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.
Usage
from transformers import (T5ForConditionalGeneration, AutoTokenizer, pipeline)
import torch
model = T5ForConditionalGeneration.from_pretrained('PardisSzah/PersianTextFormalizer')
tokenizer = AutoTokenizer.from_pretrained('PardisSzah/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 = "اسم من پردیسه و خوشحالم که از این مدل خوشتون اومده "
print("Original:", text)
test_model(text)
# output: اسم من پردیس است و خوشحالم که از این مدل خوشتان آمده است
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