Instructions to use Aminrhmni/PersianAutomaticPunctuation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aminrhmni/PersianAutomaticPunctuation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Aminrhmni/PersianAutomaticPunctuation") model = AutoModelForSeq2SeqLM.from_pretrained("Aminrhmni/PersianAutomaticPunctuation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Adding `safetensors` variant of this model (#3)
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Hey there ***This model is intended for non-commercial use only. If you wish to use it for commercial purposes, please make sure to refer to this LinkedIn address. "Viravirast" is an editor based on transformer algorithms. By visiting the Viravirast.com, you can use a Persian semantic and structural text editor. usage from transformers import ( T5Tokenizer, MT5ForConditionalGeneration, Text2TextGenerationPipeline, ) path = "" pipe = Text2TextGenerationPipeline( model=MT5ForConditionalGeneration.from_pretrained(path), tokenizer=T5Tokenizer.from_pretrained(path), ) sentence = "ویراویراست یک نرم افزار ویرایش متن ساختاری و معنایی زبان فارسی است چیزی شبیه به گرامرلی در زبان انگلیسی" #res = pipe(sentence, max_length=100, num_beams=4) res = pipe(sentence, max_length=100) print(res[0]['generated_text']) n_epochs = 4 train_batch_size = 8 eval_batch_size = 4 lr = 5e-4 Training loss = 0.005501 Validation loss= 0.052046054affb -
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