Instructions to use parsi-ai-nlpclass/PersianTextFormalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use parsi-ai-nlpclass/PersianTextFormalizer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="parsi-ai-nlpclass/PersianTextFormalizer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("parsi-ai-nlpclass/PersianTextFormalizer")
model = AutoModelForSeq2SeqLM.from_pretrained("parsi-ai-nlpclass/PersianTextFormalizer")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use parsi-ai-nlpclass/PersianTextFormalizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "parsi-ai-nlpclass/PersianTextFormalizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "parsi-ai-nlpclass/PersianTextFormalizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/parsi-ai-nlpclass/PersianTextFormalizer

SGLang

How to use parsi-ai-nlpclass/PersianTextFormalizer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "parsi-ai-nlpclass/PersianTextFormalizer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "parsi-ai-nlpclass/PersianTextFormalizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "parsi-ai-nlpclass/PersianTextFormalizer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "parsi-ai-nlpclass/PersianTextFormalizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use parsi-ai-nlpclass/PersianTextFormalizer with Docker Model Runner:
```
docker model run hf.co/parsi-ai-nlpclass/PersianTextFormalizer
```

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YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

PersianTextFormalizer

This model is fine-tuned to generate formal text from informal text based on the input provided. It has been fine-tuned on [Mohavere Dataset] (Takalli vahideh, Kalantari, Fateme, Shamsfard, Mehrnoush, Developing an Informal-Formal Persian Corpus, 2022.) using the pretrained model persian-t5-formality-transfer.

Evaluation Metrics

Metric	Basic Model	Base Persian T5	Our Model
BLEU-1	0.524	0.212	0.636
BLEU-2	0.358	0.137	0.511
BLEU-3	0.254	0.096	0.416
BLEU-4	0.18	0.068	0.337
Bert-Score Precision	0.671	0.537	0.797
Bert-Score Recall	0.712	0.570	0.805
Bert-Score F1 Score	0.690	0.549	0.800
ROUGE-1 F1 Score	0.553	-	0.645
ROUGE-2 F1 Score	0.274	-	0.427
ROUGE-l F1 Score	0.522	-	0.628

Usage


from transformers import (T5ForConditionalGeneration, AutoTokenizer, pipeline)
import torch

model = T5ForConditionalGeneration.from_pretrained('parsi-ai-nlpclass/PersianTextFormalizer')
tokenizer = AutoTokenizer.from_pretrained('parsi-ai-nlpclass/PersianTextFormalizer')

pipe = pipeline(task='text2text-generation', model=model, tokenizer=tokenizer)
def test_model(text):
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  
  model.to(device) 

  inputs = tokenizer.encode("informal: " + text, return_tensors='pt', max_length=128, truncation=True, padding='max_length')
  inputs = inputs.to(device) 

  outputs = model.generate(inputs, max_length=128, num_beams=4)
  print("Output:", tokenizer.decode(outputs[0], skip_special_tokens=True))  

text = "به یکی از دوستام میگم که چرا اینکار رو میکنی چرا به فکرت نباید برسه "
print("Original:", text)
test_model(text)

# output:  .به یکی از دوستانم می گویم که چرا اینکار را می کنی چرا به فکرت نباید برسد

text = "کجا مخفیش کردی؟"
print("Original:", text)
test_model(text)

# output:   کجا او را پنهان کرده ای؟

text = "نمیکشنمون که"
print("Original:", text)
test_model(text)

# output: .ما را که نمی‌کشند.

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Tensor type

F32