mt5-persian-summary

nafisehNik/mt5-persian-summary

mt5-persian-summary is a summarization model from nafisehNik. Use it when you need a shorter version of a longer text. It is set up for transformers. The card lists the license as mit.

This model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on a wide range of Persian news data, including BBC news and pnsummary.

Downloads · 30 days

565

1% of all-time downloads

All-time downloads

109K

Public

Repo size

3.6 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin1.2 GB · 100%

At a glance

Task: Summarization
Library: transformers
License: mit
Model type: mt5
Access: Public
Created: Aug 17, 2022
Updated: Nov 7, 2023
SHA: b575560a

Try a prompt

Datasets

Spaces

Model card

From the Hugging Face model README

mT5 Persian Summary

This model is fine-tuned to generate summaries based on the input provided. It has been fine-tuned on a wide range of Persian news data, including BBC news and pn_summary.

Usage

from transformers import  AutoModelForSeq2SeqLM, MT5Tokenizer

model = AutoModelForSeq2SeqLM.from_pretrained('nafisehNik/mt5-persian-summary')

tokenizer = MT5Tokenizer.from_pretrained("nafisehNik/mt5-persian-summary")


# method for summary generation, using the global model and tokenizer
def generate_summary(model, abstract, num_beams = 2, repetition_penalty = 1.0,
                    length_penalty = 2.0, early_stopping = True, max_output_length = 120):
    source_encoding=tokenizer(abstract, max_length=1000, padding="max_length", truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt")

    generated_ids=model.generate(
        input_ids=source_encoding["input_ids"],
        attention_mask=source_encoding["attention_mask"],
        num_beams=num_beams,
        max_length=max_output_length,
        repetition_penalty=repetition_penalty,
        length_penalty=length_penalty,
        early_stopping=early_stopping,
        use_cache=True
        )

    preds=[tokenizer.decode(gen_id, skip_special_tokens=True, clean_up_tokenization_spaces=True) 
         for gen_id in generated_ids]

    return "".join(preds)

text = "YOUR INPUT TEXT"
result = generate_summary(model=model, abstract=text, num_beams=2, max_output_length=120)

Citation

If you find this model useful, make a link to the huggingface model.

Task: Summarization
Library: transformers
Type: mt5
License: mit
Languages: fa
Created: Aug 17, 2022
Updated: Nov 7, 2023