.py code using Googles vertex-ai Container.

How can I make inference code to utilise all 4 GPU's ? So that inferencing is super-fast.

Here is the same code I use in inference.py:

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 = [0=Regular model On CPU, 1= Regular model On GPU, 2=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)     

Love the library!

Was hoping to do sentiment transfer but I see that has not yet been integrated. Any pointers towards off the shelf models that can do that?